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