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Legal-BERTimbau-sts-large

Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Portuguese
bert
feature-extraction
Eval Results
text-embeddings-inference
Inference Endpoints
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@@ -10,7 +10,6 @@ tags:
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  datasets:
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  - assin
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  - assin2
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-
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  widget:
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  - source_sentence: "O advogado apresentou as provas ao juíz."
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  sentences:
@@ -21,37 +20,25 @@ widget:
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  metrics:
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  - bleu
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  ---
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-
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- # rufimelo/Legal-SBERTimbau-nli-large
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-
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  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.
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- Legal-SBERTimbau-large is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large.
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- It is adapted to the Portuguese legal domain.
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-
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  ## Usage (Sentence-Transformers)
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-
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  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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-
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  ```
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  pip install -U sentence-transformers
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  ```
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-
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  Then you can use the model like this:
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-
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  ```python
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  from sentence_transformers import SentenceTransformer
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  sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
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- model = SentenceTransformer('rufimelo/Legal-SBERTimbau-nli-large')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
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-
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-
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-
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  ## Usage (HuggingFace Transformers)
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-
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-
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  ```python
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  from transformers import AutoTokenizer, AutoModel
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  import torch
@@ -63,13 +50,12 @@ def mean_pooling(model_output, attention_mask):
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  input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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  return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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-
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  # Sentences we want sentence embeddings for
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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- tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-SBERTimbau-nli-large')
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- model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-nli-large}')
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -77,26 +63,21 @@ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tenso
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  # Compute token embeddings
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  with torch.no_grad():
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  model_output = model(**encoded_input)
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-
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  # Perform pooling. In this case, mean pooling.
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  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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-
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  print("Sentence embeddings:")
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  print(sentence_embeddings)
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  ```
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-
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-
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  ## Evaluation Results STS
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-
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  | Model| Dataset | PearsonCorrelation |
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  | ---------------------------------------- | ---------- | ---------- |
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- | Legal-SBERTimbau-large| Assin | 0.76629 |
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- | Legal-SBERTimbau-large| Assin2| 0.82357 |
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- | Legal-SBERTimbau-base| Assin | 0.71457 |
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- | Legal-SBERTimbau-base| Assin2| 0.73545|
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- | Legal-SBERTimbau-sts-large| Assin | 0.76299 |
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- | Legal-SBERTimbau-sts-large| Assin2| 0.81121 |
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- | Legal-SBERTimbau-sts-large| stsb_multi_mt pt| 0.81726 |
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  | ---------------------------------------- | ---------- |---------- |
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  | paraphrase-multilingual-mpnet-base-v2| Assin | 0.71457|
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  | paraphrase-multilingual-mpnet-base-v2| Assin2| 0.79831 |
@@ -104,26 +85,18 @@ print(sentence_embeddings)
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  | paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)| Assin | 0.77641 |
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  | paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)| Assin2| 0.79831 |
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  | paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)| stsb_multi_mt pt| 0.84575 |
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-
108
-
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  ## Training
110
-
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- Legal-SBERTimbau-large is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large.
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- It was trained for Natural Language Inference (NLI). This was chosen due to the lack of Portuguese available data.
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- 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.
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-
115
  ## Full Model Architecture
116
  ```
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  SentenceTransformer(
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- (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel
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- (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})
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  )
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  ```
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-
123
  ## Citing & Authors
124
-
125
  If you use this work, please cite BERTimbau's work:
126
-
127
  ```bibtex
128
  @inproceedings{souza2020bertimbau,
129
  author = {F{\'a}bio Souza and
 
10
  datasets:
11
  - assin
12
  - assin2
 
13
  widget:
14
  - source_sentence: "O advogado apresentou as provas ao juíz."
15
  sentences:
 
20
  metrics:
21
  - bleu
22
  ---
23
+ # rufimelo/Legal-SBERTimbau-sts-large
 
 
24
  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.
25
+ rufimelo/Legal-SBERTimbau-sts-large is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) alrge.
26
+ It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.
 
27
  ## Usage (Sentence-Transformers)
 
28
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
29
  ```
30
  pip install -U sentence-transformers
31
  ```
 
32
  Then you can use the model like this:
 
33
  ```python
34
  from sentence_transformers import SentenceTransformer
35
  sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
36
 
37
+ model = SentenceTransformer('rufimelo/Legal-SBERTimbau-sts-large')
38
  embeddings = model.encode(sentences)
39
  print(embeddings)
40
  ```
 
 
 
41
  ## Usage (HuggingFace Transformers)
 
 
42
  ```python
43
  from transformers import AutoTokenizer, AutoModel
44
  import torch
 
50
  input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
51
  return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
52
 
 
53
  # Sentences we want sentence embeddings for
54
  sentences = ['This is an example sentence', 'Each sentence is converted']
55
 
56
  # Load model from HuggingFace Hub
57
+ tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-SBERTimbau-sts-large')
58
+ model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-sts-large')
59
 
60
  # Tokenize sentences
61
  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
 
63
  # Compute token embeddings
64
  with torch.no_grad():
65
  model_output = model(**encoded_input)
 
66
  # Perform pooling. In this case, mean pooling.
67
  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
 
68
  print("Sentence embeddings:")
69
  print(sentence_embeddings)
70
  ```
 
 
71
  ## Evaluation Results STS
 
72
  | Model| Dataset | PearsonCorrelation |
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  | ---------------------------------------- | ---------- | ---------- |
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+ | Legal-SBERTimbau-sts-large| Assin | 0.76629 |
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+ | Legal-SBERTimbau-sts-large| Assin2| 0.82357 |
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+ | Legal-SBERTimbau-sts-base| Assin | 0.71457 |
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+ | Legal-SBERTimbau-sts-base| Assin2| 0.73545|
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+ | Legal-SBERTimbau-sts-large-v2| Assin | 0.76299 |
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+ | Legal-SBERTimbau-sts-large-v2| Assin2| 0.81121 |
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+ | Legal-SBERTimbau-sts-large-v2| stsb_multi_mt pt| 0.81726 |
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  | ---------------------------------------- | ---------- |---------- |
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  | paraphrase-multilingual-mpnet-base-v2| Assin | 0.71457|
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  | paraphrase-multilingual-mpnet-base-v2| Assin2| 0.79831 |
 
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  | paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)| Assin | 0.77641 |
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  | paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)| Assin2| 0.79831 |
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  | paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)| stsb_multi_mt pt| 0.84575 |
 
 
88
  ## Training
89
+ rufimelo/Legal-SBERTimbau-sts-large is based on Legal-BERTimbau-largewhich derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) large.
90
+ It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin) and [assin2](https://huggingface.co/datasets/assin2) datasets.
 
 
 
91
  ## Full Model Architecture
92
  ```
93
  SentenceTransformer(
94
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
95
+ (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})
96
  )
97
  ```
 
98
  ## Citing & Authors
 
99
  If you use this work, please cite BERTimbau's work:
 
100
  ```bibtex
101
  @inproceedings{souza2020bertimbau,
102
  author = {F{\'a}bio Souza and