--- base_model: bobox/DeBERTa-small-ST-v1-test datasets: - jinaai/negation-dataset-v2 - tals/vitaminc - allenai/scitail - allenai/sciq - allenai/qasc - sentence-transformers/msmarco-msmarco-distilbert-base-v3 - sentence-transformers/natural-questions - sentence-transformers/trivia-qa - sentence-transformers/gooaq - google-research-datasets/paws language: - en library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:305010 - loss:CachedGISTEmbedLoss widget: - source_sentence: how long should a prelude be before a funeral sentences: - 'Organic 101: What the USDA Organic Label Means. This is the third installment of the Organic 101 series that explores different aspects of the USDA organic regulations. Organic certification requires that farmers and handlers document their processes and get inspected every year.' - 'The Quadrille. The Quadrille is a historic dance performed by four couples in a rectangular formation, and a precursor to traditional square dancing as well as a style of music. The Quadrille or Quadrille de Contre Danse was originally a card game for four people but the name was given to this dance about 1740.The dance probably derived from the Cotillions of the time. The Quadrille was a very lively dance, unlike the Minuet.Wikipedia states thus: The term quadrille came to exist in the 17th century, within military parades, in which four horsemen and their mounts performed special square-shaped formations or figures.he Quadrille or Quadrille de Contre Danse was originally a card game for four people but the name was given to this dance about 1740. The dance probably derived from the Cotillions of the time. The Quadrille was a very lively dance, unlike the Minuet.' - 1 Arrive early. 2 You should always endeavor to arrive at the church or funeral home at between 15 to 20 minutes before the service is scheduled to begin. 3 Take your seat quietly, and reverently await the arrival of the family. - source_sentence: More than 169 countries had reported over 212,000 COVID-19 cases before March 19 , 2020 . sentences: - As of 23 March , more than 341,000 cases of COVID-19 have been reported in 192 countries and territories , resulting in more than 14,700 deaths and 99,000 recoveries . - As of 21 March , more than 278,000 cases of COVID-19 have been reported in over 186 countries and territories , resulting in more than 11,500 deaths and 92,000 recoveries. virus seems to mostly spread between people via respiratory droplets . - As of 18 March 2020 , more than 212,000 cases of COVID-19 have been reported in at least 170 countries and territories , with major outbreaks in China , Iran and the European Union . - source_sentence: 'The book is about a fictional obsessed fan hunting down King, the author of Misery, The Shining and Carrie. But Patterson said he had learned in the run-up to the planned November publication that fans had "disrupted" King''s home in real life. King has had nothing to do with the novel, Patterson has stressed. Before deciding to scrap the book, he wrote on his website: "I''m a Stephen King fan, but Stephen King did not participate in the making of this novel, nor is he affiliated with it in any way. I hope he likes it." However, in a statement released by his publisher on Thursday, Patterson - who co-wrote the book with Derek Nikitas - said: "My book is a positive portrayal of a fictional character, and, spoiler alert, the main character is not actually murdered. "Nevertheless, I do not want to cause Stephen King or his family any discomfort. Out of respect for them, I have decided not to publish The Murder of Stephen King." King declined to comment on the book when asked about it last week by the Associated Press. Patterson, ranked as the world''s highest-earning author for the last three years, told the news agency the pair do not know each other. In 2009, King called Patterson a successful yet "terrible" writer. Crime writer Patterson described that remark as "hyperbole" when speaking to AP. Patterson is releasing the novel Taking the Titanic instead of the planned King book. Follow us on Twitter @BBCNewsEnts, on Instagram, or if you have a story suggestion email entertainment.news@bbc.co.uk.' sentences: - Author James Patterson has scrapped the publication of a new novel titled The Murder of Stephen King because he does not want to cause "discomfort" to King. - Portsmouth manager Paul Cook says he will "move heaven and earth" to get the club promoted from League Two. - Swansea City have reached a settlement with former manager Michael Laudrup over his sacking. - source_sentence: Electrical energy can be converted into kinetic energy and heat energy by an electric motor. sentences: - Solution is the term for a homogeneous mixture of two or more substances. - Solution is the term for a homogeneous mixture of two or more substances. - Electric motors transform electrical energy into kinetic energy. - source_sentence: where did the ice storm of 1998 happen sentences: - January 1998 North American ice storm The North American Ice Storm of 1998 (also known as Great Ice Storm of 1998) was a massive combination of five smaller successive ice storms in January 1998 that struck a relatively narrow swath of land from eastern Ontario to southern Quebec, New Brunswick and Nova Scotia in Canada, and bordering areas from northern New York to central Maine in the United States. It caused massive damage to trees and electrical infrastructure all over the area, leading to widespread long-term power outages. Millions were left in the dark for periods varying from days to several weeks, and in some instances, months. It led to 35 fatalities, a shutdown of activities in large cities like Montreal and Ottawa, and an unprecedented effort in reconstruction of the power grid. The ice storm led to the largest deployment of Canadian military personnel since the Korean War, with over 16,000 Canadian Forces personnel deployed, 12,000 in Quebec and 4,000 in Ontario at the height of the crisis.[1][2]:16 - Tom and Jerry Tom and Jerry is an American animated series of short films created in 1940, by William Hanna and Joseph Barbera. It centers on a rivalry between its two title characters, Tom and Jerry, and many recurring characters, based around slapstick comedy. - Nimbostratus cloud Nimbostratus is a stratiform genus formerly classified as "Family C" low-level, but now considered by the World Meteorological Organization (WMO) to be a middle- or multi-level stratus type.[1]. Although it is usually a low-based cloud, it actually forms most commonly in the middle level of the troposphere and then spreads vertically into the low and high levels. This change in classification would once have made it a "Family D" cloud, but this style of nomenclature was discontinued by the WMO in 1956. Nimbostratus usually produces precipitation over a wide area. Nimbo- is from the Latin word nimbus, which denotes precipitation. It has a diffuse cloud base generally found anywhere from near surface in the low levels to about 3,000 m (9,800 ft) in the middle level of the troposphere. Although usually dark at its base, it often appears illuminated from within to a surface observer.[2] Nimbostratus usually has a thickness of about 2000 m. Though found worldwide, nimbostratus occurs more commonly in the middle latitudes.[3] It is coded CM2 on the SYNOP report. model-index: - name: SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.8794649611143293 name: Pearson Cosine - type: spearman_cosine value: 0.9036383241029473 name: Spearman Cosine - type: pearson_manhattan value: 0.9013878798004252 name: Pearson Manhattan - type: spearman_manhattan value: 0.8981009820234211 name: Spearman Manhattan - type: pearson_euclidean value: 0.9018920659152481 name: Pearson Euclidean - type: spearman_euclidean value: 0.89878504980505 name: Spearman Euclidean - type: pearson_dot value: 0.8699021205629681 name: Pearson Dot - type: spearman_dot value: 0.8710281246782958 name: Spearman Dot - type: pearson_max value: 0.9018920659152481 name: Pearson Max - type: spearman_max value: 0.9036383241029473 name: Spearman Max - task: type: binary-classification name: Binary Classification dataset: name: VitaminC type: VitaminC metrics: - type: cosine_accuracy value: 0.556640625 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.8423352241516113 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6657824933687002 name: Cosine F1 - type: cosine_f1_threshold value: 0.28707611560821533 name: Cosine F1 Threshold - type: cosine_precision value: 0.4990059642147117 name: Cosine Precision - type: cosine_recall value: 1.0 name: Cosine Recall - type: cosine_ap value: 0.5514274268482979 name: Cosine Ap - type: dot_accuracy value: 0.5546875 name: Dot Accuracy - type: dot_accuracy_threshold value: 307.8145751953125 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6693440428380187 name: Dot F1 - type: dot_f1_threshold value: 136.3172607421875 name: Dot F1 Threshold - type: dot_precision value: 0.5040322580645161 name: Dot Precision - type: dot_recall value: 0.9960159362549801 name: Dot Recall - type: dot_ap value: 0.5319992367485454 name: Dot Ap - type: manhattan_accuracy value: 0.5546875 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 224.1436767578125 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6666666666666666 name: Manhattan F1 - type: manhattan_f1_threshold value: 489.0301818847656 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.5 name: Manhattan Precision - type: manhattan_recall value: 1.0 name: Manhattan Recall - type: manhattan_ap value: 0.5514724989625399 name: Manhattan Ap - type: euclidean_accuracy value: 0.556640625 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 14.50575065612793 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.6666666666666666 name: Euclidean F1 - type: euclidean_f1_threshold value: 23.260427474975586 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.5 name: Euclidean Precision - type: euclidean_recall value: 1.0 name: Euclidean Recall - type: euclidean_ap value: 0.5539696276195623 name: Euclidean Ap - type: max_accuracy value: 0.556640625 name: Max Accuracy - type: max_accuracy_threshold value: 307.8145751953125 name: Max Accuracy Threshold - type: max_f1 value: 0.6693440428380187 name: Max F1 - type: max_f1_threshold value: 489.0301818847656 name: Max F1 Threshold - type: max_precision value: 0.5040322580645161 name: Max Precision - type: max_recall value: 1.0 name: Max Recall - type: max_ap value: 0.5539696276195623 name: Max Ap --- # SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [bobox/DeBERTa-small-ST-v1-test](https://huggingface.co/bobox/DeBERTa-small-ST-v1-test) on the [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2), [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), xsum-pairs, [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), openbookqa_pairs, [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) and [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [bobox/DeBERTa-small-ST-v1-test](https://huggingface.co/bobox/DeBERTa-small-ST-v1-test) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Datasets:** - [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2) - [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) - [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) - [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) - xsum-pairs - [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) - [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) - openbookqa_pairs - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) - [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) - **Language:** en ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("bobox/DeBERTa-small-ST-v1-test-step2") # Run inference sentences = [ 'where did the ice storm of 1998 happen', 'January 1998 North American ice storm The North American Ice Storm of 1998 (also known as Great Ice Storm of 1998) was a massive combination of five smaller successive ice storms in January 1998 that struck a relatively narrow swath of land from eastern Ontario to southern Quebec, New Brunswick and Nova Scotia in Canada, and bordering areas from northern New York to central Maine in the United States. It caused massive damage to trees and electrical infrastructure all over the area, leading to widespread long-term power outages. Millions were left in the dark for periods varying from days to several weeks, and in some instances, months. It led to 35 fatalities, a shutdown of activities in large cities like Montreal and Ottawa, and an unprecedented effort in reconstruction of the power grid. The ice storm led to the largest deployment of Canadian military personnel since the Korean War, with over 16,000 Canadian Forces personnel deployed, 12,000 in Quebec and 4,000 in Ontario at the height of the crisis.