--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:4096000 - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-small-en-v1.5 widget: - source_sentence: vascular disorders of the stomach sentences: - vascular disorders of the stomach, unspecified - ringworm of the hand - anogenital herpes - source_sentence: neonatal malabsorption secondary to defect in carbohydrate metabolism sentences: - overfeeding problem of newborn - other specified neonatal malabsorption syndromes - bronchial asthma - source_sentence: white matter disorder due to carbon monoxide toxicity sentences: - white matter disorder due to nitrous oxide toxicity - neonatal dehydration - moerman lethal chondrodysplasia - source_sentence: chronic myelogenous leukaemia, bcr-abl1-positive in complete remission sentences: - chronic myeloid leukaemia, bcr-abl-positive in complete remission - lesion of sciatic nerve - tooth injury - source_sentence: metabolic or transporter liver disease, unspecified sentences: - metabolic or transporter liver disease - staphyloma of cornea - spontaneous rupture of synovium or tendon, unspecified pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - 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@10 model-index: - name: SentenceTransformer based on BAAI/bge-small-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: icd11 val type: icd11-val metrics: - type: cosine_accuracy@1 value: 0.7478448275862069 name: Cosine Accuracy@1 - type: cosine_accuracy@5 value: 0.8717672413793104 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.896551724137931 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7478448275862069 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.6253591954022988 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.5258620689655173 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.3786637931034483 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1588556265383415 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.37728305808648516 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4796248471294223 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6031417673743621 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6649315033350626 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8006516830870286 name: Cosine Mrr@10 - type: cosine_map@10 value: 0.5745241229374094 name: Cosine Map@10 --- # SentenceTransformer based on BAAI/bge-small-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) - **Maximum Sequence Length:** 30 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Supported Modality:** Text ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'}) (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'cls', 'include_prompt': True}) (2): Normalize({}) ) ``` ## 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("seanfarrell/icd11_ner_model_small") # Run inference queries = [ 'metabolic or transporter liver disease, unspecified', ] documents = [ 'metabolic or transporter liver disease', 'spontaneous rupture of synovium or tendon, unspecified', 'staphyloma of cornea', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 384] [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[ 0.9840, -0.1296, -0.1292]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `icd11-val` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7478 | | cosine_accuracy@5 | 0.8718 | | cosine_accuracy@10 | 0.8966 | | cosine_precision@1 | 0.7478 | | cosine_precision@3 | 0.6254 | | cosine_precision@5 | 0.5259 | | cosine_precision@10 | 0.3787 | | cosine_recall@1 | 0.1589 | | cosine_recall@3 | 0.3773 | | cosine_recall@5 | 0.4796 | | cosine_recall@10 | 0.6031 | | **cosine_ndcg@10** | **0.6649** | | cosine_mrr@10 | 0.8007 | | cosine_map@10 | 0.5745 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 4,096,000 training samples * Columns: anchor and positive * Approximate statistics based on the first 100 samples: | | anchor | positive | |:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | modality | text | text | | details | | | * Samples: | anchor | positive | |:-------------------------------------------------------------|:-----------------------------------------------------------------| | duodenal adenoma in familial polyposis | benign duodenal neoplasm | | congenital unilateral hip dislocation | developmental dysplasia of hip, unilateral | | postprocedural pulmonary arterial tree disorder | postprocedural pulmonary arterial tree complication | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false, "directions": [ "query_to_doc" ], "partition_mode": "joint", "hardness_mode": null, "hardness_strength": 0.0 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 512 - `num_train_epochs`: 1 - `learning_rate`: 2e-05 - `lr_scheduler_type`: cosine - `warmup_steps`: 800 - `weight_decay`: 0.01 - `bf16`: True - `per_device_eval_batch_size`: 256 - `eval_on_start`: True - `load_best_model_at_end`: True - `dataloader_drop_last`: True #### All Hyperparameters
Click to expand - `per_device_train_batch_size`: 512 - `num_train_epochs`: 1 - `max_steps`: -1 - `learning_rate`: 2e-05 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: None - `warmup_steps`: 800 - `optim`: adamw_torch_fused - `optim_args`: None - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `optim_target_modules`: None - `gradient_accumulation_steps`: 1 - `average_tokens_across_devices`: True - `max_grad_norm`: 1.0 - `label_smoothing_factor`: 0.0 - `bf16`: True - `fp16`: False - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `use_liger_kernel`: False - `liger_kernel_config`: None - `use_cache`: False - `neftune_noise_alpha`: None - `torch_empty_cache_steps`: None - `auto_find_batch_size`: False - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `include_num_input_tokens_seen`: no - `log_level`: passive - `log_level_replica`: warning - `disable_tqdm`: False - `project`: huggingface - `trackio_space_id`: None - `trackio_bucket_id`: None - `trackio_static_space_id`: None - `per_device_eval_batch_size`: 256 - `prediction_loss_only`: True - `eval_on_start`: True - `eval_do_concat_batches`: True - `eval_use_gather_object`: False - `eval_accumulation_steps`: None - `include_for_metrics`: [] - `batch_eval_metrics`: False - `save_only_model`: False - `save_on_each_node`: False - `enable_jit_checkpoint`: False - `push_to_hub`: False - `hub_private_repo`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_always_push`: False - `hub_revision`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `restore_callback_states_from_checkpoint`: False - `full_determinism`: False - `seed`: 42 - `data_seed`: None - `use_cpu`: False - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `parallelism_config`: None - `dataloader_drop_last`: True - `dataloader_num_workers`: 0 - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `dataloader_prefetch_factor`: None - `dataloader_multiprocessing_context`: None - `dataloader_in_order`: True - `remove_unused_columns`: True - `label_names`: None - `train_sampling_strategy`: random - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `ddp_static_graph`: None - `ddp_backend`: None - `ddp_timeout`: 1800 - `fsdp`: None - `fsdp_config`: None - `deepspeed`: None - `debug`: [] - `skip_memory_metrics`: True - `do_predict`: False - `resume_from_checkpoint`: None - `local_rank`: -1 - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} - `warmup_ratio`: None
