|
--- |
|
base_model: microsoft/deberta-v3-base |
|
datasets: |
|
- tals/vitaminc |
|
- allenai/scitail |
|
- allenai/sciq |
|
- allenai/qasc |
|
- sentence-transformers/msmarco-msmarco-distilbert-base-v3 |
|
- sentence-transformers/natural-questions |
|
- sentence-transformers/trivia-qa |
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- sentence-transformers/gooaq |
|
- google-research-datasets/paws |
|
language: |
|
- en |
|
library_name: sentence-transformers |
|
metrics: |
|
- pearson_cosine |
|
- spearman_cosine |
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- pearson_manhattan |
|
- spearman_manhattan |
|
- pearson_euclidean |
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- spearman_euclidean |
|
- pearson_dot |
|
- spearman_dot |
|
- pearson_max |
|
- spearman_max |
|
- cosine_accuracy |
|
- cosine_accuracy_threshold |
|
- cosine_f1 |
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- cosine_f1_threshold |
|
- cosine_precision |
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- 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 |
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- max_precision |
|
- max_recall |
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- max_ap |
|
pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:123245 |
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- loss:CachedGISTEmbedLoss |
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widget: |
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- source_sentence: what type of inheritance does haemochromatosis |
|
sentences: |
|
- Nestled on the tranquil banks of the Pamlico River, Moss Landing is a vibrant |
|
new community of thoughtfully conceived, meticulously crafted single-family homes |
|
in Washington, North Carolina. Washington is renowned for its historic architecture |
|
and natural beauty. |
|
- '1 Microwave on high for 8 to 10 minutes or until tender, turning the yams once. |
|
2 To microwave sliced yams: Wash, peel, and cut off the woody portions and ends. |
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3 Cut yams into quarters. 4 Place the yams and 1/2 cup water in a microwave-safe |
|
casserole.ake the Yams. 1 Place half the yams in a 1-quart casserole. 2 Layer |
|
with half the brown sugar and half the margarine. 3 Repeat the layers. 4 Bake, |
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uncovered, in a 375 degree F oven for 30 to 35 minutes or until the yams are glazed, |
|
spooning the liquid over the yams once or twice during cooking.' |
|
- Types 1, 2, and 3 hemochromatosis are inherited in an autosomal recessive pattern, |
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which means both copies of the gene in each cell have mutations. Most often, the |
|
parents of an individual with an autosomal recessive condition each carry one |
|
copy of the mutated gene but do not show signs and symptoms of the condition.Type |
|
4 hemochromatosis is distinguished by its autosomal dominant inheritance pattern.With |
|
this type of inheritance, one copy of the altered gene in each cell is sufficient |
|
to cause the disorder. In most cases, an affected person has one parent with the |
|
condition.ype 1, the most common form of the disorder, and type 4 (also called |
|
ferroportin disease) begin in adulthood. Men with type 1 or type 4 hemochromatosis |
|
typically develop symptoms between the ages of 40 and 60, and women usually develop |
|
symptoms after menopause. Type 2 hemochromatosis is a juvenile-onset disorder. |
|
- source_sentence: More than 273 people have died from the 2019-20 coronavirus outside |
|
mainland China . |
|
sentences: |
|
- 'More than 3,700 people have died : around 3,100 in mainland China and around |
|
550 in all other countries combined .' |
|
- 'More than 3,200 people have died : almost 3,000 in mainland China and around |
|
275 in other countries .' |
|
- more than 4,900 deaths have been attributed to COVID-19 . |
|
- source_sentence: The male reproductive system consists of structures that produce |
|
sperm and secrete testosterone. |
|
sentences: |
|
- What does the male reproductive system consist of? |
|
- What facilitates the diffusion of ions across a membrane? |
