Update README.md

README.md

@@ -1,42 +1,65 @@
+
 ---
+language: 
+  - pt
+thumbnail: "Portuguese BERT for the Legal Domain"
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
-- feature-extraction
 - sentence-similarity
 - transformers
+datasets:
+- assin
+- assin2
+- stjiris/portuguese-legal-sentences-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: 0.7716333759993093
+      - name: Pearson Correlation - assin2 Dataset
+        type: Pearson Correlation
+        value: 0.8403302138785704
+      - name: Pearson Correlation - stsb_multi_mt pt Dataset
+        type: Pearson Correlation
+        value: 0.8249826985133595
 ---
+# stjiris/bert-large-portuguese-cased-legal-mlm-sts-v0
+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-mlm-sts-v0 derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
 
-# {MODEL_NAME}
+It was trained using the MLM technique with a learning rate 3e-5 [Legal Sentences from +-30000 documents](https://huggingface.co/datasets/stjiris/portuguese-legal-sentences-v0)
 
-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.
+It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets. [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2) and [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt) portuguese subdataset
 
-<!--- 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"]
+sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v0')
 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 +71,12 @@ 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}')
+tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v0')
+model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v0')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -62,64 +84,60 @@ 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:
+## Full Model Architecture
 ```
-{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+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
 
-**Loss**:
-
-`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
+If you use this work, please cite:
 
-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
+```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}
+}
 
-## 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})
-)
-```
+@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}
+}
 
-## Citing & Authors
+@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}
+}
 
-<!--- Describe where people can find more information -->
+```