Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 language: 
   - pt
-thumbnail: "Portugues SBERT for the Legal Domain"
+thumbnail: "Portuguese BERT for the Legal Domain"
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
@@ -20,7 +20,7 @@ widget:
     - "O juíz atirou uma pedra."
   example_title: "Example 1"
 model-index:
-- name: SBERTimbau
+- name: BERTimbau
   results:
   - task:
       name: STS
@@ -37,10 +37,10 @@ model-index:
         value: 0.8364
 ---
 
-# rufimelo/Legal-SBERTimbau-sts-large-ma
+# rufimelo/Legal-BERTimbau-sts-large-ma-v3
 
 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.
-rufimelo/Legal-SBERTimbau-sts-large-ma-v3 is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) alrge.
+rufimelo/Legal-BERTimbau-sts-large-ma-v3 is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
 It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.
 
 ## Usage (Sentence-Transformers)
@@ -57,7 +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-large-ma-v3')
+model = SentenceTransformer('rufimelo/Legal-BERTimbau-sts-large-ma-v3')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -83,8 +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-large-ma-v3')
-model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-sts-large-ma-v3')
+tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-BERTimbau-sts-large-ma-v3')
+model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-sts-large-ma-v3')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -105,14 +105,14 @@ print(sentence_embeddings)
 
 | Model| Assin | Assin2|stsb_multi_mt pt|
 | ---------------------------------------- | ---------- | ---------- |---------- |
-| Legal-SBERTimbau-sts-base| 0.71457| 0.73545 | |
-| Legal-SBERTimbau-sts-base-ma| 0.74874 | 0.79532|0.82254 |
-| Legal-SBERTimbau-sts-base-ma-v2| 0.75481 | 0.80262|0.82178|
-| Legal-SBERTimbau-sts-large| 0.76629| 0.82357 | |
-| Legal-SBERTimbau-sts-large-v2| 0.76299 | 0.81121|0.81726 |
-| Legal-SBERTimbau-sts-large-ma| 0.76195| 0.81622 | 0.82608|
-| Legal-SBERTimbau-sts-large-ma-v2| 0.7836| 0.8462| 0.8261| 
-| Legal-SBERTimbau-sts-large-ma-v3| 0.7749| 0.8470| 0.8364| 
+| 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|
@@ -121,7 +121,7 @@ print(sentence_embeddings)
 | paraphrase-multilingual-mpnet-base-v2 Fine-tuned with assin(s)| 0.77641|0.79831   |0.84575 |
 ## Training
 
-rufimelo/Legal-SBERTimbau-sts-large-ma-v3 is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
+rufimelo/Legal-BERTimbau-sts-large-ma-v3 is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
 
 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/stsb-roberta-large', the supposed supported language as English and the language to learn was portuguese.
 
@@ -131,8 +131,8 @@ It was trained for Semantic Textual Similarity, being submitted to a fine tuning
 ## Full Model Architecture
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 768, '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})
+  (0): Transformer({'max_seq_length': 512, '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})
 )
 ```