SYAS1 PTBR

🚀 SYAS1

SYAS1-PTBR is a Transformer model based on Distilbert, focusing on sentiment analysis for Brazilian Portuguese.

Apart from being used for study purposes, its main goal is to contribute to the Brazilian community, as there is a significant shortage of LLMs for the Portuguese language.

📚 Documentation

SYAS1-PTBR is a model based on Distilbert-base, trained through fine-tuning using the "Portuguese Tweets for Sentiment Analysis" dataset from Kaggle. It was trained exclusively on Google Colab Pro with an NVIDIA L4 GPU.

🔧 Technical Details

💻 Usage Examples

Basic Usage

import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("1Arhat/SYAS1-PTBR")
model = AutoModelForSequenceClassification.from_pretrained("1Arhat/SYAS1-PTBR")

# Text for classification
texto = "Esse produto é incrível! Recomendo muito."

# Process your text
inputs = tokenizer(texto, return_tensors="pt")

# Model predictions
with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits
predicao = torch.argmax(logits, dim=1).item()

#print(f"Classe prevista: {predicao}")

# Output: Classe prevista: 2

# If you want it to provide percentages for all classes:

probs = F.softmax(logits, dim=1)  # Use of the softmax function to transform the logits output into probabilities

labels = ["Negativo", "Neutro", "Positivo"]

for idx, label in enumerate(labels):
    print(f'{label}: {probs[0][idx]}')

# Output:

#Negativo: 0.2401905506849289
#Neutro: 0.028042761608958244
#Positivo: 0.7317667007446289

# If you want more convenience, use the Hugging face pipeline

✨ Features

SYAS1 is designed for sentiment analysis, specifically focused on the Portuguese language.

As it is a model fine-tuned from DistilBERT, it can work in English, but it is highly recommended to use it only for Portuguese.

If you need sentiment analysis in other languages, I recommend using other available models.

Results