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Create README.md |
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## ByT5 Base Portuguese Product Reviews |
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#### Model Description |
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This is a finetuned version from ByT5 Base by Google for Sentimental Analysis from Product Reviews in Portuguese. |
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##### Paper: https://arxiv.org/abs/2105.13626 |
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#### Training data |
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It was trained from products reviews from a Americanas.com. You can found the data here: https://github.com/HeyLucasLeao/finetuning-byt5-model. |
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#### Training Procedure |
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It was finetuned using the Trainer Class available on the Hugging Face library. For evaluation it was used accuracy, precision, recall and f1 score. |
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##### Learning Rate: **1e-4** |
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##### Epochs: **1** |
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##### Colab for Finetuning: https://drive.google.com/file/d/17TcaN52moq7i7TE2EbcVbwQEQuAIQU63/view?usp=sharing |
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##### Colab for Metrics: https://colab.research.google.com/drive/1wbTDfOsE45UL8Q3ZD1_FTUmdVOKCcJFf#scrollTo=S4nuLkAFrlZ6 |
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#### Score: |
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```python |
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Training Set: |
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'accuracy': 0.9019706922688226, |
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'f1': 0.9305820610687022, |
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'precision': 0.9596555965559656, |
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'recall': 0.9032183375781431 |
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Test Set: |
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'accuracy': 0.9019409684035312, |
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'f1': 0.9303758732034697, |
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'precision': 0.9006660401258529, |
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'recall': 0.9621126145787866 |
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Validation Set: |
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'accuracy': 0.9044948078526491, |
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'f1': 0.9321924443009364, |
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'precision': 0.9024426549173129, |
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'recall': 0.9639705531617191 |
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``` |
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#### Goals |
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My true intention was totally educational, thus making available a this version of the model as a example for future proposes. |
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How to use |
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``` python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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import torch |
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if torch.cuda.is_available(): |
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device = torch.device('cuda') |
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else: |
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device = torch.device('cpu') |
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print(device) |
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tokenizer = AutoTokenizer.from_pretrained("HeyLucasLeao/byt5-base-pt-product-reviews") |
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model = AutoModelForSeq2SeqLM.from_pretrained("HeyLucasLeao/byt5-base-pt-product-reviews") |
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model.to(device) |
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def classificar_review(review): |
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inputs = tokenizer([review], padding='max_length', truncation=True, max_length=512, return_tensors='pt') |
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input_ids = inputs.input_ids.to(device) |
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attention_mask = inputs.attention_mask.to(device) |
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output = model.generate(input_ids, attention_mask=attention_mask) |
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pred = np.argmax(output.cpu(), axis=1) |
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dici = {0: 'Review Negativo', 1: 'Review Positivo'} |
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return dici[pred.item()] |
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classificar_review(review) |
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``` |