Update README.md

README.md

@@ -1,13 +1,18 @@
 ---
+language: 
+  - pt
+thumbnail: "Portugues SBERT for the Legal Domain"
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
-- feature-extraction
 - sentence-similarity
 - transformers
+datasets:
+- assin
+- assin2
 ---
 
-# {MODEL_NAME}
+# rufimelo/Legal-SBERTimbau-large
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -27,7 +32,7 @@ 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('rufimelo/Legal-SBERTimbau-large')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -53,8 +58,8 @@ def mean_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('rufimelo/Legal-SBERTimbau-large')
+model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-large}')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -71,52 +76,30 @@ print(sentence_embeddings)
 ```
 
 
+## Evaluation Results STS
 
-## 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})
+| Model| Dataset | PearsonCorrelation        | 
+| ---------------------------------------- | ---------- |
+| Legal-SBERTimbau-large| Assin | 0.766293861 |
+| Legal-SBERTimbau-large| Assin2| 0.823565322 |
+| ---------------------------------------- | ---------- |
+| paraphrase-multilingual-mpnet-base-v2| Assin | 0.743740222 |
+| paraphrase-multilingual-mpnet-base-v2| Assin2| 0.823565322 |
+| paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)| Assin | 0.77641  |
+| paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)| Assin2| 0.79831 |
 
 
 ## Training
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 204 with parameters:
-```
-{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 1,
-    "evaluation_steps": 0,
-    "evaluator": "NoneType",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 20.400000000000002,
-    "weight_decay": 0.01
-}
-```
 
+Legal-SBERTimbau-large is based on Legal-BERTimbau-large whioch derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large.
+It was trained using the multilingual knowledge distillation process, meaning it was trained as a multilingual model. This was chosen due to the lack of Portuguese available data.
+In addition to that, it was submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin) and [assin2](https://huggingface.co/datasets/assin2) datasets.
 
 ## Full Model Architecture
 ```
 SentenceTransformer(
   (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
 )
 ```