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README.md

@@ -13,20 +13,44 @@ widget:
 
 # FinBertPTBR : Financial Bert PT BR
 
-FinBertPTBR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts. It is built by further training the BERTimbau language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification.
+FinBertPTBR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts.
+
+The model was trained in two main stages: language modeling and sentiment modeling. In the first stage, a language model was trained with more than 1.4 million texts of financial news in Portuguese. 
+From this first training, it was possible to build a sentiment classifier with few labeled texts (500) that presented a satisfactory convergence.
+
+At the end of the work, a comparative analysis with other models and the possible applications of the developed model are presented. 
+In the comparative analysis, it was possible to observe that the developed model presented better results than the current models in the state of the art. 
+Among the applications, it was demonstrated that the model can be used to build sentiment indices, investment strategies and macroeconomic data analysis, such as inflation.
+
+## Applications
+
+### Sentiment Index
+
+![Sentiment Index](sentiment_index_and_economy.png)
+
+### Inflation Analysis
+
+![Inflation Analysis](sentiment_inflation.png)
 
 ## Usage
 ```python
-from transformers import AutoTokenizer, AutoModel
+from transformers import AutoTokenizer, BertForSequenceClassification
+import numpy as np
   
-tokenizer = AutoTokenizer.from_pretrained("turing-usp/FinBertPTBR")
-model = AutoModel.from_pretrained("turing-usp/FinBertPTBR")
+pred_mapper = {0: 'negative', 1: 'positive', 2: 'neutral'}
+
+tokenizer = AutoTokenizer.from_pretrained("lucas-leme/FinBERT-PT-BR")
+finbertptbr = BertForSequenceClassification.from_pretrained("lucas-leme/FinBERT-PT-BR")
+
+tokens = tokenizer(["Hoje a bolsa caiu", "Hoje a bolsa subiu"], return_tensors="pt",
+                    padding=True, truncation=True, max_length=512)
+finbertptbr_outputs = finbertptbr(**tokens)
+preds = [pred_mapper[np.argmax(pred)] for pred in finbertptbr_outputs.logits.cpu().detach().numpy()]
 ```
 
-## Authors
+## Author
 
-  - [Vinicius Carmo](https://www.linkedin.com/in/vinicius-cleves/)
-  - [Julia Pocciotti](https://www.linkedin.com/in/juliapocciotti/)
-  - [Luísa Heise](https://www.linkedin.com/in/lu%C3%ADsa-mendes-heise/)
   - [Lucas Leme](https://www.linkedin.com/in/lucas-leme-santos/)
-  
+
+## Paper - Stay tuned!
+  

config.json

@@ -10,9 +10,9 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "LABEL_0",
-    "1": "LABEL_1",
-    "2": "LABEL_2"
+    "0": "POSITIVE",
+    "1": "NEGATIVE",
+    "2": "NEUTRAL"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
@@ -40,4 +40,4 @@
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 29794
-}
+}