Update README.md

README.md

@@ -1,125 +1,115 @@
 ---
-pipeline_tag: sentence-similarity
+language: 
+  - pt
+thumbnail: "Portugues BERT for the Legal Domain"
 tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
+  - bert
+  - pytorch
+  - tsdae
+datasets:
+ - rufimelo/PortugueseLegalSentences-v1
+license: "mit"
+widget:
+ - text: "O advogado apresentou [MASK] ao juíz."
 ---
 
-# {MODEL_NAME}
+# 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.
+## Introduction
 
-<!--- Describe your model here -->
+Legal_BERTimbau Large is a fine-tuned BERT model based on [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) Large.
 
-## Usage (Sentence-Transformers)
+"BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity and Recognizing Textual Entailment. It is available in two sizes: Base and Large.
 
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+For further information or requests, please go to [BERTimbau repository](https://github.com/neuralmind-ai/portuguese-bert/)."
 
-```
-pip install -U sentence-transformers
-```
+The performance of Language Models can change drastically when there is a domain shift between training and test data. In order create a Portuguese Language Model adapted to a Legal domain, the original BERTimbau model was submitted to a fine-tuning stage where it was performed 1 "PreTraining" epoch over 200000 cleaned documents (lr: 2e-5, using TSDAE technique)
 
-Then you can use the model like this:
 
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+## Available models
 
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
+| Model                                    | Arch.      | #Layers | #Params |
+| ---------------------------------------- | ---------- | ------- | ------- |
+|`rufimelo/Legal-BERTimbau-base`	|BERT-Base	|12	|110M|
+| `rufimelo/Legal-BERTimbau-large` | BERT-Large | 24      | 335M    |
 
-
-
-## 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.
+## Usage
 
 ```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-
+from transformers import AutoTokenizer, AutoModelForMaskedLM
 
-#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)
+tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal-BERTimbau-large-TSDAE")
 
-
-# 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')
-
-# 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)
+model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal-BERTimbau-large-TSDAE")
 ```
 
+### Masked language modeling prediction example
 
+```python
+from  transformers  import  pipeline
+from  transformers  import  AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("rufimelo/Legal-BERTimbau-large-TSDAE")
+model = AutoModelForMaskedLM.from_pretrained("rufimelo/Legal-BERTimbau-large-TSDAE")
+
+pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer)
+pipe('O advogado apresentou [MASK] para o juíz')
+# [{'score': 0.5034703612327576, 
+#'token': 8190, 
+#'token_str': 'recurso', 
+#'sequence': 'O advogado apresentou recurso para o juíz'}, 
+#{'score': 0.07347951829433441, 
+#'token': 21973, 
+#'token_str': 'petição', 
+#'sequence': 'O advogado apresentou petição para o juíz'}, 
+#{'score': 0.05165359005331993, 
+#'token': 4299, 
+#'token_str': 'resposta', 
+#'sequence': 'O advogado apresentou resposta para o juíz'}, 
+#{'score': 0.04611917585134506,
+#'token': 5265, 
+#'token_str': 'exposição', 
+#'sequence': 'O advogado apresentou exposição para o juíz'}, 
+#{'score': 0.04068068787455559, 
+#'token': 19737, 'token_str': 
+#'alegações', 
+#'sequence': 'O advogado apresentou alegações para o juíz'}]
 
-## 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**:
+### For BERT embeddings
 
-`torch.utils.data.dataloader.DataLoader` of length 200000 with parameters:
-```
-{'batch_size': 1, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```python
+import  torch
+from  transformers  import  AutoModel
+
+model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-large-TSDAE')
+input_ids = tokenizer.encode('O advogado apresentou recurso para o juíz', return_tensors='pt')
+
+with  torch.no_grad():
+	outs = model(input_ids)
+	encoded = outs[0][0, 1:-1]
+	
+#tensor([[ 0.0328, -0.4292, -0.6230, ..., -0.3048, -0.5674, 0.0157], 
+#[-0.3569, 0.3326, 0.7013, ..., -0.7778, 0.2646, 1.1310], 
+#[ 0.3169, 0.4333, 0.2026, ..., 1.0517, -0.1951, 0.7050], 
+#..., 
+#[-0.3648, -0.8137, -0.4764, ..., -0.2725, -0.4879, 0.6264], 
+#[-0.2264, -0.1821, -0.3011, ..., -0.5428, 0.1429, 0.0509], 
+#[-1.4617, 0.6281, -0.0625, ..., -1.2774, -0.4491, 0.3131]])
 ```
 
-**Loss**:
+## Citation
 
-`sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss` 
+If you use this work, please cite BERTimbau's work:
 
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 1,
-    "evaluation_steps": 0,
-    "evaluator": "NoneType",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
-    "optimizer_params": {
-        "lr": 1e-05
-    },
-    "scheduler": "constantlr",
-    "steps_per_epoch": null,
-    "warmup_steps": 10000,
-    "weight_decay": 0
+```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}
 }
 ```
-
-
-## 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 -->