Update README.md (#2)

- Update README.md (976d730da7ecfab1af0f99a1c4af5e95ab620b61)


Co-authored-by: Louis Brulé Naudet <[email protected]>

README.md

@@ -18,7 +18,10 @@ library_name: sentence-transformers
 
 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 with the largest Wikipedia using a masked language modeling (MLM) objective, 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)
 
@@ -34,13 +37,12 @@ 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-bert-base")
 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.
 
@@ -75,14 +77,6 @@ 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:
 
@@ -102,7 +96,6 @@ 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": {
@@ -126,4 +119,13 @@ 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 BERT for anti-doping practice},
+  year =         {2023}
+  howpublished = {\url{https://huggingface.co/timotheeplanes/anti-doping-bert-base}},
+}
+```