| | --- |
| | pipeline_tag: sentence-similarity |
| | tags: |
| | - sentence-transformers |
| | - feature-extraction |
| | - sentence-similarity |
| | - transformers |
| | base_model: thomas-sounack/BioClinical-ModernBERT-base |
| | language: en |
| | license: apache-2.0 |
| | --- |
| | |
| | # BioClinical ModernBERT Embeddings |
| |
|
| | This is a [BioClinical ModernBERT](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-base) model fined-tuned using [sentence-transformers](https://www.SBERT.net). It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The training dataset was generated using a random sample of [PubMed](https://pubmed.ncbi.nlm.nih.gov/) title-abstract pairs along with similar title pairs. |
| |
|
| | Given that this is a [ModernBERT](https://huggingface.co/answerdotai/ModernBERT-base) model, it supports a longer context length of up to `8,192` tokens vs the `512` tokens supported by standard BERT models. |
| |
|
| | ## Usage (txtai) |
| |
|
| | This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG). |
| |
|
| | ```python |
| | import txtai |
| | |
| | embeddings = txtai.Embeddings(path="neuml/bioclinical-modernbert-base-embeddings", content=True) |
| | embeddings.index(documents()) |
| | |
| | # Run a query |
| | embeddings.search("query to run") |
| | ``` |
| |
|
| | ## Usage (Sentence-Transformers) |
| |
|
| | Alternatively, the model can be loaded with [sentence-transformers](https://www.SBERT.net). |
| |
|
| | ```python |
| | from sentence_transformers import SentenceTransformer |
| | sentences = ["This is an example sentence", "Each sentence is converted"] |
| | |
| | model = SentenceTransformer("neuml/bioclinical-modernbert-base-embeddings") |
| | embeddings = model.encode(sentences) |
| | print(embeddings) |
| | ``` |
| |
|
| | ## Usage (Hugging Face Transformers) |
| |
|
| | The model can also be used directly with Transformers. |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModel |
| | import torch |
| | |
| | # Mean Pooling - Take attention mask into account for correct averaging |
| | def meanpooling(output, mask): |
| | embeddings = output[0] # First element of model_output contains all token embeddings |
| | mask = mask.unsqueeze(-1).expand(embeddings.size()).float() |
| | return torch.sum(embeddings * mask, 1) / torch.clamp(mask.sum(1), min=1e-9) |
| | |
| | # Sentences we want sentence embeddings for |
| | sentences = ['This is an example sentence', 'Each sentence is converted'] |
| | |
| | # Load model from HuggingFace Hub |
| | tokenizer = AutoTokenizer.from_pretrained("neuml/bioclinical-modernbert-base-embeddings") |
| | model = AutoModel.from_pretrained("neuml/bioclinical-modernbert-base-embeddings") |
| | |
| | # Tokenize sentences |
| | inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
| | |
| | # Compute token embeddings |
| | with torch.no_grad(): |
| | output = model(**inputs) |
| | |
| | # Perform pooling. In this case, mean pooling. |
| | embeddings = meanpooling(output, inputs['attention_mask']) |
| | |
| | print("Sentence embeddings:") |
| | print(embeddings) |
| | ``` |
| |
|
| | ## Evaluation Results |
| |
|
| | Performance of this model compared to the top base models on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) is shown below. A popular smaller model was also evaluated along with the most downloaded PubMed similarity model on the Hugging Face Hub. |
| |
|
| | The following datasets were used to evaluate model performance. |
| |
|
| | - [PubMed QA](https://huggingface.co/datasets/qiaojin/PubMedQA) |
| | - Subset: pqa_labeled, Split: train, Pair: (question, long_answer) |
| | - [PubMed Subset](https://huggingface.co/datasets/awinml/pubmed_abstract_3_1k) |
| | - Split: test, Pair: (title, text) |
| | - [PubMed Summary](https://huggingface.co/datasets/armanc/scientific_papers) |
| | - Subset: pubmed, Split: validation, Pair: (article, abstract) |
| |
|
| | Evaluation results are shown below. The [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) is used as the evaluation metric. |
| |
|
| | | Model | PubMed QA | PubMed Subset | PubMed Summary | Average | |
| | | ----------------------------------------------------------------------------- | --------- | ------------- | -------------- | --------- | |
| | | [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2) | 90.40 | 95.92 | 94.07 | 93.46 | |
| | | [**bioclinical-modernbert-base-embeddings**](https://hf.co/neuml/bioclinical-modernbert-base-embeddings) | **92.49** | **97.10** | **97.04** | **95.54** | |
| | | [bge-base-en-v1.5](https://hf.co/BAAI/bge-base-en-v1.5) | 91.02 | 95.82 | 94.49 | 93.78 | |
| | | [gte-base](https://hf.co/thenlper/gte-base) | 92.97 | 96.90 | 96.24 | 95.37 | |
| | | [pubmedbert-base-embeddings](https://hf.co/neuml/pubmedbert-base-embeddings) | 93.27 | 97.00 | 96.58 | 95.62 | |
| | | [S-PubMedBert-MS-MARCO](https://hf.co/pritamdeka/S-PubMedBert-MS-MARCO) | 90.86 | 93.68 | 93.54 | 92.69 | |
| |
|
| | Note that while this model scores slightly lower than the PubMedBERT embeddings model, it supports a longer context of `8,192` tokens vs `512`. |
| |
|
| | ## Training |
| |
|
| | The model was trained with the parameters: |
| |
|
| | **DataLoader**: |
| |
|
| | ``` |
| | {'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} |
| | ``` |
| |
|
| | **Loss**: |
| |
|
| | `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: |
| | ``` |
| | {'scale': 20.0, 'similarity_fct': 'cos_sim'} |
| | ``` |
| |
|
| | Parameters of the fit() method: |
| | ``` |
| | { |
| | "epochs": 1, |
| | "evaluation_steps": 2000, |
| | "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", |
| | "max_grad_norm": 1, |
| | "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", |
| | "optimizer_params": { |
| | "lr": 2e-05 |
| | }, |
| | "scheduler": "WarmupLinear", |
| | "steps_per_epoch": null, |
| | "warmup_steps": 10000, |
| | "weight_decay": 0.01 |
| | } |
| | ``` |
| |
|
| | ### Full Model Architecture |
| |
|
| | ``` |
| | SentenceTransformer( |
| | (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel |
| | (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}) |
| | ) |
| | ``` |
| |
|
| | ## More Information |
| |
|
| | This model was trained using the same method described in [this article](https://medium.com/neuml/embeddings-for-medical-literature-74dae6abf5e0). |
| |
|
