--- 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) - **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 ### 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] ``` ## Evaluation ### Metrics #### Binary Classification * Dataset: `quora-duplicates` * Evaluated with [BinaryClassificationEvaluator](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 [ParaphraseMiningEvaluator](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 [InformationRetrievalEvaluator](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 | ## 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: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|:---------------| | How much worse do things need to get before the "blue" states cut off welfare to the "red" states? | If the red states and the blue states were separated into two countries, which country would be more successful? | 0 | | Can you offer me any advice on how to lose weight? | What are the best ways to lose weight? What is the best diet plan? | 1 | | How do I break my knee? | How do I break my elbow? | 0 | * Loss: [OnlineContrastiveLoss](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: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:---------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:---------------| | Which is the best SAP online training centre at Hyderabad? | Which is the best sap workflow online training institute in Hyderabad? | 1 | | How did World War Two start? | What will most likely cause World War III? | 0 | | How do I find a unique string from a given string in Java without methods such as split, contain, and divide? | How can I split the string "[] {() <>} []" into " [,], {, (, ..." in Java? | 0 | * Loss: [OnlineContrastiveLoss](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
Click to expand - `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
### 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", } ```