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  1. README.md +15 -13
README.md CHANGED
@@ -18,7 +18,10 @@ library_name: sentence-transformers
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  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.
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- <!--- Describe your model here -->
 
 
 
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  ## Usage (Sentence-Transformers)
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@@ -34,13 +37,12 @@ Then you can use the model like this:
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  from sentence_transformers import SentenceTransformer
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  sentences = ["This is an example sentence", "Each sentence is converted"]
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- model = SentenceTransformer('{MODEL_NAME}')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
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-
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  ## Usage (HuggingFace Transformers)
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  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.
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  ```
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-
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- ## Evaluation Results
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-
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- <!--- Describe how your model was evaluated -->
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-
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- For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
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-
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-
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  ## Training
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  The model was trained with the parameters:
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  {
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  "epochs": 1,
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  "evaluation_steps": 0,
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- "evaluator": "NoneType",
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  "max_grad_norm": 1,
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  "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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  "optimizer_params": {
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  ## Citing & Authors
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- <!--- Describe where people can find more information -->
 
 
 
 
 
 
 
 
 
 
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  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.
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+ 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.
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+
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+ 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.
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+
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  ## Usage (Sentence-Transformers)
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  from sentence_transformers import SentenceTransformer
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  sentences = ["This is an example sentence", "Each sentence is converted"]
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+ model = SentenceTransformer("timotheeplanes/anti-doping-bert-base")
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
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  ## Usage (HuggingFace Transformers)
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  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.
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  ```
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  ## Training
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  The model was trained with the parameters:
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  {
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  "epochs": 1,
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  "evaluation_steps": 0,
 
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  "max_grad_norm": 1,
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  "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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  "optimizer_params": {
 
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  ## Citing & Authors
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+ If you use this code in your research, please use the following BibTeX entry.
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+
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+ ```BibTeX
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+ @misc{louisbrulenaudet2023,
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+ author = {Brulé Naudet (L.), Planes (T.).},
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+ title = {Domain-adapted BERT for anti-doping practice},
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+ year = {2023}
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+ howpublished = {\url{https://huggingface.co/timotheeplanes/anti-doping-bert-base}},
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+ }
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+ ```