[1][2]:16', 'Nimbostratus cloud Nimbostratus is a stratiform genus formerly classified as "Family C" low-level, but now considered by the World Meteorological Organization (WMO) to be a middle- or multi-level stratus type.[1]. Although it is usually a low-based cloud, it actually forms most commonly in the middle level of the troposphere and then spreads vertically into the low and high levels. This change in classification would once have made it a "Family D" cloud, but this style of nomenclature was discontinued by the WMO in 1956. Nimbostratus usually produces precipitation over a wide area. Nimbo- is from the Latin word nimbus, which denotes precipitation. It has a diffuse cloud base generally found anywhere from near surface in the low levels to about 3,000\xa0m (9,800\xa0ft) in the middle level of the troposphere. Although usually dark at its base, it often appears illuminated from within to a surface observer.[2] Nimbostratus usually has a thickness of about 2000 m. Though found worldwide, nimbostratus occurs more commonly in the middle latitudes.[3] It is coded CM2 on the SYNOP report.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8795 | | **spearman_cosine** | **0.9036** | | pearson_manhattan | 0.9014 | | spearman_manhattan | 0.8981 | | pearson_euclidean | 0.9019 | | spearman_euclidean | 0.8988 | | pearson_dot | 0.8699 | | spearman_dot | 0.871 | | pearson_max | 0.9019 | | spearman_max | 0.9036 | #### Binary Classification * Dataset: `VitaminC` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:----------| | cosine_accuracy | 0.5566 | | cosine_accuracy_threshold | 0.8423 | | cosine_f1 | 0.6658 | | cosine_f1_threshold | 0.2871 | | cosine_precision | 0.499 | | cosine_recall | 1.0 | | cosine_ap | 0.5514 | | dot_accuracy | 0.5547 | | dot_accuracy_threshold | 307.8146 | | dot_f1 | 0.6693 | | dot_f1_threshold | 136.3173 | | dot_precision | 0.504 | | dot_recall | 0.996 | | dot_ap | 0.532 | | manhattan_accuracy | 0.5547 | | manhattan_accuracy_threshold | 224.1437 | | manhattan_f1 | 0.6667 | | manhattan_f1_threshold | 489.0302 | | manhattan_precision | 0.5 | | manhattan_recall | 1.0 | | manhattan_ap | 0.5515 | | euclidean_accuracy | 0.5566 | | euclidean_accuracy_threshold | 14.5058 | | euclidean_f1 | 0.6667 | | euclidean_f1_threshold | 23.2604 | | euclidean_precision | 0.5 | | euclidean_recall | 1.0 | | euclidean_ap | 0.554 | | max_accuracy | 0.5566 | | max_accuracy_threshold | 307.8146 | | max_f1 | 0.6693 | | max_f1_threshold | 489.0302 | | max_precision | 0.504 | | max_recall | 1.0 | | **max_ap** | **0.554** | ## Training Details ### Training Datasets #### negation-triplets * Dataset: [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2) * Size: 39,000 training samples * Columns: anchor, entailment, and negative * Approximate statistics based on the first 1000 samples: | | anchor | entailment | negative | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | entailment | negative | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:---------------------------------------------------------------------| | A young man in white is midair on his bicycle performing a trick. | A young man in white is midair | A young man in white is not midair | | A bicycle has a red umbrella attached to it. | A parked bicycle with a red umbrella attached to it. | A parked bicycle without a red umbrella attached to it. | | Tanzania started rationing electricity after a technical problem shut down machinery at the Songas gas-fired power plant in Dar es Salaam, the state-run power utility said. | Tanzania rations electricity after technical problem at plant | Tanzania boosts electricity after upgrade at plant | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 36,000 training samples * Columns: claim and evidence * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | claim | evidence | |:---------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Candice Crawford participated in 2 beauty pageants . | He has a younger sister , former Miss Missouri USA winner and [ [ Miss USA ] contestant Candice Crawford . | | Rio Ferdinand plays for Queens Park Rangers on a free transfer . | A centre-back , he is currently plays for Queens Park Rangers on a Free Transfer after leaving Manchester United following twelve years at the club . | | Matt Damon is credited in Deadpool 2 as Dickie Grrenleaf , in reference to Jude Law 's character from The Talented Mr. Ripley . | This was inspired by a real manifesto written by Reese , which the writers wanted to be discussed in the film by a certain `` calibre '' of actor : the characters are portrayed by Alan Tudyk and a disguised Matt Damon , with the latter credited as `` Dickie Greenleaf '' ( a reference to Jude Law 's character from the 1999 film The Talented Mr. Ripley in which Damon stars ) . | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 14,237 training samples * Columns: sentence2 and sentence1 * Approximate statistics based on the first 1000 samples: | | sentence2 | sentence1 | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence2 | sentence1 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| | How ice cores are important to the study of geologic history is best described as they contain evidence showing changes in the atmospheric composition over time. | Which best describes how ice cores are important to the study of geologic history? | | The lens of the eye is a(n) convex shape. | What shape is the lens of the eye? | | Most ferns produce the same type of spores and are therefore called homosporous. | Most ferns produce the same type of spores and are therefore called what? | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 8,600 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------| | The rate of decay is conveniently expressed in terms of an isotope's half-life, or the time it takes for one-half of a particular radioactive isotope in a sample to decay. | The term half-life decribes the amount of time required for half of the original material to decay in an isotope. | | This rock, called magma, furnishes the heat for the park's geysers and hot springs. | The water in some springs are hot because they're heated by hot magma. | | Carbon, with four valence electrons, forms covalent bonds to four neighboring carbon atoms arranged toward the corners of a tetrahedron, as shown in the figure below. | Four valence electrons can be found in a carbon atom. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### xsum-pairs * Dataset: xsum-pairs * Size: 36,000 training samples * Columns: document and summary * Approximate statistics based on the first 1000 samples: | | document | summary | |:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | document | summary | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------| | Christopher Paul O'Kane, aged 42, from Woodland Avenue, was due to stand trial on Wednesday on 17 Provisional IRA related terrorist offences, including the attempted murder of a police officer more than 20 years ago.
However, O'Kane replied "guilty'' to five of the five terror charges when they were put to him in court.
These were: assisting an offender in relation to the murder of Constable Michael Ferguson in January 1994, and firing a mortar bomb at a police vehicle in Fanad Drive in October 1993. Planting a bomb at the railway line at Ebrington Barracks in December 1993; placing a bomb at the home of a senior police officer at Prehen in 1994, and planting a bomb at Forge George army base in Derry.
Prosecution lawyer Robin Steer asked that the remaining 12 charges "be left on the books" and not to be proceeded without the leave of the Court or the Court of Appeal.
O'Kane will be sentenced on December 16. | A Londonderry man has pleaded guilty at Belfast Crown Court to five terrorist offences relating to bomb attacks against security force members. | | Chase, 31, has made five appearances for the Vikings since the initial temporary move, having been told he was not part of Castleford's 2018 plans.
In addition, Widnes have signed hooker Danny Walker to a new four-year contract after his first-grade breakthrough this season.
Head coach Denis Betts said: "The deals represent a real statement of intent."
New Zealand-born Chase was the 2011 Man of Steel, has played for the Tigers, Salford, Leigh and now Widnes, as well as representing England at senior level.
In contrast, Walker, 18, is at the opposite end of his career, with just six professional appearances to his name.
"Rangi has made a big impact since his arrival, contributing well both on and off the field," Betts added.
"He has exceptional abilities and, as an international standard half-back, adds real quality to the team.
"Danny is one of the most promising young players in the Super League. His commitment to a four-year contract shows his belief in what we are building at the Vikings." | Widnes have signed half-back Rangi Chase from Castleford on a permanent deal after a successful loan spell. | | The characters include Nebula, a blue skinned alien played by Scottish actress Karen Gillan.
There was a backlash on social media to merchandise released for the first Guardians film in 2014.
Gamora, who is played by Zoe Saldana, did not appear on a t-shirt while her fellow, male leads did.
The row saw the hashtag #WheresGamora appear on Twitter.
Fans of the film also complained that other merchandise of Gamora, and Gillan's Nebula, who also appeared in the first movie, was harder to find than that featuring male characters.
In a post on Facebook on Sunday, the films' director James Gunn confirmed that there was a commitment to raising the profile of the female characters.
He wrote: "Guardians of the Galaxy Vol. 2 will have Mantis, Nebula, Gamora and Elizabeth Debicki's character as part of the fray, and we're committed to making sure they're included in more toys and merch than the last go round."
Marvel and Disney are involved in making the movies and commissioning the merchandising.
Guardians, which topped the US box office in 2014 with ticket sales of $332.8m (£203.9m), also stars Chris Pratt and Dave Bautista and the voices of Vin Diesel and Bradley Cooper.