|
- Autoimmunity can develop with time, and its causes may be rooted in this? |
|
- source_sentence: Nitrogen gas comprises about three-fourths of earth's atmosphere. |
|
sentences: |
|
- What do all cells have in common? |
|
- What gas comprises about three-fourths of earth's atmosphere? |
|
- What do you call an animal in which the embryo, often termed a joey, is born immature |
|
and must complete its development outside the mother's body? |
|
- source_sentence: What device is used to regulate a person's heart rate? |
|
sentences: |
|
- 'Marie Antoinette and the French Revolution . Famous Faces . Mad Max: |
|
Maximilien Robespierre | PBS Extended Interviews > Resources > For Educators |
|
> Mad Max: Maximilien Robespierre Maximilien Robespierre was born May 6, 1758 |
|
in Arras, France. Educated at the Lycée Louis-le-Grand in Paris as a lawyer, Robespierre |
|
became a disciple of philosopher Jean-Jacques Rousseau and a passionate advocate |
|
for the poor. Called "the Incorruptible" because of his unwavering dedication |
|
to the Revolution, Robespierre joined the Jacobin Club and earned a loyal following. |
|
In contrast to the more republican Girondins and Marie Antoinette, Robespierre |
|
fiercely opposed declaring war on Austria, feeling it would distract from revolutionary |
|
progress in France. Robespierre''s exemplary oratory skills influenced the National |
|
Convention in 1792 to avoid seeking public opinion about the Convention’s decision |
|
to execute King Louis XVI. In 1793, the Convention elected Robespierre to the |
|
Committee of Public Defense. He was a highly controversial member, developing |
|
radical policies, warning of conspiracies, and suggesting restructuring the Convention. |
|
This behavior eventually led to his downfall, and he was guillotined without trial |
|
on 10th Thermidor An II (July 28, 1794), marking the end of the Reign of Terror. |
|
Famous Faces' |
|
- Devices for Arrhythmia Devices for Arrhythmia Updated:Dec 21,2016 In a medical |
|
emergency, life-threatening arrhythmias may be stopped by giving the heart an |
|
electric shock (as with a defibrillator ). For people with recurrent arrhythmias, |
|
medical devices such as a pacemaker and implantable cardioverter defibrillator |
|
(ICD) can help by continuously monitoring the heart's electrical system and providing |
|
automatic correction when an arrhythmia starts to occur. This section covers everything |
|
you need to know about these devices. Implantable Cardioverter Defibrillator (ICD) |
|
- 'vintage cleats | eBay vintage cleats: 1 2 3 4 5 eBay determines this price through |
|
a machine learned model of the product''s sale prices within the last 90 days. |
|
eBay determines trending price through a machine learned model of the product’s |
|
sale prices within the last 90 days. "New" refers to a brand-new, unused, unopened, |
|
undamaged item, and "Used" refers to an item that has been used previously. Top |
|
Rated Plus Sellers with highest buyer ratings Returns, money back Sellers with |
|
highest buyer ratings Returns, money back' |
|
model-index: |
|
- name: SentenceTransformer based on microsoft/deberta-v3-base |
|
results: |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: sts test |
|
type: sts-test |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.8253431554642914 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.870857890879963 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.8653068915625914 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.8667110599943904 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.8671346646296434 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.8681442638917114 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.7826717704847901 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.7685403521338614 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.8671346646296434 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.870857890879963 |
|
name: Spearman Max |
|
- task: |
|
type: binary-classification |
|
name: Binary Classification |
|
dataset: |
|
name: allNLI dev |
|
type: allNLI-dev |
|
metrics: |
|
- type: cosine_accuracy |
|
value: 0.71875 |
|
name: Cosine Accuracy |
|
- type: cosine_accuracy_threshold |
|
value: 0.8745474815368652 |
|
name: Cosine Accuracy Threshold |
|
- type: cosine_f1 |
|
value: 0.617169373549884 |
|
name: Cosine F1 |
|
- type: cosine_f1_threshold |
|
value: 0.7519949674606323 |
|
name: Cosine F1 Threshold |
|
- type: cosine_precision |
|
value: 0.5155038759689923 |
|
name: Cosine Precision |
|
- type: cosine_recall |
|
value: 0.7687861271676301 |
|
name: Cosine Recall |
|
- type: cosine_ap |
|
value: 0.6116004689391709 |
|
name: Cosine Ap |
|
- type: dot_accuracy |
|
value: 0.693359375 |
|
name: Dot Accuracy |
|
- type: dot_accuracy_threshold |
|
value: 401.3755187988281 |
|
name: Dot Accuracy Threshold |
|
- type: dot_f1 |
|
value: 0.566735112936345 |
|
name: Dot F1 |
|
- type: dot_f1_threshold |
|
value: 295.2575988769531 |
|
name: Dot F1 Threshold |
|
- type: dot_precision |
|
value: 0.4394904458598726 |
|
name: Dot Precision |
|
- type: dot_recall |
|
value: 0.7976878612716763 |
|
name: Dot Recall |
|
- type: dot_ap |
|
value: 0.5243551756921989 |
|
name: Dot Ap |
|
- type: manhattan_accuracy |
|
value: 0.724609375 |
|
name: Manhattan Accuracy |
|
- type: manhattan_accuracy_threshold |
|
value: 228.3092498779297 |
|
name: Manhattan Accuracy Threshold |
|
- type: manhattan_f1 |
|
value: 0.6267281105990783 |
|
name: Manhattan F1 |
|
- type: manhattan_f1_threshold |
|
value: 266.0207824707031 |
|
name: Manhattan F1 Threshold |
|
- type: manhattan_precision |
|
value: 0.5210727969348659 |
|
name: Manhattan Precision |
|
- type: manhattan_recall |
|
value: 0.7861271676300579 |
|
name: Manhattan Recall |
|
- type: manhattan_ap |
|
value: 0.6101425904568746 |
|
name: Manhattan Ap |
|
- type: euclidean_accuracy |
|
value: 0.720703125 |
|
name: Euclidean Accuracy |
|
- type: euclidean_accuracy_threshold |
|
value: 9.726119041442871 |
|
name: Euclidean Accuracy Threshold |
|
- type: euclidean_f1 |
|
value: 0.6303854875283447 |
|
name: Euclidean F1 |
|
- type: euclidean_f1_threshold |
|
value: 14.837699890136719 |
|
name: Euclidean F1 Threshold |
|
- type: euclidean_precision |
|
value: 0.5186567164179104 |
|
name: Euclidean Precision |
|
- type: euclidean_recall |
|
value: 0.8034682080924855 |
|
name: Euclidean Recall |
|
- type: euclidean_ap |
|
value: 0.6172110045723997 |
|
name: Euclidean Ap |
|
- type: max_accuracy |
|
value: 0.724609375 |
|
name: Max Accuracy |
|
- type: max_accuracy_threshold |
|
value: 401.3755187988281 |
|
name: Max Accuracy Threshold |
|
- type: max_f1 |
|
value: 0.6303854875283447 |
|
name: Max F1 |
|
- type: max_f1_threshold |
|
value: 295.2575988769531 |
|
name: Max F1 Threshold |
|
- type: max_precision |
|
value: 0.5210727969348659 |
|
name: Max Precision |
|
- type: max_recall |
|
value: 0.8034682080924855 |
|
name: Max Recall |
|
- type: max_ap |
|
value: 0.6172110045723997 |
|
name: Max Ap |
|
- task: |
|
type: binary-classification |
|
name: Binary Classification |
|
dataset: |
|
name: Qnli dev |
|
type: Qnli-dev |
|
metrics: |
|
- type: cosine_accuracy |
|
value: 0.673828125 |
|
name: Cosine Accuracy |
|
- type: cosine_accuracy_threshold |
|
value: 0.7472400069236755 |
|
name: Cosine Accuracy Threshold |
|
- type: cosine_f1 |
|
value: 0.6863468634686347 |
|
name: Cosine F1 |
|
- type: cosine_f1_threshold |
|
value: 0.7334084510803223 |
|
name: Cosine F1 Threshold |
|
- type: cosine_precision |
|
value: 0.6078431372549019 |
|
name: Cosine Precision |
|
- type: cosine_recall |
|
value: 0.788135593220339 |
|
name: Cosine Recall |
|
- type: cosine_ap |
|
value: 0.7293502303398447 |
|
name: Cosine Ap |
|
- type: dot_accuracy |
|
value: 0.6484375 |
|
name: Dot Accuracy |
|
- type: dot_accuracy_threshold |
|
value: 392.88726806640625 |
|
name: Dot Accuracy Threshold |
|
- type: dot_f1 |
|
value: 0.6634920634920635 |
|
name: Dot F1 |
|
- type: dot_f1_threshold |
|
value: 310.97833251953125 |
|
name: Dot F1 Threshold |
|
- type: dot_precision |
|
value: 0.5304568527918782 |
|
name: Dot Precision |
|
- type: dot_recall |
|
value: 0.885593220338983 |
|
name: Dot Recall |
|
- type: dot_ap |
|
value: 0.6331200610041253 |
|
name: Dot Ap |
|
- type: manhattan_accuracy |
|
value: 0.671875 |
|
name: Manhattan Accuracy |
|
- type: manhattan_accuracy_threshold |
|
