stjiris
/

bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0

@@ -1,42 +1,82 @@
 ---
-pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
-- feature-extraction
-- sentence-similarity
 - transformers
 ---
-# {MODEL_NAME}
 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.
-<!--- Describe your model here -->
-## 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 = ["This is an example sentence", "Each sentence is converted"]
-model = SentenceTransformer('{MODEL_NAME}')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 ## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
 ```python
 from transformers import AutoTokenizer, AutoModel
 import torch
@@ -48,13 +88,12 @@ def mean_pooling(model_output, attention_mask):
     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('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -62,64 +101,66 @@ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tenso
 # 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
-<!--- Describe how your model was evaluated -->
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-## Training
-The model was trained with the parameters:
-**DataLoader**:
-`torch.utils.data.dataloader.DataLoader` of length 2157 with parameters:
-```
-{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-**Loss**:
-`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 5,
-    "evaluation_steps": 0,
-    "evaluator": "NoneType",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
-    "optimizer_params": {
-        "lr": 1e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 1079,
-    "weight_decay": 0.01
 }
-```
-## Full Model Architecture
-```
-SentenceTransformer(
-  (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})
-)
-```
-## Citing & Authors
-<!--- Describe where people can find more information -->

 ---
+language:
+- pt
+thumbnail: Portuguese BERT for the Legal Domain
 tags:
 - sentence-transformers
 - transformers
+- bert
+- pytorch
+- sentence-similarity
+license: mit
+pipeline_tag: sentence-similarity
+datasets:
+- stjiris/portuguese-legal-sentences-v0
+- assin
+- assin2
+- stsb_multi_mt
+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."
+model-index:
+- name: BERTimbau
+  results:
+  - task:
+      name: STS
+      type: STS
+    metrics:
+      - name: Pearson Correlation - assin Dataset
+        type: Pearson Correlation
+        value: 0.7786255075301941
+      - name: Pearson Correlation - assin2 Dataset
+        type: Pearson Correlation
+        value: 0.8067505591581619
+      - name: Pearson Correlation - stsb_multi_mt pt Dataset
+        type: Pearson Correlation
+        value: 0.8082102611526221
+      - name: Pearson Correlation - IRIS STS Dataset
+        type: Pearson Correlation
+        value: 0.8392547167695102
 ---
+![INESC-ID](https://www.inesc-id.pt/wp-content/uploads/2019/06/INESC-ID-logo_01.png)
+![A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/_static/logo.png)
+Work developed as part of [Project IRIS](https://www.inesc-id.pt/projects/PR07005/).
+Thesis: [A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/)
+# stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0 (Legal BERTimbau)
 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.
+stjiris/bert-large-portuguese-cased-legal-tsdae derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
+It was trained using the TSDAE technique with a learning rate 1e-5 [Legal Sentences from +-30000 documents](https://huggingface.co/datasets/stjiris/portuguese-legal-sentences-v1.0) 212k training steps (best performance for our semantic search system implementation)
+It was presented to Generative Pseudo Labeling training.
+The model was presented to NLI data. 16 batch size,  2e-5 lr
+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), [stsb_multi_mt pt](https://huggingface.co/datasets/stsb_multi_mt)  datasets. 'lr': 1e-5
+## 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('stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 ## Usage (HuggingFace Transformers)
 ```python
 from transformers import AutoTokenizer, AutoModel
 import torch
     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('stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0')
+model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-tsdae-gpl-nli-sts-v0')
 # 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)
 ```
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel
+  (1): Pooling({'word_embedding_dimension': 1028, '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
+### Contributions
+[@rufimelo99](https://github.com/rufimelo99)
+If you use this work, please cite:
+```bibtex
+@inproceedings{MeloSemantic,
+	author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o},
+	title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a},
 }
+@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}
+}
+```