--- language: - en library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - loss:MultipleNegativesRankingLoss - loss:ContrastiveLoss base_model: sentence-transformers/stsb-distilbert-base metrics: - 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 - average_precision - f1 - precision - recall - threshold - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 widget: - source_sentence: What is Mindset? sentences: - What is a mindset? - Can you eat only once a day? - Is law a good career choice? - source_sentence: Is a queef real? sentences: - Is "G" based on real events? - What is the entire court process? - How do I reduce my weight? - source_sentence: Is Cicret a scam? sentences: - Is the Cicret Bracelet a scam? - Was World War II Inevitable? - What are some of the best photos? - source_sentence: What is Planet X? sentences: - Do planet X exist? - What are the best C++ books? - How can I lose my weight fast? - source_sentence: How fast is fast? sentences: - How does light travel so fast? - How do I copyright my books? - What is a black hole made of? pipeline_tag: sentence-similarity co2_eq_emissions: emissions: 32.724475965905576 energy_consumed: 0.08418911136527617 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.399 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: SentenceTransformer based on sentence-transformers/stsb-distilbert-base results: - task: type: binary-classification name: Binary Classification dataset: name: quora duplicates type: quora-duplicates metrics: - type: cosine_accuracy value: 0.846 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7969297170639038 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.7791495198902607 name: Cosine F1 - type: cosine_f1_threshold value: 0.7139598727226257 name: Cosine F1 Threshold - type: cosine_precision value: 0.6977886977886978 name: Cosine Precision - type: cosine_recall value: 0.8819875776397516 name: Cosine Recall - type: cosine_ap value: 0.8230449963294564 name: Cosine Ap - type: dot_accuracy value: 0.843 name: Dot Accuracy - type: dot_accuracy_threshold value: 151.2908477783203 name: Dot Accuracy Threshold - type: dot_f1 value: 0.7660818713450294 name: Dot F1 - type: dot_f1_threshold value: 143.77838134765625 name: Dot F1 Threshold - type: dot_precision value: 0.7237569060773481 name: Dot Precision - type: dot_recall value: 0.8136645962732919 name: Dot Recall - type: dot_ap value: 0.7946044629726107 name: Dot Ap - type: manhattan_accuracy value: 0.838 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 194.99119567871094 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.7704081632653061 name: Manhattan F1 - type: manhattan_f1_threshold value: 247.49777221679688 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.6536796536796536 name: Manhattan Precision - type: manhattan_recall value: 0.937888198757764 name: Manhattan Recall - type: manhattan_ap value: 0.8149715271935773 name: Manhattan Ap - type: euclidean_accuracy value: 0.841 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 9.02225112915039 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.7703889585947302 name: Euclidean F1 - type: euclidean_f1_threshold value: 11.385245323181152 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.6463157894736842 name: Euclidean Precision - type: euclidean_recall value: 0.953416149068323 name: Euclidean Recall - type: euclidean_ap value: 0.8152967320117391 name: Euclidean Ap - type: max_accuracy value: 0.846 name: Max Accuracy - type: max_accuracy_threshold value: 194.99119567871094 name: Max Accuracy Threshold - type: max_f1 value: 0.7791495198902607 name: Max F1 - type: max_f1_threshold value: 247.49777221679688 name: Max F1 Threshold - type: max_precision value: 0.7237569060773481 name: Max Precision - type: max_recall value: 0.953416149068323 name: Max Recall - type: max_ap value: 0.8230449963294564 name: Max Ap - task: type: paraphrase-mining name: Paraphrase Mining dataset: name: quora duplicates dev type: quora-duplicates-dev metrics: - type: average_precision value: 0.5888649029434471 name: Average Precision - type: f1 value: 0.5761652140962487 name: F1 - type: precision value: 0.5477552123204396 name: Precision - type: recall value: 0.6076834690513064 name: Recall - type: threshold value: 0.7728720009326935 name: Threshold - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.963 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9906 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9944 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9982 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.963 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.4285333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.27568000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14494 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8299562338609103 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9590366552956846 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9806221849555673 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9925738410935468 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9784033087450696 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9771579365079368 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9709189650394419 name: Cosine Map@100 - type: dot_accuracy@1 value: 0.9514 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9852 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.991 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9968 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.9514 name: Dot Precision@1 - type: dot_precision@3 value: 0.4247333333333334 name: Dot Precision@3 - type: dot_precision@5 value: 0.27364 name: Dot Precision@5 - type: dot_precision@10 value: 0.14458000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.8194380520427287 name: Dot Recall@1 - type: dot_recall@3 value: 0.9520212390452685 name: Dot Recall@3 - type: dot_recall@5 value: 0.9755502441186265 name: Dot Recall@5 - type: dot_recall@10 value: 0.9910547306614953 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9715023430522326 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9692583333333334 name: Dot Mrr@10 - type: dot_map@100 value: 0.961739772177385 name: Dot Map@100 --- # SentenceTransformer based on sentence-transformers/stsb-distilbert-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) on the [mnrl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) and [cl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) 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:** [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Datasets:** - [mnrl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) - [cl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) - **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': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (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("tomaarsen/stsb-distilbert-base-mnrl-cl-multi") # Run inference sentences = [ 'How fast is fast?', 'How does light travel so fast?', 'How do I copyright my books?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Binary Classification * Dataset: `quora-duplicates` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:----------| | cosine_accuracy | 0.846 | | cosine_accuracy_threshold | 0.7969 | | cosine_f1 | 0.7791 | | cosine_f1_threshold | 0.714 | | cosine_precision | 0.6978 | | cosine_recall | 0.882 | | cosine_ap | 0.823 | | dot_accuracy | 0.843 | | dot_accuracy_threshold | 151.2908 | | dot_f1 | 0.7661 | | dot_f1_threshold | 143.7784 | | dot_precision | 0.7238 | | dot_recall | 0.8137 | | dot_ap | 0.7946 | | manhattan_accuracy | 0.838 | | manhattan_accuracy_threshold | 194.9912 | | manhattan_f1 | 0.7704 | | manhattan_f1_threshold | 247.4978 | | manhattan_precision | 0.6537 | | manhattan_recall | 0.9379 | | manhattan_ap | 0.815 | | euclidean_accuracy | 0.841 | | euclidean_accuracy_threshold | 9.0223 | | euclidean_f1 | 0.7704 | | euclidean_f1_threshold | 11.3852 | | euclidean_precision | 0.6463 | | euclidean_recall | 0.9534 | | euclidean_ap | 0.8153 | | max_accuracy | 0.846 | | max_accuracy_threshold | 194.9912 | | max_f1 | 0.7791 | | max_f1_threshold | 247.4978 | | max_precision | 0.7238 | | max_recall | 0.9534 | | **max_ap** | **0.823** | #### Paraphrase Mining * Dataset: `quora-duplicates-dev` * Evaluated with [ParaphraseMiningEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator) | Metric | Value | |:----------------------|:-----------| | **average_precision** | **0.5889** | | f1 | 0.5762 | | precision | 0.5478 | | recall | 0.6077 | | threshold | 0.7729 | #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.963 | | cosine_accuracy@3 | 0.9906 | | cosine_accuracy@5 | 0.9944 | | cosine_accuracy@10 | 0.9982 | | cosine_precision@1 | 0.963 | | cosine_precision@3 | 0.4285 | | cosine_precision@5 | 0.2757 | | cosine_precision@10 | 0.1449 | | cosine_recall@1 | 0.83 | | cosine_recall@3 | 0.959 | | cosine_recall@5 | 0.9806 | | cosine_recall@10 | 0.9926 | | cosine_ndcg@10 | 0.9784 | | cosine_mrr@10 | 0.9772 | | **cosine_map@100** | **0.9709** | | dot_accuracy@1 | 0.9514 | | dot_accuracy@3 | 0.9852 | | dot_accuracy@5 | 0.991 | | dot_accuracy@10 | 0.9968 | | dot_precision@1 | 0.9514 | | dot_precision@3 | 0.4247 | | dot_precision@5 | 0.2736 | | dot_precision@10 | 0.1446 | | dot_recall@1 | 0.8194 | | dot_recall@3 | 0.952 | | dot_recall@5 | 0.9756 | | dot_recall@10 | 0.9911 | | dot_ndcg@10 | 0.9715 | | dot_mrr@10 | 0.9693 | | dot_map@100 | 0.9617 | ## Training Details ### Training Datasets #### mnrl * Dataset: [mnrl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 100,000 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------| | Why in India do we not have one on one political debate as in USA? | Why cant we have a public debate between politicians in India like the one in US? | Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk? | | What is OnePlus One? | How is oneplus one? | Why is OnePlus One so good? | | Does our mind control our emotions? | How do smart and successful people control their emotions? | How can I control my positive emotions for the people whom I love but they don't care about me? | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` #### cl * Dataset: [cl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 100,000 training samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:---------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:---------------| | What is the step by step guide to invest in share market in india? | What is the step by step guide to invest in share market? | 0 | | What is the story of Kohinoor (Koh-i-Noor) Diamond? | What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back? | 0 | | How can I increase the speed of my internet connection while using a VPN? | How can Internet speed be increased by hacking through DNS? | 0 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Evaluation Datasets #### mnrl * Dataset: [mnrl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 1,000 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:---------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Which programming language is best for developing low-end games? | What coding language should I learn first for making games? | I am entering the world of video game programming and want to know what language I should learn? Because there are so many languages ​​I do not know which one to start with. Can you recommend a language that's easy to learn and can be used with many platforms? | | Was it appropriate for Meryl Streep to use her Golden Globes speech to attack Donald Trump? | Should Meryl Streep be using her position to attack the president? | Why did Kelly Ann Conway say that Meryl Streep incited peoples worst feelings? | | Where can I found excellent commercial fridges in Sydney? | Where can I found impressive range of commercial fridges in Sydney? | What is the best grocery delivery service in Sydney? | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` #### cl * Dataset: [cl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 1,000 evaluation samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:--------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|:---------------| | What should I ask my friend to get from UK to India? | What is the process of getting a surgical residency in UK after completing MBBS from India? | 0 | | How can I learn hacking for free? | How can I learn to hack seriously? | 1 | | Which is the best website to learn programming language C++? | Which is the best website to learn C++ Programming language for free? | 0 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: False - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: 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`: None - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `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_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | cl loss | mnrl loss | cosine_map@100 | quora-duplicates-dev_average_precision | quora-duplicates_max_ap | |:------:|:----:|:-------------:|:-------:|:---------:|:--------------:|:--------------------------------------:|:-----------------------:| | 0 | 0 | - | - | - | 0.9245 | 0.4200 | 0.6890 | | 0.0320 | 100 | 0.1634 | - | - | - | - | - | | 0.0640 | 200 | 0.1206 | - | - | - | - | - | | 0.0800 | 250 | - | 0.0190 | 0.1469 | 0.9530 | 0.5068 | 0.7354 | | 0.0960 | 300 | 0.1036 | - | - | - | - | - | | 0.1280 | 400 | 0.0836 | - | - | - | - | - | | 0.1599 | 500 | 0.0918 | 0.0180 | 0.1008 | 0.9553 | 0.5259 | 0.7643 | | 0.1919 | 600 | 0.0784 | - | - | - | - | - | | 0.2239 | 700 | 0.0656 | - | - | - | - | - | | 0.2399 | 750 | - | 0.0177 | 0.0905 | 0.9593 | 0.5305 | 0.7686 | | 0.2559 | 800 | 0.0593 | - | - | - | - | - | | 0.2879 | 900 | 0.0534 | - | - | - | - | - | | 0.3199 | 1000 | 0.0612 | 0.0161 | 0.0736 | 0.9642 | 0.5512 | 0.7881 | | 0.3519 | 1100 | 0.0572 | - | - | - | - | - | | 0.3839 | 1200 | 0.06 | - | - | - | - | - | | 0.3999 | 1250 | - | 0.0158 | 0.0641 | 0.9649 | 0.5567 | 0.7983 | | 0.4159 | 1300 | 0.0565 | - | - | - | - | - | | 0.4479 | 1400 | 0.0565 | - | - | - | - | - | | 0.4798 | 1500 | 0.0475 | 0.0154 | 0.0578 | 0.9645 | 0.5614 | 0.8062 | | 0.5118 | 1600 | 0.0596 | - | - | - | - | - | | 0.5438 | 1700 | 0.0509 | - | - | - | - | - | | 0.5598 | 1750 | - | 0.0150 | 0.0525 | 0.9674 | 0.5762 | 0.8092 | | 0.5758 | 1800 | 0.0403 | - | - | - | - | - | | 0.6078 | 1900 | 0.0431 | - | - | - | - | - | | 0.6398 | 2000 | 0.0481 | 0.0150 | 0.0531 | 0.9689 | 0.5824 | 0.8128 | | 0.6718 | 2100 | 0.05 | - | - | - | - | - | | 0.7038 | 2200 | 0.0468 | - | - | - | - | - | | 0.7198 | 2250 | - | 0.0146 | 0.0486 | 0.9684 | 0.5756 | 0.8195 | | 0.7358 | 2300 | 0.0436 | - | - | - | - | - | | 0.7678 | 2400 | 0.0409 | - | - | - | - | - | | 0.7997 | 2500 | 0.0391 | 0.0145 | 0.0454 | 0.9705 | 0.5822 | 0.8190 | | 0.8317 | 2600 | 0.0412 | - | - | - | - | - | | 0.8637 | 2700 | 0.0373 | - | - | - | - | - | | 0.8797 | 2750 | - | 0.0143 | 0.0451 | 0.9705 | 0.5889 | 0.8229 | | 0.8957 | 2800 | 0.0428 | - | - | - | - | - | | 0.9277 | 2900 | 0.0419 | - | - | - | - | - | | 0.9597 | 3000 | 0.0376 | 0.0143 | 0.0435 | 0.9709 | 0.5889 | 0.8230 | | 0.9917 | 3100 | 0.0366 | - | - | - | - | - | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.084 kWh - **Carbon Emitted**: 0.033 kg of CO2 - **Hours Used**: 0.399 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.0.0.dev0 - Transformers: 4.41.0.dev0 - PyTorch: 2.3.0+cu121 - Accelerate: 0.26.1 - Datasets: 2.18.0 - 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### ContrastiveLoss ```bibtex @inproceedings{hadsell2006dimensionality, author={Hadsell, R. and Chopra, S. and LeCun, Y.}, booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, title={Dimensionality Reduction by Learning an Invariant Mapping}, year={2006}, volume={2}, number={}, pages={1735-1742}, doi={10.1109/CVPR.2006.100} } ```