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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false
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+ }
README.md CHANGED
@@ -1,3 +1,143 @@
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  ---
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- license: mit
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - pt
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+ thumbnail: "Portugues SBERT for the Legal Domain"
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+ pipeline_tag: sentence-similarity
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - transformers
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+ datasets:
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+ - assin
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+ - assin2
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+ - stsb_multi_mt
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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:
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+ - "O juíz leu as provas."
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+ - "O juíz leu o recurso."
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+ - "O juíz atirou uma pedra."
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+ example_title: "Example 1"
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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-sts-base-ma
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+
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+ 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.
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+ 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.
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+ It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.
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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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+
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+ model = SentenceTransformer('rufimelo/Legal-SBERTimbau-sts-base-ma')
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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
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+
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+
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+ #Mean Pooling - Take attention mask into account for correct averaging
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+ def mean_pooling(model_output, attention_mask):
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+ token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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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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+
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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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+
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+ # Load model from HuggingFace Hub
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+ tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-SBERTimbau-sts-base-ma')
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+ model = AutoModel.from_pretrained('rufimelo/Legal-SBERTimbau-sts-base-ma')
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+
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+ # Tokenize sentences
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+ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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+
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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-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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+ | Legal-SBERTimbau-sts-base-ma| Assin | 0.74874 |
102
+ | Legal-SBERTimbau-sts-base-ma| Assin2| 0.79532 |
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+ | Legal-SBERTimbau-sts-base-ma| stsb_multi_mt pt| 0.82254 |
104
+ | ---------------------------------------- | ---------- |---------- |
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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| stsb_multi_mt pt| 0.83999 |
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+ | paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)| Assin | 0.77641 |
109
+ | paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)| Assin2| 0.79831 |
110
+ | paraphrase-multilingual-mpnet-base-v2 Fine tuned with assin(s)| stsb_multi_mt pt| 0.84575 |
111
+
112
+ ## Training
113
+
114
+ 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.
115
+
116
+ Firstly, due to the lack of portuguese datasets, it was trained using multilingual knowledge distillation.
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+ 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.
118
+
119
+ It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2) and [stsb_multi_mt pt](https://huggingface.co/datasets/stsb_multi_mt) datasets.
120
+
121
+
122
+ ## Full Model Architecture
123
+ ```
124
+ SentenceTransformer(
125
+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
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+ (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})
127
+ )
128
+ ```
129
+
130
+ ## Citing & Authors
131
+
132
+ If you use this work, please cite BERTimbau's work:
133
+
134
+ ```bibtex
135
+ @inproceedings{souza2020bertimbau,
136
+ author = {F{\'a}bio Souza and
137
+ Rodrigo Nogueira and
138
+ Roberto Lotufo},
139
+ title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
140
+ booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
141
+ year = {2020}
142
+ }
143
+ ```
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