Earlier this year, Gunn revealed that Gillan, from Inverness, has a bigger role in the new movie, which is to be released next year. | Female stars of the new Guardians of the Galaxy movie will feature more prominently in its merchandising, the film's makers have said. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 11,095 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What branch of science is defined as the study of matter? | 1.8 End-of-Chapter Material Chapter Summary To ensure that you understand the material in this chapter, you should review the meanings of the bold terms in the following summary and ask yourself how they relate to the topics in the chapter. Chemistry is the study of matter, which is anything that has mass and takes up space. Chemistry is one branch of science, which is the study of the natural universe. Like all branches of science, chemistry relies on the scientific method, which is a process of Saylor URL: http://www. saylor. org/books. | | The net effect of aldosterone is to conserve and increase water levels in the plasma by reducing the excretion of what element, and thus water? | Aldosterone Recall that aldosterone increases the excretion of potassium and the reabsorption of sodium in the distal tubule. Aldosterone is released if blood levels of potassium increase, if blood levels of sodium severely decrease, or if blood pressure decreases. Its net effect is to conserve and increase water levels in the plasma by reducing the excretion of sodium, and thus water, from the kidneys. In a negative feedback loop, increased osmolality of the ECF (which follows aldosterone-stimulated sodium absorption) inhibits the release of the hormone (Figure 26.13). | | What do all chemical sections need to get started? | The bonds between the atoms need to be rearranged. That is the definition of a chemical reaction. And all chemical sections need energy to get started. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 7,727 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | vertebrates have a complete digestive system and a what? | Chordates have a complete digestive system and a closed circulatory system.. Vertebrates are members of of a larger group, the chordates .. Vertebrates have a complete digestive system and a closed circulatory system | | What is plasma? | plasma is formed by electrons separating from atoms in stars. Stars shine because they are hot .. plasma is hot | | Tungsten filaments sealed in a glass bulb are used for what in the dark? | a light bulb is used for seeing in the dark. Incandescent light bulbs use a tungsten filament sealed inside a glass bulb.. Tungsten filament sealed in a glass bulb is used for seeing in the dark. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 4,522 training samples * Columns: question and fact * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | fact | |:-----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | What is animal competition? | if two animals eat the same prey then those animals compete for that pey | | If you wanted to make a metal bed frame, where would you start? | alloys are made of two or more metals | | Places lacking warmth have few what | cold environments contain few organisms | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### msmarco_pairs * Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 33,000 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what is the salary of hvac service mana | According to the Bureau of Labor Statistics, the median salary for HVAC technician in 2015 is $45,110 per year and approximately $21,69 per hour. Salary variations according to various factors Depending on technician’s ... | | caffeine effects on nervous system | Caffeine is a central nervous system stimulant that reduces fatigue and drowsiness. At normal doses, caffeine has variable effects on learning and memory, but it generally improves reaction time, wakefulness, concentration, and motor coordination. | | cost to flush transmission | 1 Flushing a transmission with a pressurized machine can cost $125-$300 or more, and may include pushing a special cleaning compound through the system. 2 The power flush process typically replaces all of the transmission fluid, and can require 12-22 or more quarts. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### nq_pairs * Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 33,000 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | is watcher in the woods a disney movie | The Watcher in the Woods Filmed at Pinewood Studios and the surrounding areas in Buckinghamshire, England, The Watcher in the Woods was one of several live-action films produced by Walt Disney Productions in the 1980s, when the studio was targeting young adult audiences. The film suffered from various production problems and was pulled from theatres after its initial release in 1980. It was re-released in 1981 after being re-edited and a revised ending added. | | how much money did the ice bucket challenge raise for als | Ice Bucket Challenge Within weeks of the challenge going viral, The New York Times reported that the ALS Association had received $41.8 million in donations from more than 739,000 new donors from July 29 until August 21, more than double the $19.4 million the association received during the year that ended January 31, 2013.[89] On August 29, the ALS Association announced that their total donations since July 29 had exceeded $100 million.[90] The ALS Association is just one of several ALS-related charities that have benefited from the challenge: | | last episode of one foot in the grave | Things Aren't Simple Any More "Things Aren't Simple Any More" is the final episode of the British television sitcom One Foot in the Grave. It was written by David Renwick and stars Richard Wilson as Victor Meldrew, Annette Crosbie as his wife Margaret, and features guest appearances by Hannah Gordon and Paul Merton. The episode depicts the death of the series' protagonist, Victor Meldrew, in a hit-and-run road accident, and his wife's efforts to deal with the driver who killed him. Renwick had been struggling to conceive and write new stories for the series and decided to kill off the character. The episode was filmed at Shawford, Hampshire, and at BBC Television Centre in London.[3] | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### trivia_pairs * Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 30,000 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Which Channel Four game show was remade in the USA under the title Junkyard Wars? | Junkyard Wars Needs A Few Good Contestants - Slashdot Slashdot Follow Slashdot stories on Twitter   Check out the new SourceForge HTML5 internet speed test! No Flash necessary and runs on all devices. × 1380727 story on Saturday January 27, 2001 @09:07AM from the gentlemen-start-your-torches dept. Andy B writes: "At long last, we have got an address for aspiring contestants to send their Junkyard Wars applications to (Slashdot slightly jumped the gun last autumn). Hurry to get you applications in, as the deadline for applications is Fri 16th Feb." ← You may like to read: → Yes, but its license states that it must act as a public service broadcaster: This [itc.org.uk] is taken from the ITC [itc.org.uk] website: The main points in the new licence are: redefinition of the remit in relation to all channels, not just ITV, and further commitment to innovation and experiment; a revised and strengthened statement on education; a commitment to provide at least three hours on average per week of multicultural programmes, and also to schedule at least some of these in peak time; a major commitment to the UK film industry, giving some preference to innovative and risky subjects and treatments; a new commitment to programmes for and about people with disabilities; increased requirement of 60 per cent of programmes specially commissioned for the Channel by 1999; a new commitment for production outside the London region, including a minimum requirement of 30 per cent by 2002; new maxima for repeats; a new commitment and new minimum requirement for spending on training; a new requirement for diversity in the peak-time schedule, including news, current affairs, educational, religious and multicultural programmes; revised commitments to subtitling and other provisions for those with hearing and sight impairments. I think it's not fully privatised either - doesn't the government still own a proportion of it and fund it a bit too? by MrP- ( 45616 ) writes: if you watched the last episode of junkyard wars (the rocket one), cathy said you can go to the site and submit an idea for them to build, i forget what site she said, probably either the junkyard wars site, or tlc.com, either way you should submit that, that would be cool to see... but only thing is theyd probably want to fight during the show at the end, unless they go tape the fight at battlebots, show the match during junkyard wars, then battle bots later, like they did with jay lenos chinkilla, they aired the match early on the tonight show then again on battle bots... but i dont know since battle bots is comedy centrals, maybe theyd have to do robot wars, but they dont air robot wars enough in america :( by Uberminky ( 122220 ) writes: The new Junkyard Wars episodes stink. I mean yeah, it's still a cool show, but it's just not what it used to be. I miss the silly Brits, they cracked me up. But yunno... maybe it's just me, but I swear the type of things they do on the show have changed since they got the new host and stuff. I mean.. one of the last episodes the Scrapheap Challenge did was drag racing. And what's the first Junkyard Wars thing? Drag racing. They also did all-terrain vehicles, which Scrapheap Challenge did.. and.. just tons of them. They're all the same sorts of things. Maybe it's just my perception, and the fact that I miss the old show, but it really seems like they've dumbed it down to suit us redneck Americans or something. I dunno... I just wish they'd give me my Scrapheap Challenge... Looking forward to next week's episode.... by ScuzzMonkey ( 208981 ) writes: I haven't seen all that many episodes, but of those I have seen, this seems to be the theme. The team that comes up with the more brilliant, elegant design has it crap out on them almost immediately, because, after all, it's made out of junk. The crowd that bangs together some brute-force job powers through and wins it. Apparently, it doesn't pay to get too creative. Makes it more fun to watch, though. by Uberminky ( 122220 ) writes: that would be so lame. On the one hand, we have people turning scrap met | | Which 2010 film stars Mila Kunis as Lily and Natalie Portman as Nina Sayers? | Black Swan (2010) - IMDb IMDb There was an error trying to load your rating for this title. Some parts of this page won't work property. Please reload or try later. X Beta I'm Watching This! Keep track of everything you watch; tell your friends. Error From $2.99 (SD) on Amazon Video ON DISC A committed dancer wins the lead role in a production of Tchaikovsky's "Swan Lake" only to find herself struggling to maintain her sanity. Director: a list of 34 titles created 25 Dec 2012 a list of 27 images created 09 Mar 2013 a list of 43 titles created 10 Nov 2013 a list of 26 titles created 05 Nov 2014 a list of 48 titles created 14 Mar 2015 Search for " Black Swan " on Amazon.com Connect with IMDb Want to share IMDb's rating on your own site? Use the HTML below. You must be a registered user to use the IMDb rating plugin. Won 1 Oscar. Another 90 wins & 245 nominations. See more awards  » Videos Harvard student Mark Zuckerberg creates the social networking site that would become known as Facebook, but is later sued by two brothers who claimed he stole their idea, and the co-founder who was later squeezed out of the business. Director: David Fincher Two astronauts work together to survive after an accident which leaves them alone in space. Director: Alfonso Cuarón After a stint in a mental institution, former teacher Pat Solitano moves back in with his parents and tries to reconcile with his ex-wife. Things get more challenging when Pat meets Tiffany, a mysterious girl with problems of her own. Director: David O. Russell A Mumbai teen reflects on his upbringing in the slums when he is accused of cheating on the Indian Version of "Who Wants to be a Millionaire?" Directors: Danny Boyle, Loveleen Tandan Stars: Dev Patel, Freida Pinto, Saurabh Shukla A young man who survives a disaster at sea is hurtled into an epic journey of adventure and discovery. While cast away, he forms an unexpected connection with another survivor: a fearsome Bengal tiger. Director: Ang Lee The story of King George VI of the United Kingdom of Great Britain and Northern Ireland, his impromptu ascension to the throne and the speech therapist who helped the unsure monarch become worthy of it. Director: Tom Hooper In 1985 Dallas, electrician and hustler Ron Woodroof works around the system to help AIDS patients get the medication they need after he is diagnosed with the disease. Director: Jean-Marc Vallée A paraplegic marine dispatched to the moon Pandora on a unique mission becomes torn between following his orders and protecting the world he feels is his home. Director: James Cameron A lonely writer develops an unlikely relationship with an operating system designed to meet his every need. Director: Spike Jonze Tells the story of Benjamin Button, a man who starts aging backwards with bizarre consequences. Director: David Fincher A family determined to get their young daughter into the finals of a beauty pageant take a cross-country trip in their VW bus. Directors: Jonathan Dayton, Valerie Faris Stars: Steve Carell, Toni Collette, Greg Kinnear A seventeen-year-old aristocrat falls in love with a kind but poor artist aboard the luxurious, ill-fated R.M.S. Titanic. Director: James Cameron Edit Storyline Nina (Portman) is a ballerina in a New York City ballet company whose life, like all those in her profession, is completely consumed with dance. She lives with her obsessive former ballerina mother Erica (Hershey) who exerts a suffocating control over her. When artistic director Thomas Leroy (Cassel) decides to replace prima ballerina Beth MacIntyre (Ryder) for the opening production of their new season, Swan Lake, Nina is his first choice. But Nina has competition: a new dancer, Lily (Kunis), who impresses Leroy as well. Swan Lake requires a dancer who can play both the White Swan with innocence and grace, and the Black Swan, who represents guile and sensuality. Nina fits the White Swan role perfectly but Lily is the personification of the Black Swan. As the two young dancers expand their rivalry into a twisted friendship, Nina begins to | | In computing, what is the device which is plugged into a computer which serves as an adapter or to enable the use of certain software? | What Is A Dongle? - Business Insider print 3 Dongles plugged into Google's Chromebook Pixel. Kevin Smith/Business Insider This week two people in the tech industry lost their jobs because of jokes gone awry at a conference. A couple of male developers were joking around about "big" dongles. When a female developer evangelist heard their jokes she tweeted it out with their photo, complaining that they were being rude. One of those developers lost his job, and then eventually, so did the evangelist. In the developers' defense, the word "dongle" is funny. It's nearly impossible to say without giggling or making childish jokes. Some people in our office had heard the word before, but didn't know what a dongle is. Merriam-Webster  defines a dongle as a small device that plugs into a computer and serves as an adapter or a security measure to enable the use of certain software. Kevin Smith/Business Insider The term, dongle, was rumored to have originated from a 1992 advertisement for Rainbow Technologies. The ad claimed the word dongle was derived from the name "Don Gall." Though untrue, this has given rise to an urban myth, we learned from Wikipedia . According to the University of Pennsylvania's language log , the earliest citation of the word dongle began appearing in 1982: 1982   MicroComputer Printout  Jan. 19/2 The word ‘dongle’ has been appearing in many articles with reference to security systems for computer software [refers to alleged coinage in 1980]. But as the term 'dongle' became more widespread its meaning changed from strictly a scientific term to mainstream. UPenn clarifies: The current meaning for dongle seems to be something like "a self-contained device that plugs into a  port on a computer that is normally used for connections to a separate external device". Thus in addition to the original serial-port dongles, and the USB dongles that Suzanne (and Stephen Fry) wrote about, there are also  "firewire dongles" , and presumably there could be dongles for any other sort of port as well.  