readme update

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -8,7 +8,7 @@ tags:
 
 ---
 
-# {MODEL_NAME}
+# Domain-adapted BERT for General Legal 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.
 
@@ -28,7 +28,7 @@ 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("louisbrulenaudet/tsdae-lemone-mbert-base")
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -51,32 +51,23 @@ 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-base")
+model = AutoModel.from_pretrained("louisbrulenaudet/tsdae-lemone-mbert-base")
 
 # 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 +87,6 @@ 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 +110,16 @@ 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 =        {Tranformer-based Denoising AutoEncoder for tax practice},
+  year =         {2023}
+}
+```
+
+## Feedback
+
+If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).