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Model: rufimelo/Legal-BERTimbau-sts-large
Source: Original Platform

.gitattributes

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1_Pooling/config.json

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+{
+  "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
+}

README.md

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+
+---
+language: 
+  - pt
+thumbnail: "Portugues BERT for the Legal Domain"
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- transformers
+datasets:
+- assin
+- assin2
+- rufimelo/PortugueseLegalSentences-v0
+widget:
+- source_sentence: "O advogado apresentou as provas ao juíz."
+  sentences:
+    - "O juíz leu as provas."
+    - "O juíz leu o recurso."
+    - "O juíz atirou uma pedra."
+  example_title: "Example 1"
+model-index:
+- name: BERTimbau
+  results:
+  - task:
+      name: STS
+      type: STS
+    metrics:
+      - name: Pearson Correlation - assin Dataset
+        type: Pearson Correlation
+        value: 0.76629
+      - name: Pearson Correlation - assin2 Dataset
+        type: Pearson Correlation
+        value: 0.82357 
+      - name: Pearson Correlation - stsb_multi_mt pt Dataset
+        type: Pearson Correlation
+        value: 0.79120
+---
+# rufimelo/Legal-BERTimbau-sts-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.
+rufimelo/Legal-BERTimbau-sts-large 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)
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+```
+pip install -U sentence-transformers
+```
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
+
+model = SentenceTransformer('rufimelo/Legal-BERTimbau-sts-large')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+## Usage (HuggingFace Transformers)
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-BERTimbau-sts-large')
+model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-sts-large')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+## Evaluation Results STS
+
+| Model| Assin | Assin2|stsb_multi_mt pt| avg|
+| ---------------------------------------- | ---------- | ---------- |---------- |---------- |
+| Legal-BERTimbau-sts-base| 0.71457| 0.73545 | 0.72383|0.72462|
+| Legal-BERTimbau-sts-base-ma| 0.74874 | 0.79532|0.82254 |0.78886|
+| Legal-BERTimbau-sts-base-ma-v2| 0.75481 | 0.80262|0.82178|0.79307|
+| Legal-BERTimbau-base-TSDAE-sts|0.78814 |0.81380 |0.75777|0.78657|
+| Legal-BERTimbau-sts-large| 0.76629| 0.82357 | 0.79120|0.79369|
+| Legal-BERTimbau-sts-large-v2| 0.76299 | 0.81121|0.81726 |0.79715|
+| Legal-BERTimbau-sts-large-ma| 0.76195| 0.81622 | 0.82608|0.80142|
+| Legal-BERTimbau-sts-large-ma-v2| 0.7836| 0.8462| 0.8261| 0.81863|
+| Legal-BERTimbau-sts-large-ma-v3| 0.7749| **0.8470**| 0.8364| **0.81943**|
+| Legal-BERTimbau-large-v2-sts| 0.71665| 0.80106| 0.73724| 0.75165|
+| Legal-BERTimbau-large-TSDAE-sts| 0.72376| 0.79261| 0.73635| 0.75090|
+| Legal-BERTimbau-large-TSDAE-sts-v2| 0.81326| 0.83130| 0.786314| 0.81029|
+| Legal-BERTimbau-large-TSDAE-sts-v3|0.80703 |0.82270 |0.77638 |0.80204 |
+| ---------------------------------------- | ---------- |---------- |---------- |---------- |
+| BERTimbau base Fine-tuned for STS|**0.78455** | 0.80626|0.82841|0.80640|
+| BERTimbau large Fine-tuned for STS|0.78193 | 0.81758|0.83784|0.81245|
+| ---------------------------------------- | ---------- |---------- |---------- |---------- |
+| paraphrase-multilingual-mpnet-base-v2| 0.71457| 0.79831 |0.83999 |0.78429|
+| paraphrase-multilingual-mpnet-base-v2 Fine-tuned with assin(s)| 0.77641|0.79831   |**0.84575**|0.80682|
+
+## Training
+rufimelo/Legal-BERTimbau-sts-large is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) large.
+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.
+## 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})
+)
+```
+## Citing & Authors
+## Citing & Authors
+
+If you use this work, please cite:
+
+```bibtex
+@inproceedings{souza2020bertimbau,
+  author    = {F{\'a}bio Souza and
+               Rodrigo Nogueira and
+               Roberto Lotufo},
+  title     = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
+  booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
+  year      = {2020}
+}
+
+@inproceedings{fonseca2016assin,
+  title={ASSIN: Avaliacao de similaridade semantica e inferencia textual},
+  author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S},
+  booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal},
+  pages={13--15},
+  year={2016}
+}
+
+@inproceedings{real2020assin,
+  title={The assin 2 shared task: a quick overview},
+  author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
+  booktitle={International Conference on Computational Processing of the Portuguese Language},
+  pages={406--412},
+  year={2020},
+  organization={Springer}
+}
+@InProceedings{huggingface:dataset:stsb_multi_mt,
+title = {Machine translated multilingual STS benchmark dataset.},
+author={Philip May},
+year={2021},
+url={https://github.com/PhilipMay/stsb-multi-mt}
+}
+
+```

config.json

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+{
+  "_name_or_path": "Legal_SBERT/",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.20.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 29794
+}

config_sentence_transformers.json

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+  "__version__": {
+    "sentence_transformers": "2.2.0",
+    "transformers": "4.20.1",
+    "pytorch": "1.10.1+cu111"
+  }
+}

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modules.json

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+  }
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sentence_bert_config.json

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+{
+  "max_seq_length": 75,
+  "do_lower_case": false
+}

similarity_evaluation_SBERTimbau_fine_tune_assin2_results.csv

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similarity_evaluation_SBERTimbau_fine_tune_assin_results.csv

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special_tokens_map.json

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+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "name_or_path": "Legal_SBERT/",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}