Update README.md (#2)

- Update README.md (058979f6127b341ff455d429cbe9c590be829e44)


Co-authored-by: Louis Brulé Naudet <louisbrulenaudet@users.noreply.huggingface.co>

README.md

@@ -5,14 +5,22 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-
+- doping
+- anti-doping
+pretty_name: Domain-adapted GTE for anti-doping practice
+license: apache-2.0
+language:
+- en
+library_name: sentence-transformers
 ---
 
-# {MODEL_NAME}
+# Domain-adapted GTE for anti-doping practice
 
 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.
 
-<!--- Describe your model here -->
+Pretrained transformers model on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc. Fitted using Transformer-based Sequential Denoising Auto-Encoder for unsupervised sentence embedding learning with one objective : anti-doping domain adaptation.
+
+This way, the model learns an inner representation of the anti-doping language in the training set that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the model as inputs.
 
 ## Usage (Sentence-Transformers)
 
@@ -28,7 +36,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer("timotheeplanes/anti-doping-gte-base")
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -51,8 +59,8 @@ def cls_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained("timotheeplanes/anti-doping-gte-base")
+model = AutoModel.from_pretrained("timotheeplanes/anti-doping-gte-base")
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -68,15 +76,6 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
-
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-
-
 ## Training
 The model was trained with the parameters:
 
@@ -96,7 +95,6 @@ Parameters of the fit()-Method:
 {
     "epochs": 1,
     "evaluation_steps": 0,
-    "evaluator": "NoneType",
     "max_grad_norm": 1,
     "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
     "optimizer_params": {
@@ -120,4 +118,13 @@ SentenceTransformer(
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+If you use this code in your research, please use the following BibTeX entry.
+
+```BibTeX
+@misc{louisbrulenaudet2023,
+  author =       {Brulé Naudet (L.), Planes (T.).},
+  title =        {Domain-adapted GTE for anti-doping practice},
+  year =         {2023}
+  howpublished = {\url{https://huggingface.co/timotheeplanes/anti-doping-gte-base}},
+}
+```