Simply put, dongles are computer peripherals that plug into your computer like a USB flash drive or a cord connecting a computer with a printer for example.  Dongles are also huge in the video game world because they allow consoles to have added features like increased audio quality. An Xbox 360 Audio Dongle Wikimedia Commons Before USB was the standard in attaching PC peripherals, there were tons of different dongles that came in various shapes and sizes. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 30,000 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what is the difference between back off and back down? | You back off by retreating from a confrontation of some kind, but here the other person is not being antagonistic. You back down by giving way when you realise you're losing an argument or fight, but that doesn't apply here either. | | how many days after lh surge should i have intercourse? | The three days immediately after a positive test represent the best time to have intercourse to increase the probability of getting pregnant. Ovulation generally occurs a day or two after the LH surge. | | what episode does jane and rafael do it? | Rodriguez's directorial debut was the tenth episode of the season, "Chapter Seventy-Four", which aired on February 9, 2018. The episode was specially selected by showrunner Jennie Snyder-Urman as it featured the characters of Jane (Gina Rodriguez) and Rafael (Justin Baldoni) having sex for the first time. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### paws-pos * Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09) * Size: 21,829 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------| | Mouhoun is one of the 45 provinces of Boucle du Mouhoun Region and is in Burkina Faso . The capital of Mouhoun is Dédougou . | Mouhoun is one of 45 provinces in the Boucle du Mouhoun region and is located in Burkina Faso , the capital of Mouhoun is Dédougou . | | Besides Quintin , they had five children : Juan , Phillip , Willie , Patrick and Lucy . | They had five children besides Quintin : Lucy , Phillip , Juan , Patrick and Willie . | | According to the U.S. Census Bureau , the county is a total area that has land and ( 0.2 % ) of water . | According to the US Census Bureau , the county has a total area of which is land and ( 0.2 % ) of water . | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` ### Evaluation Datasets #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 108 evaluation samples * Columns: claim and evidence * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | claim | evidence | |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Dragon Con had over 5000 guests . | Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell . | | COVID-19 has reached more than 185 countries . | As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths . | | In March , Italy had 3.6x times more cases of coronavirus than China . | As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China . | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### negation-triplets * Dataset: [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2) * Size: 64 evaluation samples * Columns: anchor, entailment, and negative * Approximate statistics based on the first 1000 samples: | | anchor | entailment | negative | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | entailment | negative | |:-----------------------------------------------------------------|:-------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | Boy rides skateboard and does trick over stairs. | Man in air on skateboard at night by light of street lamps. | Man on the ground on skateboard during the day by light of street lamps. | | A small child climbs atop a large motorcycle | A young boy riding a motorcycle next to a silver car. | An adult riding a motorcycle next to a silver car. | | A group of people skiing down a snow covered slope. | Six people in snow field with ski equipment. | Six people in a desert with no ski equipment. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 54 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------| | humans normally have 23 pairs of chromosomes. | Humans typically have 23 pairs pairs of chromosomes. | | A solution is a homogenous mixture of two or more substances that exist in a single phase. | Solution is the term for a homogeneous mixture of two or more substances. | | Upwelling The physical process in near-shore ocean systems of rising of nutrients and colder bottom waters to the surface because of constant wind patterns along the shoreline. | Upwelling is the term for when deep ocean water rises to the surface. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### xsum-pairs * Dataset: xsum-pairs * Size: 128 evaluation samples * Columns: document and summary * Approximate statistics based on the first 1000 samples: | | document | summary | |:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | document | summary | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------| | The five were arrested in Milford Haven and are in police custody.
It follows a multi-agency operation between Dyfed-Powys Police, the Gangmasters and Labour Abuse Authority and Pembrokeshire council.
Det Ch Insp Ross Evans said: "The victims are our priority and those affected are being fully supported."
As part of anti-slavery awareness week, the force said it was raising awareness, identifying offences and supporting victims.
He added: "In reality modern slavery can happen anywhere and there is no typical victim of slavery." | Five people in Pembrokeshire have been arrested on suspicion of gangmaster offences following an investigation into "modern slavery". | | DNA tests showed the bones belonged to the men, who went missing on the mountain in August 1970, police say.
The remains were discovered at an altitude of about 2,800m (9,200ft) in the Alps last September.
They are the latest to be found on the 4,478-metre (14,692-foot) Matterhorn as ice melts.
The Japanese consulate in Geneva identified the climbers as Michio Oikawa and Masayuki Kobayashi, AFP news agency reports. They were 22 and 21 respectively when they went missing.
The consulate assisted police to track down family members to help compare their DNA profiles.
As Alpine glaciers melt because of global warming, the remains of long-lost climbers have increasingly been emerging from the shrinking mountain ice.
A mountain rescue pilot discovered remains and climbing equipment belonging to British climber Jonathan Conville, missing since 1979, in 2013 near the peak of the Matterhorn.
Last year the body of a Czech climber who disappeared 40 years ago following an accident was found in the Bernese Alps. | Remains found at the foot of Switzerland's Matterhorn glacier have been identified as two Japanese climbers who disappeared 45 years ago. | | The song, featuring Charlie Puth, rose 21 places to become the fastest-selling single of the year so far with combined chart sales of 193,000 copies.
It also set a new streaming record with 3.68 million streams in seven days.
The track features on the Fast & Furious 7 soundtrack and pays tribute to cast member Paul Walker, who died while filming the blockbuster in 2013.
It is Khalifa's second number one single after a guest appearance on Maroon 5's Payphone in 2012.
Omi's Cheerleader was another high climber, jumping 25 places to number two, according to the Official Charts Company.
Last week's number one - Hold My Hand by Jess Glynne - slipped down to three, while Spanish house DJ Dr Kucho's collaboration with Gregor Salto, Can't Stop Playing, was the highest new entry at four.
See the UK Top 40 singles chart
See the UK Top 40 albums chart
BBC Radio 1's Official Chart Show
In the album chart, Paul Simon scored his first number one for 25 years with his greatest hits record The Ultimate Collection. His last chart topper was 1990's The Rhythm Of The Saints.
James Bay, Ed Sheeran and Sam Smith all held firm, occupying the second to fourth places on the chart.
Wombat's third studio album Glitterbug was a new entry at five.
All Time Low's Future Hearts, which had topped the album chart last week, fell to 18. | US rapper Wiz Khalifa has topped the UK singles chart with his track, See You Again. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | During karyogamy, the haploid nuclei contributed by the two parents fuse, which produces what? | | | Every human cell has the same what, and each cell has thousands of them? | There are about 22,000 genes in every human cell. Does every human cell have the same genes? Yes. Does every human cell make the same proteins? No. In a multicellular organism, such as us, cells have specific functions because they have different proteins. They have different proteins because different genes are expressed in different cell types (which is known as gene expression ). | | Fertilized mollusk eggs develop into what? | Mollusks reproduce sexually. Most species have separate male and female sexes. Fertilization may be internal or external, depending on the species. Fertilized eggs develop into larvae. There may be one or more larval stages. Each one is different from the adult stage. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what happens to fibers after muscle activation? | Muscle contraction occurs when muscle fibers get shorter.. Muscle activation is a prerequisite for muscle contraction.. muscles activate before fibers get shorter | | What do most streams start with? | Streams may start with runoff or water seeping out of a spring.. Most water comes from precipitation.. most streams start with precipitation | | Mitosis begins when what unite in fertilization? | Gametes then unite in fertilization and form a diploid zygote.. Mitosis produces diploid cells.. Gametes then unite in fertilization and begin Mitosis | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 128 evaluation samples * Columns: question and fact * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | fact | |:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------| | The thermal production of a stove is generically used for | a stove generates heat for cooking usually | | What creates a valley? | a valley is formed by a river flowing | | when it turns day and night on a planet, what cause this? | a planet rotating causes cycles of day and night on that planet | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### msmarco_pairs * Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what does perjure mean | Perjure (verb) to cause to violate an oath or a vow; to cause to make oath knowingly to what is untrue; to make guilty of perjury; to forswear; to corrupt; -- often used reflexively; as, he perjured himself. Perjure (verb) to make a false oath to; to deceive by oaths and protestations. Perjure (noun) a perjured person | | weather in salalah | Average monthly weather in Salalah, Oman. Salalah has an hot and desert climate, however with temperatures that rarely reach above 33 degrees Celsius (91° Fahrenheit). Most rainfall (monsoon) is seen in the months July and August. | | what is dukan diet plan | The Dukan Diet is a protein based nutritional approach designed by Pierre Dukan, a French nutritionist and dietician. The Dukan Diet, or Dukan method proposes a healthy eating plan which is based on how primitive man used to eat when we were hunter-gatherers.It includes 100 foods, of which 72 are animal sourced and 28 come from plants.And you can eat as much as you like, as long as you stick to those 100 foods.he Dukan Diet, or Dukan method proposes a healthy eating plan which is based on how primitive man used to eat when we were hunter-gatherers. It includes 100 foods, of which 72 are animal sourced and 28 come from plants. And you can eat as much as you like, as long as you stick to those 100 foods. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### nq_pairs * Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what do you call more than one hypothesis | Hypothesis A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with the available scientific theories. Even though the words "hypothesis" and "theory" are often used synonymously, a scientific hypothesis is not the same as a scientific theory. A working hypothesis is a provisionally accepted hypothesis proposed for further research.[1] | | dragons name on how to train your dragon | How to Train Your Dragon (film) Stoick assembles a fleet to find the dragons' nest, leaving Hiccup in a dragon-fighting class taught by Gobber. Hiccup returns to the forest to find the Night Fury still there, and realizes it is unable to fly properly because of its crippled caudal fin. Hiccup gradually tames the dragon and gives it the name "Toothless", for its retractable teeth. Hiccup makes a harness and prosthetic fin that allows him to guide the dragon in free flight. By studying Toothless' behavior, Hiccup becomes proficient in subduing the captive dragons during training. Stoick's fleet arrives home unsuccessful, but he is cheered by Hiccup's unexpected success. | | was the original pre-modern maya script based on syllabic alphabetic or ideographic principles | Mesoamerican writing systems Maya writing first developed as only utilizing logograms, but later included the use of phonetic complements in order to differentiate between the semantic meanings of the logograms and for context that allows for syllabic spelling of words.