--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity language: en license: other datasets: - FremyCompany/BioLORD-Dataset widget: - source_sentence: bartonellosis sentences: - cat scratch disease - cat scratch wound - tick-borne orbivirus fever - cat fur --- # FremyCompany/BioLORD-STAMB2-v1 This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. > ## IMPORTANT NOTE: > **This model was introduced in 2022. Since then, a new version has been published.**
> For most use cases, you will be better served by [BioLORD-2023](https://huggingface.co/FremyCompany/BioLORD-2023), our latest generation of BioLORD models. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (MayoSRS). This model is based on [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) and was further finetuned on the [BioLORD-Dataset](https://huggingface.co/datasets/FremyCompany/BioLORD-Dataset). ## General purpose This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model has been finentuned for the biomedical domain. While it preserves a good ability to produce embeddings for general-purpose text, it will be more useful to you if you are trying to process medical documents such as EHR records or clinical notes. Both sentences and phrases can be embedded in the same latent space. ## Citation This model accompanies the [BioLORD: Learning Ontological Representations from Definitions](https://arxiv.org/abs/2210.11892) paper, accepted in the EMNLP 2022 Findings. When you use this model, please cite the original paper as follows: ```latex @inproceedings{remy-etal-2022-biolord, title = "{B}io{LORD}: Learning Ontological Representations from Definitions for Biomedical Concepts and their Textual Descriptions", author = "Remy, François and Demuynck, Kris and Demeester, Thomas", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.findings-emnlp.104", pages = "1454--1465", abstract = "This work introduces BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (MayoSRS).", } ``` You might also want to take a look at our MWE 2023 Paper: - [Detecting Idiomatic Multiword Expressions in Clinical Terminology using Definition-Based Representation Learning](https://www.researchgate.net/publication/370426650_Detecting_Idiomatic_Multiword_Expressions_in_Clinical_Terminology_using_Definition-Based_Representation_Learning) ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["Cat scratch injury", "Cat scratch disease", "Bartonellosis"] model = SentenceTransformer('FremyCompany/BioLORD-STAMB2-v1') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ["Cat scratch injury", "Cat scratch disease", "Bartonellosis"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('FremyCompany/BioLORD-STAMB2-v1') model = AutoModel.from_pretrained('FremyCompany/BioLORD-STAMB2-v1') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ## License My own contributions for this model are covered by the MIT license. However, given the data used to train this model originates from UMLS, you will need to ensure you have proper licensing of UMLS before using this model. UMLS is free of charge in most countries, but you might have to create an account and report on your usage of the data yearly to keep a valid license.