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---
license: mit
datasets:
- yassiracharki/Amazon_Reviews_for_Sentiment_Analysis_fine_grained_5_classes
language:
- en
metrics:
- accuracy
tags:
- text-classification
- sentiment-classification
- BERT
- Roberta
- mini-roberta
---
# RoBERTa-mini: Sentiment Classifier
**Model Name**: `dilip025/RoBERTa-mini`
**Task**: Sentiment Classification
**Labels**: Very Negative, Negative, Neutral, Positive, Very Positive
A compact RoBERTa like model trained from scratch for sentiment classification.
## Example Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("dilip025/RoBERTa-mini")
model = AutoModelForSequenceClassification.from_pretrained("dilip025/RoBERTa-mini", trust_remote_code=True)
id2label = {
0: "Very Negative",
1: "Negative",
2: "Neutral",
3: "Positive",
4: "Very Positive"
}
def predict_sentiment(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs["logits"], dim=1)
pred_class = torch.argmax(probs, dim=1).item()
return {
"text": text,
"class_id": pred_class,
"label": id2label[pred_class],
"probabilities": probs.tolist()[0]
}
# Example
result = predict_sentiment("I absolutely hate this product.")
print(result)
```
## Model Card
- **Architecture**: RoBERTa (custom small version)
- **Training Dataset**: [Amazon Reviews Dataset](https://huggingface.co/datasets/yassiracharki/Amazon_Reviews_for_Sentiment_Analysis_fine_grained_5_classes/viewer)
- **Use Case**: Sentiment classification for customer feedback, reviews, etc.
- **Input Format**: Plain text (string)
- **Output**: Dictionary with class ID, label, and class probabilities
## License
This model is licensed under the MIT License. You are free to use, modify, and distribute it with attribution.
## Author
Developed and Trained by [Dilip Pokhrel](https://huggingface.co/dilip025) |