[1] | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### trivia_pairs * Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What was heavyweight boxer Joe Louis' nickname? | Joe Louis - Biography - IMDb Joe Louis Jump to: Overview  (5) | Mini Bio  (1) | Spouse  (3) | Trivia  (19) | Personal Quotes  (3) Overview (5) 6' 1½" (1.87 m) Mini Bio (1) Joe Louis is considered by many fistic experts and fans as the greatest Heavyweight Boxing Champion in the sport's history. Born into a poor family, Joe Louis's mother felt the only way her son could escape poverty was through music. She bought him a violin and sent him off daily to lessons. On his way there, young Joe would pass by a boxing gym. In no time, he was working out at the gym, training for a boxing career. His amateur career started off disastrously, as he was knocked-out down 16 times in losing the fight. However, he was determined to continue and posted an outstanding amateur career with only 5 defeats in 60 fights. He turned professional and quickly racked up one of the most impressive winning streaks in boxing history. He was nicknamed, The Brown Bomber, and became the first boxer to defeat six heavyweight champions ( Primo Carnera , Max Baer , Jack Sharkey , Jimmy Braddock , Max Schmeling , and Jersey Joe Walcott ). After winning the championship, he held it almost 12 years to set a record, plus set another record with 25 successful title defenses. He retired with a 60-1 record, only to make an unsuccessful and very sad comeback at the age of 37. While champion, Joe Louis volunteered to join the U.S. Army at the height of his career. He made two title defenses in which he donated his entire purses to relief funds to help both the Army and the Navy. He spent almost five years in the service and boxed hundreds of exhibitions. However, after the war, he was hounded by the Internal Revenue Service to pay back taxes on the purses he had donated. He suffered terribly through this ordeal. and soon found himself broke. He launched a "controversial" pro-wrestling career and was undefeated in some 20 matches before retiring with a heart problem. He was helped by his good friend Frank Sinatra and acted in a few films, worked as a host in Las Vegas, and made numerous appearances for boxing. He died a few years after suffering a massive stroke. Joe Louis was buried with full-military honors, and it was said that he was "most" proud of his European-African-Middle Eastern Medal and his Victory Medal World War II. In or out of the ring, Joe Louis was a Champion. - IMDb Mini Biography By: angelsunchained Spouse (3) Born at 8:00 a.m. CST. World Heavyweight boxing champion, 1937-1949. Inducted into the International Boxing Hall of Fame, 1990. His reign as heavyweight champion (12 years) is a boxing record that stands in all weight divisions. He fought so many bad opponents that they were dubbed the "Bum of the Month" tour. Inducted into the World Boxing Hall of Fame, 1980. Pictured on a 29¢ US commemorative postage stamp in the Sports series, issued 22 June 1993. The Joe Louis Arena in Detroit, Michigan, is named after him. Has a daughter, Jacquelin (b. 1943), with Marva. Because Louis had been down on his luck in his last years and had died in poverty, his funeral was paid for by his most famous rival (and later friend), German boxer Max Schmeling . Biography in "The Scribner Encyclopedia of American Lives," Volume One, 1981-1985, pages 512-515, New York: Charles Scribner's Sons, 1998. Portrayed by Bernie Casey in Ring of Passion (1978) and Coley Wallace in The Joe Louis Story (1953) and Raging Bull (1980). Buried in Arlington National Cemetery. Americans tend to pronounce Louis's second name - "Lewis" - whereas Europeans tend to pronounce it "Loo-e." Curiously enough, Louis Armstrong is universally referred to as "Loo-e" Armstrong even though, in one of the lines in the lyrics to Armstrong's classic, "Hello Dolly," one can clearly hear Armstrong sing, "Hello Dolly, this is "Lewis" Dolly...". One of his grandmothers was a Native American. Is played by Leonard Roberts , who gained nearly 20 lbs. for the role, in Joe and Max (2002). He was a student of mail order physical culture trainer, Charles Atlas, and a graduate of his course. Served in the U.S. Arm | | On the human body, exungulation is the trimming of what? | Human Physiology/Integumentary System - Wikibooks, open books for an open world Human Physiology/Integumentary System Introduction[ edit ] The integumentary system consists of the skin, hair, nails, the subcutaneous tissue below the skin,and assorted glands.The most obvious function of the integumentary system is the protection that the skin gives to underlying tissues. The skin not only keeps most harmful substances out, but also prevents the loss of fluids. A major function of the subcutaneous tissue is to connect the skin to underlying tissues such as muscles. Hair on the scalp provides insulation from cold for the head. The hair of eyelashes and eyebrows helps keep dust and perspiration out of the eyes, and the hair in our nostrils helps keep dust out of the nasal cavities. Any other hair on our bodies no longer serves a function, but is an evolutionary remnant. Nails protect the tips of fingers and toes from mechanical injury. Fingernails give the fingers greater ability to pick up small objects. There are four types of glands in the integumentary system: Sudoriferous glands, Sebaceous glands, Ceruminous glands, and Mammary glands. Sudoriferous glands are sweat producing glands. These are important to help maintain body temperature. Sebaceous glands are oil producing glands which help inhibit bacteria, keep us waterproof and prevent our hair and skin from drying out. Ceruminous glands produce earwax which keeps the outer surface of the eardrum pliable and prevents drying. Mammary glands produce milk. Skin[ edit ] In zoology and dermatology, skin is an organ of the integumentary system made up of a layer of tissues that guard underlying muscles and organs. As the interface with the surroundings, it plays the most important role in protecting against pathogens. Its other main functions are insulation and temperature regulation, sensation and vitamin D and B synthesis. Skin is considered one of the most important parts of the body. Skin has pigmentation, melanin, provided by melanocytes, which absorbs some of the potentially dangerous radiation in sunlight. It also contains DNA repair enzymes which reverse UV damage, and people who lack the genes for these enzymes suffer high rates of skin cancer. One form predominantly produced by UV light, malignant melanoma, is particularly invasive, causing it to spread quickly, and can often be deadly. Human skin pigmentation varies among populations in a striking manner. This has sometimes led to the classification of people(s) on the basis of skin color. Damaged skin will try to heal by forming scar tissue, often giving rise to discoloration and depigmentation of the skin. The skin is often known as "the largest organ in the human body". This applies to exterior surface, as it covers the body, appearing to have the largest surface area of all the organs. Moreover, it applies to weight, as it weighs more than any single internal organ, accounting for about 15 percent of body weight. For the average adult human, the skin has a surface area of between 1.5-2.0 square meters, most of it is between 2-3 mm thick. The average square inch of skin holds 650 sweat glands, 20 blood vessels, 60,000 melanocytes, and more than a thousand nerve endings. The use of natural or synthetic cosmetics to treat the appearance of the face and condition of the skin (such as pore control and black head cleansing) is common among many cultures. Layers[ edit ] The skin has two major layers which are made of different tissues and have very different functions. Diagram of the layers of human skin Skin is composed of the epidermis and the dermis. Below these layers lies the hypodermis or subcutaneous adipose layer, which is not usually classified as a layer of skin. The outermost epidermis consists of stratified squamous keratinizing epithelium with an underlying basement membrane. It contains no blood vessels, and is nourished by diffusion from the dermis. The main type of cells which make up the epidermis are keratinocytes, with melanocytes and Langerhans cells also present. The epidermis can be further subd | | Which disease has the medical term variola? | Smallpox Glossary of Terms with Medical Definitions See the entire definition of Acquired Aerosolization: The production of an aerosol -- a fine mist or spray containing minute par... See the entire definition of Aerosolization Allergy: A misguided reaction to foreign substances by the immune system, the body system ... See the entire definition of Allergy Anthrax: A serious bacterial infection caused by Bacillus anthracis that occurs primarily ... See the entire definition of Anthrax Antiviral: An agent that kills a virus or that suppresses its ability to replicate and, he... See the entire definition of Antiviral Arms: An appendage in anatomy and in clinical trials. See: Arm. Arthritis: Inflammation of a joint. When joints are inflamed they can develop stiffness, w... See the entire definition of Arthritis Assay: An assay is an analysis done to determine: The presence of a substance an... See the entire definition of Assay Asymptomatic: Without symptoms. For example, an asymptomatic infection is an infection wit... See the entire definition of Asymptomatic Atopic: A predisposition toward developing certain allergic hypersensitivity reactions. At... See the entire definition of Atopic Atopic dermatitis: A skin disease characterized by areas of severe itching, redness, scali... See the entire definition of Biotechnology Bioterrorism: Terrorism using biologic agents that are harmful to humans. Biological disea... See the entire definition of Bioterrorism Blindness: Loss of useful sight. Blindness can be temporary or permanent. Damage to any p... See the entire definition of Blindness Blister: A collection of fluid underneath the top layer of skin (epidermis). There are man... See the entire definition of Blister Brain: The portion of the central nervous system that is located within the skull. It func... See the entire definition of Brain Breathing: The process of respiration, during which air is inhaled into the lungs through ... See the entire definition of Breathing Calf: The belly or fleshy hind part of the back of the leg below the knee. The calf is mad... See the entire definition of Calf Cancer: An abnormal growth of cells which tend to proliferate in an uncontrolled way and, ... See the entire definition of Cancer CDC: The Centers for Disease Control and Prevention, the US agency charged with tracking a... See the entire definition of CDC Cell: The basic structural and functional unit of any living thing. Each cell is a small c... See the entire definition of Cell Centers for Disease Control and Prevention: The US agency charged with tracking and invest... See the entire definition of Contagious Contrast: Short for "contrast media." Contrast media are X-ray dyes used to provide contra... See the entire definition of Contrast Cough: A rapid expulsion of air from the lungs, typically in order to clear the lung airwa... See the entire definition of Cough Cowpox: A mild skin disease of milk cows, principally confined to the udder and teats, tha... See the entire definition of Cowpox Depression: An illness that involves the body, mood, and thoughts and that affects the way... See the entire definition of Depression Dermatitis: Inflammation of the skin, either due to an inherent skin defect, direct contac... See the entire definition of Dermatitis Diagnosis: 1 The nature of a disease; the identification of an illness. 2 A ... See the entire definition of Diagnosis DNA: Deoxyribonucleic acid. One of two types of molecules that encode genetic information... See the entire definition of DNA Drain: A device for removing fluid from a cavity or wound. A drain is typically a tube or ... See the entire definition of Drain Eczema (dermatitis): A particular type of inflammatory reaction of the skin in which there... See the entire definition of Eczema Elbow: The juncture of the long bones in the middle portion of the upper extremity. The bo... See the entire definition of Elbow ELISA: Enzyme-linked immunosorbent assay, a rapid immunochemical test that involves an enz... See the entire definition of ELISA Emergency department: T | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | how much is a destination wedding in italy? | A wedding in Italy usually costs between 20 000 and 80 000 euros, depending on the number of guests and the number of rendered services and their quality. Wedding dresses, ceremony and reception are those three main expenses, which will take the most of your budget. | | how to transfer files on pc to ipad? | ['In iTunes, select the app from the list in the File Sharing section.', 'Drag and drop files from a folder or window onto the Documents list to copy them to your device.'] | | what is difference between saturated and unsaturated compounds? | Saturated vs. ... Unlike saturated hydrocarbons in which all hydrogen atoms and carbon atoms are bonded together with single bonds, unsaturated hydrocarbons have double or even triple bonds between the carbon atoms. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### paws-pos * Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------| | They were there to enjoy us and they were there to pray for us . | They were there for us to enjoy and they were there for us to pray . | | After the end of the war in June 1902 , Higgins left Southampton in the `` SSBavarian '' in August , returning to Cape Town the following month . | In August , after the end of the war in June 1902 , Higgins Southampton left the `` SSBavarian '' and returned to Cape Town the following month . | | From the merger of the Four Rivers Council and the Audubon Council , the Shawnee Trails Council was born . | Shawnee Trails Council was formed from the merger of the Four Rivers Council and the Audubon Council . | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 320 - `per_device_eval_batch_size`: 64 - `gradient_accumulation_steps`: 3 - `learning_rate`: 4e-05 - `weight_decay`: 0.0001 - `num_train_epochs`: 2 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1e-05} - `warmup_ratio`: 0.2 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTa-small-ST-v1-test-step2-checkpoints-tmp - `hub_strategy`: all_checkpoints - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 320 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 3 - `eval_accumulation_steps`: None - `learning_rate`: 4e-05 - `weight_decay`: 0.0001 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1e-05} - `warmup_ratio`: 0.2 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: False - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: True - `resume_from_checkpoint`: None - `hub_model_id`: bobox/DeBERTa-small-ST-v1-test-step2-checkpoints-tmp - `hub_strategy`: all_checkpoints - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | nq pairs loss | vitaminc-pairs loss | openbookqa pairs loss | scitail-pairs-pos loss | xsum-pairs loss | trivia pairs loss | paws-pos loss | qasc pairs loss | sciq pairs loss | msmarco pairs loss | negation-triplets loss | gooaq pairs loss | VitaminC_max_ap | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:-------------:|:-------------------:|:---------------------:|:----------------------:|:---------------:|:-----------------:|:-------------:|:---------------:|:---------------:|:------------------:|:----------------------:|:----------------:|:---------------:|:------------------------:| | 0.0031 | 1 | 0.7374 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0063 | 2 | 0.5723 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0094 | 3 | 0.551 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0125 | 4 | 0.7379 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0156 | 5 | 0.5271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0187 | 6 | 0.5858 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0219 | 7 | 0.6562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.025 | 8 | 0.8228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0281 | 9 | 0.9988 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0312 | 10 | 0.5582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0344 | 11 | 0.8546 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0375 | 12 | 0.4235 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0406 | 13 | 0.6418 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0437 | 14 | 0.6577 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0469 | 15 | 0.8333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.05 | 16 | 0.4082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0531 | 17 | 0.8101 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0563 | 18 | 0.5259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0594 | 19 | 0.9015 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0625 | 20 | 1.3915 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0656 | 21 | 0.26 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0688 | 22 | 0.6885 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0719 | 23 | 0.9357 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.075 | 24 | 0.7168 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0781 | 25 | 0.8678 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0813 | 26 | 0.4922 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0844 | 27 | 0.4937 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0875 | 28 | 0.5891 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0906 | 29 | 0.6921 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0938 | 30 | 0.8087 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0969 | 31 | 0.805 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1 | 32 | 0.6141 | 0.1978 | 2.3326 | 0.6932 | 0.0776 | 0.0913 | 0.7433 | 0.0248 | 0.1100 | 0.0197 | 0.3269 | 0.8681 | 0.3761 | 0.5467 | 0.8955 | | 0.1031 | 33 | 0.7783 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1062 | 34 | 0.8746 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1094 | 35 | 0.5085 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1125 | 36 | 0.4842 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1156 | 37 | 0.8097 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1187 | 38 | 0.5325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1219 | 39 | 0.7221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.125 | 40 | 0.708 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1281 | 41 | 0.2789 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1313 | 42 | 0.7986 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1344 | 43 | 0.9653 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1375 | 44 | 0.7857 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1406 | 45 | 0.2726 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1437 | 46 | 0.2458 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1469 | 47 | 0.6988 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.15 | 48 | 0.6328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1531 | 49 | 0.795 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1562 | 50 | 0.6163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1594 | 51 | 0.8269 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1625 | 52 | 0.52 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1656 | 53 | 0.7523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1688 | 54 | 0.6979 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1719 | 55 | 0.7845 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.175 | 56 | 0.9325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1781 | 57 | 0.8546 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1812 | 58 | 0.6392 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1844 | 59 | 0.5827 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1875 | 60 | 0.5961 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1906 | 61 | 0.3625 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1938 | 62 | 0.2584 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1969 | 63 | 0.4047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2 | 64 | 0.9429 | 0.2214 | 2.2601 | 0.6793 | 0.0836 | 0.0857 | 0.7304 | 0.0245 | 0.1140 | 0.0197 | 0.3460 | 0.8368 | 0.3801 | 0.5495 | 0.8960 | | 0.2031 | 65 | 0.7848 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2062 | 66 | 0.7589 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2094 | 67 | 0.5905 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2125 | 68 | 0.4211 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2156 | 69 | 0.5325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2188 | 70 | 0.3541 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2219 | 71 | 0.9396 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.225 | 72 | 0.6997 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2281 | 73 | 0.6415 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2313 | 74 | 1.1966 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2344 | 75 | 0.7142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2375 | 76 | 0.6048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2406 | 77 | 0.4639 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2437 | 78 | 0.9391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2469 | 79 | 0.6364 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.25 | 80 | 0.515 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2531 | 81 | 0.6505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2562 | 82 | 0.6149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2594 | 83 | 0.4471 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2625 | 84 | 1.4199 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2656 | 85 | 0.8484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2687 | 86 | 0.6412 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2719 | 87 | 0.65 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.275 | 88 | 0.7453 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2781 | 89 | 0.9506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2812 | 90 | 0.6083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2844 | 91 | 0.7102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2875 | 92 | 0.4037 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2906 | 93 | 0.769 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2938 | 94 | 0.8765 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2969 | 95 | 1.2583 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3 | 96 | 0.8885 | 0.2074 | 2.2594 | 0.6809 | 0.0757 | 0.0821 | 0.7431 | 0.0248 | 0.1059 | 0.0200 | 0.3400 | 0.8347 | 0.3709 | 0.5525 | 0.8969 | | 0.3031 | 97 | 0.6398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3063 | 98 | 0.8263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3094 | 99 | 0.8716 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3125 | 100 | 0.5523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3156 | 101 | 0.5811 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3187 | 102 | 0.7602 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3219 | 103 | 0.5337 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.325 | 104 | 0.8182 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3281 | 105 | 0.6641 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3312 | 106 | 1.0088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3344 | 107 | 0.7556 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3375 | 108 | 0.713 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3406 | 109 | 0.8385 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3438 | 110 | 0.5181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3469 | 111 | 1.0939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.35 | 112 | 0.5826 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3531 | 113 | 0.7121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3563 | 114 | 0.9371 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3594 | 115 | 0.7739 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3625 | 116 | 0.9612 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3656 | 117 | 0.7213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3688 | 118 | 0.621 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3719 | 119 | 0.5503 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.375 | 120 | 0.8439 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3781 | 121 | 0.7813 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3812 | 122 | 0.5637 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3844 | 123 | 0.9052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3875 | 124 | 0.64 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3906 | 125 | 0.6529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3937 | 126 | 0.6894 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3969 | 127 | 0.8604 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4 | 128 | 0.8503 | 0.2085 | 2.0338 | 0.7888 | 0.0776 | 0.0820 | 0.7111 | 0.0248 | 0.1131 | 0.0210 | 0.3429 | 0.7917 | 0.3744 | 0.5518 | 0.8969 | | 0.4031 | 129 | 0.8171 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4062 | 130 | 1.0401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4094 | 131 | 0.4243 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4125 | 132 | 0.3778 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4156 | 133 | 0.7651 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4188 | 134 | 0.6003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4219 | 135 | 0.6023 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.425 | 136 | 0.6079 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4281 | 137 | 0.6206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4313 | 138 | 0.4694 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4344 | 139 | 0.7528 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4375 | 140 | 0.8395 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4406 | 141 | 0.6689 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4437 | 142 | 0.6547 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4469 | 143 | 0.9242 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.45 | 144 | 0.9496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4531 | 145 | 0.6506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4562 | 146 | 0.786 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4594 | 147 | 0.7414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4625 | 148 | 0.3978 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4656 | 149 | 0.5635 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4688 | 150 | 0.9466 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4719 | 151 | 0.5251 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.475 | 152 | 0.6636 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4781 | 153 | 0.7834 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4813 | 154 | 0.6177 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4844 | 155 | 0.4558 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4875 | 156 | 0.5228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4906 | 157 | 0.5543 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4938 | 158 | 0.7127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4969 | 159 | 0.4227 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5 | 160 | 0.5914 | 0.2085 | 2.0564 | 0.6668 | 0.0997 | 0.0846 | 0.7139 | 0.0257 | 0.1236 | 0.0201 | 0.3291 | 0.7866 | 0.3919 | 0.5543 | 0.8989 | | 0.5031 | 161 | 0.3874 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5062 | 162 | 0.8134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5094 | 163 | 0.5596 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5125 | 164 | 0.2877 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5156 | 165 | 0.5218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5188 | 166 | 0.5282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5219 | 167 | 0.7528 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.525 | 168 | 0.7174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5281 | 169 | 0.6902 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5312 | 170 | 0.7486 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5344 | 171 | 0.6333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5375 | 172 | 1.2932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5406 | 173 | 0.6259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5437 | 174 | 0.8357 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5469 | 175 | 0.3604 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.55 | 176 | 0.6598 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5531 | 177 | 0.3169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5563 | 178 | 0.8629 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5594 | 179 | 0.3648 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5625 | 180 | 0.5103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5656 | 181 | 0.6255 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5687 | 182 | 0.4382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5719 | 183 | 0.4647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.575 | 184 | 0.4218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5781 | 185 | 0.8244 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5813 | 186 | 0.6579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5844 | 187 | 0.8384 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5875 | 188 | 0.5266 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5906 | 189 | 0.5079 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5938 | 190 | 0.2574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5969 | 191 | 0.4162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6 | 192 | 0.7872 | 0.2105 | 2.0662 | 0.7506 | 0.0736 | 0.0674 | 0.7073 | 0.0252 | 0.1128 | 0.0193 | 0.3317 | 0.7631 | 0.3749 | 0.5554 | 0.8982 | | 0.6031 | 193 | 0.2606 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6062 | 194 | 0.8808 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6094 | 195 | 0.7685 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6125 | 196 | 0.7186 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6156 | 197 | 0.1147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6188 | 198 | 0.2816 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6219 | 199 | 0.506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.625 | 200 | 0.5699 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6281 | 201 | 0.2746 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6312 | 202 | 0.7131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6344 | 203 | 0.9307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6375 | 204 | 0.6033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6406 | 205 | 0.7203 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6438 | 206 | 0.7422 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6469 | 207 | 0.6955 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.65 | 208 | 0.7139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6531 | 209 | 0.4741 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6562 | 210 | 0.2658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6594 | 211 | 0.6033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6625 | 212 | 0.7776 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6656 | 213 | 0.6791 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6687 | 214 | 0.4367 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6719 | 215 | 0.7212 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.675 | 216 | 0.7797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6781 | 217 | 0.4547 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6813 | 218 | 0.6771 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6844 | 219 | 0.5488 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6875 | 220 | 0.7352 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6906 | 221 | 0.9567 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6937 | 222 | 0.4274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6969 | 223 | 0.7653 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7 | 224 | 0.5672 | 0.1809 | 2.0751 | 0.7142 | 0.0846 | 0.0593 | 0.7161 | 0.0251 | 0.1252 | 0.0190 | 0.3040 | 0.7823 | 0.3399 | 0.5487 | 0.9005 | | 0.7031 | 225 | 0.6116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7063 | 226 | 0.6484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7094 | 227 | 0.669 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7125 | 228 | 0.263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7156 | 229 | 0.6181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7188 | 230 | 0.8956 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7219 | 231 | 0.5363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.725 | 232 | 0.823 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7281 | 233 | 0.7795 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7312 | 234 | 0.3688 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7344 | 235 | 0.3835 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7375 | 236 | 0.3393 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7406 | 237 | 0.4792 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7438 | 238 | 0.3966 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7469 | 239 | 0.2902 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.75 | 240 | 0.6716 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7531 | 241 | 0.6783 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7562 | 242 | 0.4794 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7594 | 243 | 0.8283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7625 | 244 | 0.6875 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7656 | 245 | 0.8384 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7688 | 246 | 0.5796 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7719 | 247 | 0.6206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.775 | 248 | 0.7836 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7781 | 249 | 0.615 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7812 | 250 | 0.433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7844 | 251 | 0.7394 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7875 | 252 | 0.1203 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7906 | 253 | 1.0909 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7937 | 254 | 0.7107 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7969 | 255 | 0.3464 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8 | 256 | 0.9347 | 0.1729 | 2.0034 | 0.7773 | 0.0726 | 0.0565 | 0.6547 | 0.0257 | 0.1264 | 0.0197 | 0.2844 | 0.7501 | 0.3155 | 0.5497 | 0.8987 | | 0.8031 | 257 | 0.464 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8063 | 258 | 0.4622 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8094 | 259 | 0.5124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8125 | 260 | 0.832 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8156 | 261 | 0.6264 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8187 | 262 | 0.5483 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8219 | 263 | 0.5929 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.825 | 264 | 0.5797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8281 | 265 | 0.5292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8313 | 266 | 0.5376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8344 | 267 | 0.7102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8375 | 268 | 0.4605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8406 | 269 | 1.2713 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8438 | 270 | 0.7764 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8469 | 271 | 0.7517 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.85 | 272 | 0.614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8531 | 273 | 0.6046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8562 | 274 | 0.7111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8594 | 275 | 0.4401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8625 | 276 | 0.4351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8656 | 277 | 0.7498 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8688 | 278 | 0.7173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8719 | 279 | 0.4696 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.875 | 280 | 0.6246 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8781 | 281 | 0.7578 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8812 | 282 | 0.3533 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8844 | 283 | 0.7328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8875 | 284 | 0.6964 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8906 | 285 | 0.6431 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8938 | 286 | 0.7155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8969 | 287 | 0.6328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9 | 288 | 0.7895 | 0.1806 | 1.9555 | 0.7712 | 0.0754 | 0.0566 | 0.7308 | 0.0249 | 0.1089 | 0.0198 | 0.2799 | 0.7942 | 0.3324 | 0.5510 | 0.8979 | | 0.9031 | 289 | 0.5752 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9062 | 290 | 0.666 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9094 | 291 | 0.874 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9125 | 292 | 0.7431 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9156 | 293 | 0.8332 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9187 | 294 | 0.7082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9219 | 295 | 0.6618 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.925 | 296 | 0.2375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9281 | 297 | 0.5305 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9313 | 298 | 0.1686 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9344 | 299 | 0.7938 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9375 | 300 | 0.2629 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9406 | 301 | 0.973 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9437 | 302 | 0.649 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9469 | 303 | 0.3329 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.95 | 304 | 0.6105 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9531 | 305 | 0.3621 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9563 | 306 | 0.5165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9594 | 307 | 0.6075 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9625 | 308 | 0.3091 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9656 | 309 | 0.2762 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9688 | 310 | 0.5736 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9719 | 311 | 0.3876 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.975 | 312 | 1.8005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9781 | 313 | 0.6344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9812 | 314 | 0.9414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9844 | 315 | 0.4782 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9875 | 316 | 0.4196 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9906 | 317 | 0.5288 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9938 | 318 | 0.5888 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9969 | 319 | 0.4598 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0 | 320 | 0.5085 | 0.1746 | 1.9213 | 0.6944 | 0.0689 | 0.0551 | 0.6845 | 0.0250 | 0.1042 | 0.0207 | 0.2857 | 0.7787 | 0.3040 | 0.5479 | 0.9030 | | 1.0031 | 321 | 0.647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0063 | 322 | 0.4768 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0094 | 323 | 0.4834 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0125 | 324 | 0.6115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0156 | 325 | 0.4611 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0188 | 326 | 0.4812 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0219 | 327 | 0.5914 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.025 | 328 | 0.7206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0281 | 329 | 0.7854 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0312 | 330 | 0.432 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0344 | 331 | 0.6365 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0375 | 332 | 0.3754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0406 | 333 | 0.5096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0437 | 334 | 0.5762 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0469 | 335 | 0.6938 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.05 | 336 | 0.343 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0531 | 337 | 0.7258 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0562 | 338 | 0.4658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0594 | 339 | 0.7108 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0625 | 340 | 1.3076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0656 | 341 | 0.2397 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0688 | 342 | 0.4853 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0719 | 343 | 0.741 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.075 | 344 | 0.6066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0781 | 345 | 0.6838 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0813 | 346 | 0.4393 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0844 | 347 | 0.4102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0875 | 348 | 0.4947 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0906 | 349 | 0.5212 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0938 | 350 | 0.6889 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0969 | 351 | 0.625 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1 | 352 | 0.5093 | 0.1819 | 1.9222 | 0.7048 | 0.0801 | 0.0626 | 0.6875 | 0.0244 | 0.1099 | 0.0197 | 0.2770 | 0.7761 | 0.3032 | 0.5489 | 0.9028 | | 1.1031 | 353 | 0.6242 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1062 | 354 | 0.7228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1094 | 355 | 0.3717 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1125 | 356 | 0.3442 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1156 | 357 | 0.649 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1187 | 358 | 0.3935 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1219 | 359 | 0.6131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.125 | 360 | 0.5322 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1281 | 361 | 0.2073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1313 | 362 | 0.6735 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1344 | 363 | 0.7604 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1375 | 364 | 0.6165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1406 | 365 | 0.1963 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1438 | 366 | 0.1668 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1469 | 367 | 0.5055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.15 | 368 | 0.4919 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1531 | 369 | 0.7166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1562 | 370 | 0.444 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1594 | 371 | 0.6237 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1625 | 372 | 0.4197 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1656 | 373 | 0.5569 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1687 | 374 | 0.5274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1719 | 375 | 0.6259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.175 | 376 | 0.7696 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1781 | 377 | 0.6437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1812 | 378 | 0.5067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1844 | 379 | 0.3927 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1875 | 380 | 0.4557 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1906 | 381 | 0.2425 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1938 | 382 | 0.1677 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1969 | 383 | 0.3555 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2 | 384 | 0.8643 | 0.1789 | 1.8801 | 0.7123 | 0.0711 | 0.0583 | 0.6368 | 0.0244 | 0.1173 | 0.0195 | 0.2852 | 0.7318 | 0.3245 | 0.5509 | 0.9035 | | 1.2031 | 385 | 0.6056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2063 | 386 | 0.5924 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2094 | 387 | 0.4131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2125 | 388 | 0.3347 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2156 | 389 | 0.4317 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2188 | 390 | 0.2488 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2219 | 391 | 0.6856 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.225 | 392 | 0.5261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2281 | 393 | 0.4683 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2312 | 394 | 1.066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2344 | 395 | 0.5434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2375 | 396 | 0.4129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2406 | 397 | 0.3367 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2437 | 398 | 0.716 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2469 | 399 | 0.4767 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.25 | 400 | 0.3659 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2531 | 401 | 0.4731 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2563 | 402 | 0.4562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2594 | 403 | 0.3397 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2625 | 404 | 1.2082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2656 | 405 | 0.6162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2688 | 406 | 0.4767 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2719 | 407 | 0.4384 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.275 | 408 | 0.5368 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2781 | 409 | 0.6885 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2812 | 410 | 0.4318 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2844 | 411 | 0.5648 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2875 | 412 | 0.