### Training Logs
Click to expand | Epoch | Step | Training Loss | icd11-val_cosine_ndcg@10 | |:----------:|:--------:|:-------------:|:------------------------:| | 0 | 0 | - | 0.4871 | | 0.0063 | 50 | 2.5057 | - | | 0.0125 | 100 | 2.1921 | - | | 0.0187 | 150 | 1.6707 | - | | 0.025 | 200 | 1.4270 | - | | 0.0312 | 250 | 1.3379 | - | | 0.0375 | 300 | 1.2811 | - | | 0.0437 | 350 | 1.2222 | - | | 0.05 | 400 | 1.1848 | - | | 0.0563 | 450 | 1.1484 | - | | 0.0625 | 500 | 1.1260 | 0.5411 | | 0.0688 | 550 | 1.0856 | - | | 0.075 | 600 | 1.0654 | - | | 0.0813 | 650 | 1.0247 | - | | 0.0875 | 700 | 1.0050 | - | | 0.0938 | 750 | 0.9669 | - | | 0.1 | 800 | 0.9515 | - | | 0.1062 | 850 | 0.9404 | - | | 0.1125 | 900 | 0.8906 | - | | 0.1187 | 950 | 0.8692 | - | | 0.125 | 1000 | 0.8413 | 0.5746 | | 0.1313 | 1050 | 0.8439 | - | | 0.1375 | 1100 | 0.8270 | - | | 0.1437 | 1150 | 0.8128 | - | | 0.15 | 1200 | 0.7762 | - | | 0.1562 | 1250 | 0.7666 | - | | 0.1625 | 1300 | 0.7555 | - | | 0.1688 | 1350 | 0.7562 | - | | 0.175 | 1400 | 0.7180 | - | | 0.1812 | 1450 | 0.6945 | - | | 0.1875 | 1500 | 0.7032 | 0.6002 | | 0.1938 | 1550 | 0.6747 | - | | 0.2 | 1600 | 0.6729 | - | | 0.2062 | 1650 | 0.6604 | - | | 0.2125 | 1700 | 0.6644 | - | | 0.2188 | 1750 | 0.6556 | - | | 0.225 | 1800 | 0.6440 | - | | 0.2313 | 1850 | 0.6284 | - | | 0.2375 | 1900 | 0.6105 | - | | 0.2437 | 1950 | 0.5935 | - | | 0.25 | 2000 | 0.5923 | 0.6120 | | 0.2562 | 2050 | 0.5850 | - | | 0.2625 | 2100 | 0.5787 | - | | 0.2687 | 2150 | 0.5764 | - | | 0.275 | 2200 | 0.5643 | - | | 0.2812 | 2250 | 0.5655 | - | | 0.2875 | 2300 | 0.5463 | - | | 0.2938 | 2350 | 0.5414 | - | | 0.3 | 2400 | 0.5368 | - | | 0.3063 | 2450 | 0.5236 | - | | 0.3125 | 2500 | 0.5109 | 0.6230 | | 0.3187 | 2550 | 0.5308 | - | | 0.325 | 2600 | 0.5000 | - | | 0.3312 | 2650 | 0.5069 | - | | 0.3375 | 2700 | 0.5120 | - | | 0.3438 | 2750 | 0.4853 | - | | 0.35 | 2800 | 0.4877 | - | | 0.3563 | 2850 | 0.4786 | - | | 0.3625 | 2900 | 0.4869 | - | | 0.3688 | 2950 | 0.4865 | - | | 0.375 | 3000 | 0.4561 | 0.6332 | | 0.3812 | 3050 | 0.4640 | - | | 0.3875 | 3100 | 0.4629 | - | | 0.3937 | 3150 | 0.4575 | - | | 0.4 | 3200 | 0.4493 | - | | 0.4062 | 3250 | 0.4503 | - | | 0.4125 | 3300 | 0.4301 | - | | 0.4188 | 3350 | 0.4410 | - | | 0.425 | 3400 | 0.4415 | - | | 0.4313 | 3450 | 0.4316 | - | | 0.4375 | 3500 | 0.4359 | 0.6441 | | 0.4437 | 3550 | 0.4147 | - | | 0.45 | 3600 | 0.4028 | - | | 0.4562 | 3650 | 0.4002 | - | | 0.4625 | 3700 | 0.4132 | - | | 0.4688 | 3750 | 0.4021 | - | | 0.475 | 3800 | 0.4058 | - | | 0.4813 | 3850 | 0.4023 | - | | 0.4875 | 3900 | 0.4038 | - | | 0.4938 | 3950 | 0.4007 | - | | 0.5 | 4000 | 0.3894 | 0.6501 | | 0.5062 | 4050 | 0.3856 | - | | 0.5125 | 4100 | 0.3879 | - | | 0.5188 | 4150 | 0.3758 | - | | 0.525 | 4200 | 0.3796 | - | | 0.5312 | 4250 | 0.3890 | - | | 0.5375 | 4300 | 0.3831 | - | | 0.5437 | 4350 | 0.3806 | - | | 0.55 | 4400 | 0.3781 | - | | 0.5563 | 4450 | 0.3761 | - | | 0.5625 | 4500 | 0.3723 | 0.6551 | | 0.5687 | 4550 | 0.3603 | - | | 0.575 | 4600 | 0.3595 | - | | 0.5813 | 4650 | 0.3524 | - | | 0.5875 | 4700 | 0.3695 | - | | 0.5938 | 4750 | 0.3552 | - | | 0.6 | 4800 | 0.3521 | - | | 0.6062 | 4850 | 0.3459 | - | | 0.6125 | 4900 | 0.3472 | - | | 0.6188 | 4950 | 0.3511 | - | | 0.625 | 5000 | 0.3537 | 0.6589 | | 0.6312 | 5050 | 0.3490 | - | | 0.6375 | 5100 | 0.3428 | - | | 0.6438 | 5150 | 0.3492 | - | | 0.65 | 5200 | 0.3471 | - | | 0.6562 | 5250 | 0.3348 | - | | 0.6625 | 5300 | 0.3404 | - | | 0.6687 | 5350 | 0.3442 | - | | 0.675 | 5400 | 0.3494 | - | | 0.6813 | 5450 | 0.3306 | - | | 0.6875 | 5500 | 0.3349 | 0.6598 | | 0.6937 | 5550 | 0.3401 | - | | 0.7 | 5600 | 0.3374 | - | | 0.7063 | 5650 | 0.3306 | - | | 0.7125 | 5700 | 0.3277 | - | | 0.7188 | 5750 | 0.3274 | - | | 0.725 | 5800 | 0.3318 | - | | 0.7312 | 5850 | 0.3316 | - | | 0.7375 | 5900 | 0.3273 | - | | 0.7438 | 5950 | 0.3252 | - | | 0.75 | 6000 | 0.3306 | 0.6636 | | 0.7562 | 6050 | 0.3347 | - | | 0.7625 | 6100 | 0.3186 | - | | 0.7688 | 6150 | 0.3301 | - | | 0.775 | 6200 | 0.3203 | - | | 0.7812 | 6250 | 0.3234 | - | | 0.7875 | 6300 | 0.3134 | - | | 0.7937 | 6350 | 0.3175 | - | | 0.8 | 6400 | 0.3312 | - | | 0.8063 | 6450 | 0.3302 | - | | 0.8125 | 6500 | 0.3189 | 0.6644 | | 0.8187 | 6550 | 0.3191 | - | | 0.825 | 6600 | 0.3265 | - | | 0.8313 | 6650 | 0.3192 | - | | 0.8375 | 6700 | 0.3111 | - | | 0.8438 | 6750 | 0.3124 | - | | 0.85 | 6800 | 0.3101 | - | | 0.8562 | 6850 | 0.3188 | - | | 0.8625 | 6900 | 0.3180 | - | | 0.8688 | 6950 | 0.3153 | - | | 0.875 | 7000 | 0.3159 | 0.6646 | | 0.8812 | 7050 | 0.3132 | - | | 0.8875 | 7100 | 0.3168 | - | | 0.8938 | 7150 | 0.3227 | - | | 0.9 | 7200 | 0.3121 | - | | 0.9062 | 7250 | 0.3212 | - | | 0.9125 | 7300 | 0.3195 | - | | 0.9187 | 7350 | 0.3177 | - | | 0.925 | 7400 | 0.3228 | - | | 0.9313 | 7450 | 0.3172 | - | | **0.9375** | **7500** | **0.3045** | **0.6652** | | 0.9437 | 7550 | 0.3052 | - | | 0.95 | 7600 | 0.3273 | - | | 0.9563 | 7650 | 0.3163 | - | | 0.9625 | 7700 | 0.3138 | - | | 0.9688 | 7750 | 0.3280 | - | | 0.975 | 7800 | 0.3074 | - | | 0.9812 | 7850 | 0.3266 | - | | 0.9875 | 7900 | 0.3214 | - | | 0.9938 | 7950 | 0.3240 | - | | 1.0 | 8000 | 0.3141 | 0.6649 | * The bold row denotes the saved checkpoint.
### Training Time - **Training**: 8.9 minutes - **Evaluation**: 45.4 seconds - **Total**: 9.7 minutes ### Framework Versions - Python: 3.13.15 - Sentence Transformers: 5.7.0 - Transformers: 5.18.0 - PyTorch: 2.11.0+cu130 - Accelerate: 1.15.0 - Datasets: 4.8.5 - Tokenizers: 0.23.2 ## Additional Resources - [Training and Finetuning Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sentence-transformers): the end-to-end guide for training or finetuning Sentence Transformer models. - [Introduction to Matryoshka Embedding Models](https://huggingface.co/blog/matryoshka): variable-size embeddings that can be truncated with minimal quality loss. - [Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval](https://huggingface.co/blog/embedding-quantization): post-training compression of embedding vectors. - [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video models through the same API. - [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): train multimodal embedding models, with a Visual Document Retrieval walkthrough. ## 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{oord2019representationlearningcontrastivepredictive, title={Representation Learning with Contrastive Predictive Coding}, author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, year={2019}, eprint={1807.03748}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/1807.03748}, } ```