Update README.md

README.md

@@ -1,3 +1,4 @@
+
 ---
 language: 
   - pt
@@ -19,14 +20,27 @@ widget:
     - "O juíz leu o recurso."
     - "O juíz atirou uma pedra."
   example_title: "Example 1"
-metrics:
-  - bleu
+model-index:
+- name: BERTimbau
+  results:
+  - task:
+      name: STS
+      type: STS
+    metrics:
+      - name: Pearson Correlation - assin Dataset
+        type: Pearson Correlation
+        value: 0.74874 
+      - name: Pearson Correlation - assin2 Dataset
+        type: Pearson Correlation
+        value: 0.79532
+      - name: Pearson Correlation - stsb_multi_mt pt Dataset
+        type: Pearson Correlation
+        value: 0.82254 
 ---
-
-# rufimelo/Legal-SBERTimbau-sts-base-ma
+# rufimelo/Legal-BERTimbau-sts-base-ma
 
 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.
-rufimelo/rufimelo/Legal-SBERTimbau-sts-base-ma is based on Legal-BERTimbau-base which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) alrge.
+rufimelo/rufimelo/Legal-BERTimbau-sts-base-ma is based on Legal-BERTimbau-base which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) alrge.
 It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.
 
 ## Usage (Sentence-Transformers)
@@ -43,7 +57,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
 
-model = SentenceTransformer('rufimelo/Legal-SBERTimbau-sts-base-ma')
+model = SentenceTransformer('rufimelo/Legal-BERTimbau-sts-base-ma')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -69,8 +83,8 @@ 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('rufimelo/Legal-SBERTimbau-sts-base-ma')
-model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-sts-base-ma')
+tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-BERTimbau-sts-base-ma')
+model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-sts-base-ma')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -89,29 +103,26 @@ print(sentence_embeddings)
 
 ## Evaluation Results STS
 
-| Model| Dataset |  PearsonCorrelation       |
-| ---------------------------------------- | ---------- | ---------- |
-| Legal-SBERTimbau-sts-large| Assin | 0.76629 |
-| Legal-SBERTimbau-sts-large| Assin2| 0.82357 |
-| Legal-SBERTimbau-sts-base| Assin | 0.71457 |
-| Legal-SBERTimbau-sts-base| Assin2| 0.73545|
-| Legal-SBERTimbau-sts-large-v2| Assin | 0.76299 |
-| Legal-SBERTimbau-sts-large-v2| Assin2| 0.81121 |
-| Legal-SBERTimbau-sts-large-v2| stsb_multi_mt pt| 0.81726 |
-| Legal-SBERTimbau-sts-base-ma| Assin | 0.74874 |
-| Legal-SBERTimbau-sts-base-ma| Assin2| 0.79532 |
-| Legal-SBERTimbau-sts-base-ma| stsb_multi_mt pt| 0.82254 |
-| ---------------------------------------- | ---------- |---------- |
-| paraphrase-multilingual-mpnet-base-v2| Assin | 0.71457|
-| paraphrase-multilingual-mpnet-base-v2| Assin2| 0.79831 |
-| paraphrase-multilingual-mpnet-base-v2| stsb_multi_mt pt| 0.83999 |
-| 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 |
-| paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)| stsb_multi_mt pt| 0.84575 |
+| Model| Assin | Assin2|stsb_multi_mt pt|
+| ---------------------------------------- | ---------- | ---------- |---------- |
+| Legal-BERTimbau-sts-base| 0.71457| 0.73545 | |
+| Legal-BERTimbau-sts-base-ma| 0.74874 | 0.79532|0.82254 |
+| Legal-BERTimbau-sts-base-ma-v2| 0.75481 | 0.80262|0.82178|
+| Legal-BERTimbau-sts-large| 0.76629| 0.82357 | |
+| Legal-BERTimbau-sts-large-v2| 0.76299 | 0.81121|0.81726 |
+| Legal-BERTimbau-sts-large-ma| 0.76195| 0.81622 | 0.82608|
+| Legal-BERTimbau-sts-large-ma-v2| 0.7836| 0.8462| 0.8261| 
+| Legal-BERTimbau-sts-large-ma-v3| 0.7749| 0.8470| 0.8364| 
+| ---------------------------------------- | ---------- |---------- |---------- |
+| BERTimbau base Fine-tuned for STS|0.78455 | 0.80626|0.82841|
+| BERTimbau large Fine-tuned for STS|0.78193 | 0.81758|0.83784|
+| ---------------------------------------- | ---------- |---------- |---------- |
+| paraphrase-multilingual-mpnet-base-v2| 0.71457| 0.79831 |0.83999 |
+| paraphrase-multilingual-mpnet-base-v2 Fine-tuned with assin(s)| 0.77641|0.79831   |0.84575 |
 
 ## Training
 
-rufimelo/Legal-SBERTimbau-sts-base-ma is based on Legal-BERTimbau-base which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) base.
+rufimelo/Legal-BERTimbau-sts-base-ma is based on Legal-BERTimbau-base which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) base.
 
 Firstly, due to the lack of portuguese datasets, it was trained using multilingual knowledge distillation. 
 For the Multilingual Knowledge Distillation process, the teacher model was 'sentence-transformers/paraphrase-xlm-r-multilingual-v1', the supposed supported language as English and the language to learn was portuguese.