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2906 | 413 | 0.573 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2937 | 414 | 0.6759 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2969 | 415 | 1.0739 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3 | 416 | 0.6794 | 0.1615 | 1.8976 | 0.7091 | 0.0704 | 0.0550 | 0.6475 | 0.0251 | 0.1084 | 0.0199 | 0.2811 | 0.7549 | 0.3167 | 0.5507 | 0.9035 | | 1.3031 | 417 | 0.4515 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3062 | 418 | 0.5992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3094 | 419 | 0.7221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3125 | 420 | 0.3968 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3156 | 421 | 0.4198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3188 | 422 | 0.6268 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3219 | 423 | 0.3976 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.325 | 424 | 0.6003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3281 | 425 | 0.4381 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3313 | 426 | 0.8803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3344 | 427 | 0.5635 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3375 | 428 | 0.5262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3406 | 429 | 0.6506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3438 | 430 | 0.3486 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3469 | 431 | 0.9099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.35 | 432 | 0.4199 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3531 | 433 | 0.4908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3562 | 434 | 0.6869 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3594 | 435 | 0.5644 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3625 | 436 | 0.6714 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3656 | 437 | 0.4976 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3687 | 438 | 0.4468 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3719 | 439 | 0.3923 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.375 | 440 | 0.5753 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3781 | 441 | 0.5134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3813 | 442 | 0.3858 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3844 | 443 | 0.6681 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3875 | 444 | 0.4702 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3906 | 445 | 0.501 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3938 | 446 | 0.459 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3969 | 447 | 0.5879 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4 | 448 | 0.6276 | 0.1565 | 1.8430 | 0.7535 | 0.0568 | 0.0484 | 0.6307 | 0.0245 | 0.1117 | 0.0196 | 0.2782 | 0.6982 | 0.3191 | 0.5517 | 0.9040 | | 1.4031 | 449 | 0.5358 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4062 | 450 | 0.8326 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4094 | 451 | 0.2866 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4125 | 452 | 0.247 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4156 | 453 | 0.519 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4187 | 454 | 0.4117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4219 | 455 | 0.437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.425 | 456 | 0.3619 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4281 | 457 | 0.4273 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4312 | 458 | 0.2739 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4344 | 459 | 0.5714 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4375 | 460 | 0.5485 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4406 | 461 | 0.4829 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4438 | 462 | 0.4904 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4469 | 463 | 0.6449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.45 | 464 | 0.6896 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4531 | 465 | 0.4174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4563 | 466 | 0.5254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4594 | 467 | 0.5287 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4625 | 468 | 0.2421 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4656 | 469 | 0.3939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4688 | 470 | 0.7248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4719 | 471 | 0.3479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.475 | 472 | 0.472 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4781 | 473 | 0.5639 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4812 | 474 | 0.4077 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4844 | 475 | 0.3173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4875 | 476 | 0.3307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4906 | 477 | 0.3761 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4937 | 478 | 0.5454 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4969 | 479 | 0.309 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5 | 480 | 0.4082 | 0.1554 | 1.8595 | 0.6959 | 0.0752 | 0.0433 | 0.6473 | 0.0247 | 0.1079 | 0.0187 | 0.2609 | 0.7203 | 0.3060 | 0.5562 | 0.9034 | | 1.5031 | 481 | 0.2147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5063 | 482 | 0.5614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5094 | 483 | 0.3865 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5125 | 484 | 0.1715 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5156 | 485 | 0.3597 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5188 | 486 | 0.3827 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5219 | 487 | 0.4895 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.525 | 488 | 0.4987 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5281 | 489 | 0.4482 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5312 | 490 | 0.5808 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5344 | 491 | 0.3916 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5375 | 492 | 1.0877 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5406 | 493 | 0.4119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5437 | 494 | 0.6078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5469 | 495 | 0.2441 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.55 | 496 | 0.4769 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5531 | 497 | 0.218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5562 | 498 | 0.6377 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5594 | 499 | 0.2391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5625 | 500 | 0.3645 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5656 | 501 | 0.4185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5688 | 502 | 0.3363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5719 | 503 | 0.3712 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.575 | 504 | 0.2995 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5781 | 505 | 0.6178 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5813 | 506 | 0.464 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5844 | 507 | 0.5694 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5875 | 508 | 0.3587 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5906 | 509 | 0.3375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5938 | 510 | 0.1613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5969 | 511 | 0.2811 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6 | 512 | 0.5338 | 0.1449 | 1.8544 | 0.6914 | 0.0752 | 0.0407 | 0.6339 | 0.0251 | 0.1043 | 0.0182 | 0.2565 | 0.7161 | 0.2975 | 0.5553 | 0.9044 | | 1.6031 | 513 | 0.1862 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6062 | 514 | 0.6092 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6094 | 515 | 0.541 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6125 | 516 | 0.5297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6156 | 517 | 0.0664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6187 | 518 | 0.1557 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6219 | 519 | 0.3281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.625 | 520 | 0.3828 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6281 | 521 | 0.2087 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6313 | 522 | 0.5306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6344 | 523 | 0.6589 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6375 | 524 | 0.425 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6406 | 525 | 0.5026 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6438 | 526 | 0.5667 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6469 | 527 | 0.4748 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.65 | 528 | 0.5094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6531 | 529 | 0.3398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6562 | 530 | 0.1932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6594 | 531 | 0.4233 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6625 | 532 | 0.5848 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6656 | 533 | 0.5076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6687 | 534 | 0.286 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6719 | 535 | 0.5221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.675 | 536 | 0.579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6781 | 537 | 0.2717 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6812 | 538 | 0.4727 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6844 | 539 | 0.3777 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6875 | 540 | 0.537 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6906 | 541 | 0.6935 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6938 | 542 | 0.2929 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6969 | 543 | 0.5495 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7 | 544 | 0.3767 | 0.1562 | 1.8782 | 0.6965 | 0.0670 | 0.0358 | 0.6436 | 0.0247 | 0.1086 | 0.0183 | 0.2413 | 0.7195 | 0.3004 | 0.5553 | 0.9040 | | 1.7031 | 545 | 0.4054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7063 | 546 | 0.4114 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7094 | 547 | 0.4774 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7125 | 548 | 0.1662 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7156 | 549 | 0.4634 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7188 | 550 | 0.6514 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7219 | 551 | 0.3672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.725 | 552 | 0.6115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7281 | 553 | 0.5445 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7312 | 554 | 0.2447 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7344 | 555 | 0.2566 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7375 | 556 | 0.208 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7406 | 557 | 0.3175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7437 | 558 | 0.2546 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7469 | 559 | 0.1709 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.75 | 560 | 0.4799 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7531 | 561 | 0.5313 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7563 | 562 | 0.3248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7594 | 563 | 0.6279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7625 | 564 | 0.5193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7656 | 565 | 0.6262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7688 | 566 | 0.4297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7719 | 567 | 0.4763 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.775 | 568 | 0.5722 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7781 | 569 | 0.4347 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7812 | 570 | 0.3271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7844 | 571 | 0.5433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7875 | 572 | 0.0637 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7906 | 573 | 0.9049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7937 | 574 | 0.495 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7969 | 575 | 0.2218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8 | 576 | 0.7491 | 0.1476 | 1.8366 | 0.6921 | 0.0626 | 0.0385 | 0.6359 | 0.0251 | 0.1101 | 0.0179 | 0.2450 | 0.7223 | 0.2910 | 0.5548 | 0.9038 | | 1.8031 | 577 | 0.3658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8062 | 578 | 0.3549 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8094 | 579 | 0.4099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8125 | 580 | 0.6085 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8156 | 581 | 0.4319 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8188 | 582 | 0.3862 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8219 | 583 | 0.4094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.825 | 584 | 0.3864 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8281 | 585 | 0.3481 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8313 | 586 | 0.343 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8344 | 587 | 0.5048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8375 | 588 | 0.3418 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8406 | 589 | 1.0879 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8438 | 590 | 0.5701 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8469 | 591 | 0.5612 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.85 | 592 | 0.4498 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8531 | 593 | 0.4483 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8562 | 594 | 0.5586 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8594 | 595 | 0.363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8625 | 596 | 0.3553 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8656 | 597 | 0.547 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8687 | 598 | 0.5397 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8719 | 599 | 0.3574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.875 | 600 | 0.4596 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8781 | 601 | 0.5451 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8813 | 602 | 0.2327 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8844 | 603 | 0.5394 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8875 | 604 | 0.4933 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8906 | 605 | 0.4823 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8938 | 606 | 0.5327 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8969 | 607 | 0.4733 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9 | 608 | 0.5663 | 0.1455 | 1.8288 | 0.7343 | 0.0625 | 0.0389 | 0.6389 | 0.0251 | 0.1033 | 0.0183 | 0.2279 | 0.7394 | 0.2900 | 0.5539 | 0.9031 | | 1.9031 | 609 | 0.412 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9062 | 610 | 0.5386 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9094 | 611 | 0.6678 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9125 | 612 | 0.5554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9156 | 613 | 0.6435 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9187 | 614 | 0.529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9219 | 615 | 0.4753 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.925 | 616 | 0.1364 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9281 | 617 | 0.37 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9312 | 618 | 0.1058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9344 | 619 | 0.6059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9375 | 620 | 0.1856 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9406 | 621 | 0.8083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9438 | 622 | 0.4503 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9469 | 623 | 0.2172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.95 | 624 | 0.4348 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9531 | 625 | 0.2659 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9563 | 626 | 0.3441 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9594 | 627 | 0.4487 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9625 | 628 | 0.1922 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9656 | 629 | 0.191 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9688 | 630 | 0.3967 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9719 | 631 | 0.2729 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.975 | 632 | 1.56 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9781 | 633 | 0.4791 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9812 | 634 | 0.7554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9844 | 635 | 0.3477 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9875 | 636 | 0.2965 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9906 | 637 | 0.3262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9937 | 638 | 0.4571 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9969 | 639 | 0.3461 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0 | 640 | 0.3646 | 0.1450 | 1.8504 | 0.6874 | 0.0701 | 0.0423 | 0.6245 | 0.0250 | 0.1009 | 0.0184 | 0.2405 | 0.7371 | 0.2937 | 0.5540 | 0.9036 |
### Framework Versions - Python: 3.10.13 - Sentence Transformers: 3.0.1 - Transformers: 4.42.3 - PyTorch: 2.1.2 - Accelerate: 0.32.1 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ```