| --- |
| language: |
| - en |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:404290 |
| - loss:OnlineContrastiveLoss |
| base_model: sentence-transformers/stsb-distilbert-base |
| widget: |
| - source_sentence: What does the lock symbol on my iPhone 6 means? |
| sentences: |
| - How did the Soviet Navy compare to the US Navy? |
| - What does the iPhone icon with lock and arrow mean? |
| - What is the importance of electrical engineering? |
| - source_sentence: Why are blue and red neon lights illegal or restricted for commercial |
| uses in Honduras? |
| sentences: |
| - Why are blue and red neon lights illegal or restricted for commercial uses in |
| Colombia? |
| - Why would I want a Raspberry Pi? |
| - How do I see things as they are? |
| - source_sentence: How will Hillary Clinton deal with russia? |
| sentences: |
| - What would have happened if Barty crouch Jr escaped the dementors and made it |
| back to the graveyard? |
| - How will Hillary Clinton deal with terrorism? |
| - I am a commercial student who wishes to study accounting, but now I wish to study |
| law. Is it possible? |
| - source_sentence: What are the best managing skills? |
| sentences: |
| - What are the top skills of effective Product Managers? |
| - How do I lose weight in a short time? |
| - What are some good songs for lyrical dances? |
| - source_sentence: What is the best fact checking sources that all Quorans will most |
| trust? |
| sentences: |
| - Do people still write love letters? |
| - Is working in McKinsey one of the best and surest ways to get into Harvard Business |
| School? |
| - What is the most memorable book that Quorans have read? |
| datasets: |
| - sentence-transformers/quora-duplicates |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy |
| - cosine_accuracy_threshold |
| - cosine_f1 |
| - cosine_f1_threshold |
| - cosine_precision |
| - cosine_recall |
| - cosine_ap |
| - cosine_mcc |
| - 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 |
| 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: 0.869 |
| name: Cosine Accuracy |
| - type: cosine_accuracy_threshold |
| value: 0.813665509223938 |
| name: Cosine Accuracy Threshold |
| - type: cosine_f1 |
| value: 0.8390243902439025 |
| name: Cosine F1 |
| - type: cosine_f1_threshold |
| value: 0.7617226243019104 |
| name: Cosine F1 Threshold |
| - type: cosine_precision |
| value: 0.7818181818181819 |
| name: Cosine Precision |
| - type: cosine_recall |
| value: 0.9052631578947369 |
| name: Cosine Recall |
| - type: cosine_ap |
| value: 0.8852756469769394 |
| name: Cosine Ap |
| - type: cosine_mcc |
| value: 0.7337941850587686 |
| name: Cosine Mcc |
| - task: |
| type: paraphrase-mining |
| name: Paraphrase Mining |
| dataset: |
| name: quora duplicates dev |
| type: quora-duplicates-dev |
| metrics: |
| - type: average_precision |
| value: 0.5427423938771084 |
| name: Average Precision |
| - type: f1 |
| value: 0.5532539228607665 |
| name: F1 |
| - type: precision |
| value: 0.5508021390374331 |
| name: Precision |
| - type: recall |
| value: 0.5557276315132138 |
| name: Recall |
| - type: threshold |
| value: 0.865865558385849 |
| name: Threshold |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: Unknown |
| type: unknown |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.9298 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.9732 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.982 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.9868 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.9298 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.4154 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.26792 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.1417 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.8009069531416296 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.9349178789609083 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.9610774822138647 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.9765400300287947 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.9525570390902354 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.9522342063492065 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.9400294978560327 |
| name: Cosine 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 [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) dataset. 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) <!-- at revision a560fa5fec90547a51a4a41a392d4aef93b49f16 --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) |
| - **Language:** en |
| <!-- - **License:** Unknown --> |
|
|
| ### 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("yahyaabd/stsb-distilbert-base-ocl") |
| # Run inference |
| sentences = [ |
| 'What is the best fact checking sources that all Quorans will most trust?', |
| 'What is the most memorable book that Quorans have read?', |
| 'Is working in McKinsey one of the best and surest ways to get into Harvard Business School?', |
| ] |
| 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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Binary Classification |
|
|
| * Dataset: `quora-duplicates` |
| * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------------|:-----------| |
| | cosine_accuracy | 0.869 | |
| | cosine_accuracy_threshold | 0.8137 | |
| | cosine_f1 | 0.839 | |
| | cosine_f1_threshold | 0.7617 | |
| | cosine_precision | 0.7818 | |
| | cosine_recall | 0.9053 | |
| | **cosine_ap** | **0.8853** | |
| | cosine_mcc | 0.7338 | |
| |
| #### Paraphrase Mining |
| |
| * Dataset: `quora-duplicates-dev` |
| * Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator) |
| |
| | Metric | Value | |
| |:----------------------|:-----------| |
| | **average_precision** | **0.5427** | |
| | f1 | 0.5533 | |
| | precision | 0.5508 | |
| | recall | 0.5557 | |
| | threshold | 0.8659 | |
| |
| #### Information Retrieval |
| |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
| |
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.9298 | |
| | cosine_accuracy@3 | 0.9732 | |
| | cosine_accuracy@5 | 0.982 | |
| | cosine_accuracy@10 | 0.9868 | |
| | cosine_precision@1 | 0.9298 | |
| | cosine_precision@3 | 0.4154 | |
| | cosine_precision@5 | 0.2679 | |
| | cosine_precision@10 | 0.1417 | |
| | cosine_recall@1 | 0.8009 | |
| | cosine_recall@3 | 0.9349 | |
| | cosine_recall@5 | 0.9611 | |
| | cosine_recall@10 | 0.9765 | |
| | **cosine_ndcg@10** | **0.9526** | |
| | cosine_mrr@10 | 0.9522 | |
| | cosine_map@100 | 0.94 | |
| |
| <!-- |
| ## 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.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### quora-duplicates |
| |
| * Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) |
| * Size: 404,290 training samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | label | |
| |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 16.01 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.9 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~64.40%</li><li>1: ~35.60%</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | label | |
| |:----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>How much worse do things need to get before the "blue" states cut off welfare to the "red" states?</code> | <code>If the red states and the blue states were separated into two countries, which country would be more successful?</code> | <code>0</code> | |
| | <code>Can you offer me any advice on how to lose weight?</code> | <code>What are the best ways to lose weight? What is the best diet plan?</code> | <code>1</code> | |
| | <code>How do I break my knee?</code> | <code>How do I break my elbow?</code> | <code>0</code> | |
| * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
| |
| ### Evaluation Dataset |
| |
| #### quora-duplicates |
| |
| * Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) |
| * Size: 404,290 evaluation samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | label | |
| |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 15.98 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.9 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>0: ~62.00%</li><li>1: ~38.00%</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | label | |
| |:---------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:---------------| |
| | <code>Which is the best SAP online training centre at Hyderabad?</code> | <code>Which is the best sap workflow online training institute in Hyderabad?</code> | <code>1</code> | |
| | <code>How did World War Two start?</code> | <code>What will most likely cause World War III?</code> | <code>0</code> | |
| | <code>How do I find a unique string from a given string in Java without methods such as split, contain, and divide?</code> | <code>How can I split the string "[] {() <>} []" into " [,], {, (, ..." in Java?</code> | <code>0</code> | |
| * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
| |
| ### 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 |
| <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`: 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 |
| - `torch_empty_cache_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 |
| - `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`: False |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_private_repo`: None |
| - `hub_always_push`: False |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_inputs_for_metrics`: False |
| - `include_for_metrics`: [] |
| - `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 |
| - `use_liger_kernel`: False |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: False |
| - `prompts`: None |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | quora-duplicates_cosine_ap | quora-duplicates-dev_average_precision | cosine_ndcg@10 | |
| |:------:|:----:|:-------------:|:---------------:|:--------------------------:|:--------------------------------------:|:--------------:| |
| | 0 | 0 | - | - | 0.7402 | 0.4200 | 0.9413 | |
| | 0.0640 | 100 | 2.481 | - | - | - | - | |
| | 0.1280 | 200 | 2.1466 | - | - | - | - | |
| | 0.1599 | 250 | - | 1.7997 | 0.8327 | 0.4596 | 0.9355 | |
| | 0.1919 | 300 | 2.0354 | - | - | - | - | |
| | 0.2559 | 400 | 1.9342 | - | - | - | - | |
| | 0.3199 | 500 | 1.9132 | 1.6231 | 0.8617 | 0.4896 | 0.9425 | |
| | 0.3839 | 600 | 1.8015 | - | - | - | - | |
| | 0.4479 | 700 | 1.7407 | - | - | - | - | |
| | 0.4798 | 750 | - | 1.4953 | 0.8737 | 0.5112 | 0.9468 | |
| | 0.5118 | 800 | 1.6454 | - | - | - | - | |
| | 0.5758 | 900 | 1.6568 | - | - | - | - | |
| | 0.6398 | 1000 | 1.6811 | 1.4678 | 0.8751 | 0.5290 | 0.9457 | |
| | 0.7038 | 1100 | 1.711 | - | - | - | - | |
| | 0.7678 | 1200 | 1.6449 | - | - | - | - | |
| | 0.7997 | 1250 | - | 1.4363 | 0.8811 | 0.5327 | 0.9507 | |
| | 0.8317 | 1300 | 1.5921 | - | - | - | - | |
| | 0.8957 | 1400 | 1.5062 | - | - | - | - | |
| | 0.9597 | 1500 | 1.5728 | 1.4029 | 0.8853 | 0.5427 | 0.9526 | |
| |
| |
| ### Framework Versions |
| - Python: 3.10.12 |
| - Sentence Transformers: 3.4.0 |
| - Transformers: 4.48.1 |
| - PyTorch: 2.5.1+cu124 |
| - Accelerate: 1.3.0 |
| - Datasets: 3.2.0 |
| - Tokenizers: 0.21.0 |
| |
| ## 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", |
| } |
| ``` |
| |
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