Update README.md

README.md

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 ---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+language: en
 license: other
+datasets:
+- FremyCompany/BioLORD-Dataset
 ---
+
+# 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. 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).
+<img width="640" src="https://s3.amazonaws.com/moonup/production/uploads/1665568401241-5f04e8865d08220171a0ad3f.png" />
+
+
+## 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.
+
+## Citation
+This dataset accompanies the [BioLORD: Learning Ontological Representations from Definitions](https://TODO/) paper, accepted in the EMNLP 2022 Findings. When you use this dataset, please cite the original paper as follows:
+
+```latex
+TODO
+```
+
+## 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)
+```