Update README.md

README.md

@@ -5,14 +5,31 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-
+- legal
+- french-law
+- droit français
+- tax
+- droit fiscal
+- fiscalité
+license: apache-2.0
+pretty_name: Domain-adapted mBERT for French Tax Practice
+datasets:
+- louisbrulenaudet/lpf
+- louisbrulenaudet/cgi
+- louisbrulenaudet/code-douanes
+
+language:
+- fr
+library_name: sentence-transformers
 ---
 
-# {MODEL_NAME}
+# Domain-adapted mBERT for French Tax 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 the top 102 languages 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 : french tax domain adaptation.
+
+This way, the model learns an inner representation of the french legal 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)
 
@@ -28,13 +45,11 @@ 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("louisbrulenaudet/tsdae-lemone-mbert-tax")
 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.
 
@@ -51,32 +66,23 @@ 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("louisbrulenaudet/tsdae-lemone-mbert-tax")
+model = AutoModel.from_pretrained("louisbrulenaudet/tsdae-lemone-mbert-tax")
 
 # Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+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. In this case, cls pooling.
-sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
+sentence_embeddings = cls_pooling(model_output, encoded_input["attention_mask"])
 
 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 +102,6 @@ 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": {
@@ -109,7 +114,6 @@ Parameters of the fit()-Method:
 }
 ```
 
-
 ## Full Model Architecture
 ```
 SentenceTransformer(
@@ -120,4 +124,17 @@ 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 =       {Louis Brulé Naudet},
+  title =        {Domain-adapted mBERT for French Tax Practice},
+  year =         {2023}
+  howpublished = {\url{https://huggingface.co/louisbrulenaudet/tsdae-lemone-mbert-tax}},
+}
+```
+
+## Feedback
+
+If you have any feedback, please reach out at [[email protected]](mailto:[email protected]).