value: 277.69342041015625 |
|
name: Manhattan Accuracy Threshold |
|
- type: manhattan_f1 |
|
value: 0.6830122591943958 |
|
name: Manhattan F1 |
|
- type: manhattan_f1_threshold |
|
value: 301.36639404296875 |
|
name: Manhattan F1 Threshold |
|
- type: manhattan_precision |
|
value: 0.582089552238806 |
|
name: Manhattan Precision |
|
- type: manhattan_recall |
|
value: 0.826271186440678 |
|
name: Manhattan Recall |
|
- type: manhattan_ap |
|
value: 0.7276384343706648 |
|
name: Manhattan Ap |
|
- type: euclidean_accuracy |
|
value: 0.68359375 |
|
name: Euclidean Accuracy |
|
- type: euclidean_accuracy_threshold |
|
value: 15.343950271606445 |
|
name: Euclidean Accuracy Threshold |
|
- type: euclidean_f1 |
|
value: 0.6895238095238095 |
|
name: Euclidean F1 |
|
- type: euclidean_f1_threshold |
|
value: 15.738676071166992 |
|
name: Euclidean F1 Threshold |
|
- type: euclidean_precision |
|
value: 0.6262975778546713 |
|
name: Euclidean Precision |
|
- type: euclidean_recall |
|
value: 0.7669491525423728 |
|
name: Euclidean Recall |
|
- type: euclidean_ap |
|
value: 0.7307379367367225 |
|
name: Euclidean Ap |
|
- type: max_accuracy |
|
value: 0.68359375 |
|
name: Max Accuracy |
|
- type: max_accuracy_threshold |
|
value: 392.88726806640625 |
|
name: Max Accuracy Threshold |
|
- type: max_f1 |
|
value: 0.6895238095238095 |
|
name: Max F1 |
|
- type: max_f1_threshold |
|
value: 310.97833251953125 |
|
name: Max F1 Threshold |
|
- type: max_precision |
|
value: 0.6262975778546713 |
|
name: Max Precision |
|
- type: max_recall |
|
value: 0.885593220338983 |
|
name: Max Recall |
|
- type: max_ap |
|
value: 0.7307379367367225 |
|
name: Max Ap |
|
--- |
|
|
|
# SentenceTransformer based on microsoft/deberta-v3-base |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the negation-triplets, [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) and global_dataset 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:** [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) <!-- at revision 8ccc9b6f36199bec6961081d44eb72fb3f7353f3 --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Datasets:** |
|
- negation-triplets |
|
- [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) |
|
- global_dataset |
|
- **Language:** en |
|
<!-- - **License:** Unknown --> |
|
## Evaluation |
|
|
|
### Metrics |
|
|
|
#### Semantic Similarity |
|
* Dataset: `sts-test` |
|
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| pearson_cosine | 0.8253 | |
|
| **spearman_cosine** | **0.8709** | |
|
| pearson_manhattan | 0.8653 | |
|
| spearman_manhattan | 0.8667 | |
|
| pearson_euclidean | 0.8671 | |
|
| spearman_euclidean | 0.8681 | |
|
| pearson_dot | 0.7827 | |
|
| spearman_dot | 0.7685 | |
|
| pearson_max | 0.8671 | |
|
| spearman_max | 0.8709 | |
|
|
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: steps |
|
- `per_device_train_batch_size`: 96 |
|
- `per_device_eval_batch_size`: 68 |
|
- `learning_rate`: 3.5e-05 |
|
- `weight_decay`: 0.0005 |
|
- `num_train_epochs`: 2 |
|
- `lr_scheduler_type`: cosine_with_min_lr |
|
- `lr_scheduler_kwargs`: {'num_cycles': 3.5, 'min_lr': 1.5e-05} |
|
- `warmup_ratio`: 0.33 |
|
- `save_safetensors`: False |
|
- `fp16`: True |
|
- `push_to_hub`: True |
|
- `hub_model_id`: bobox/DeBERTa3-base-STr-CosineWaves-checkpoints-tmp |
|
- `hub_strategy`: all_checkpoints |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: steps |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 96 |
|
- `per_device_eval_batch_size`: 68 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 1 |
|
- `eval_accumulation_steps`: None |
|
- `torch_empty_cache_steps`: None |
|
- `learning_rate`: 3.5e-05 |
|
- `weight_decay`: 0.0005 |
|
- `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': 3.5, 'min_lr': 1.5e-05} |
|
- `warmup_ratio`: 0.33 |
|
- `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/DeBERTa3-base-STr-CosineWaves-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 |
|
- `eval_use_gather_object`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.10.14 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.44.0 |
|
- PyTorch: 2.4.0 |
|
- Accelerate: 0.33.0 |
|
- Datasets: 2.21.0 |
|
- 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", |
|
} |
|
``` |
