Rcids/my-finetuned-model

🤖 My Fine-Tuned Sentiment Analysis Model

This model is a fine-tuned version of DistilBERT designed for sentiment analysis. It analyzes text and predicts whether the sentiment is POSITIVE or NEGATIVE (or specific labels depending on your training).

📊 Model Details

• Model Architecture: DistilBERT
• Task: Text Classification (Sentiment Analysis)
• Language: English
• License: MIT

🚀 How to Use

You can use this model directly with the Hugging Face pipeline in just a few lines of code:

from transformers import pipeline

# 1. Load the pipeline
classifier = pipeline("text-classification", model="Rcids/my-finetuned-model")

# 2. Test it out
text = "I absolutely loved this product! It was amazing."
result = classifier(text)

print(result)
# Output: [{'label': 'POSITIVE', 'score': 0.99}]

## 🔧 Training Details
This model was fine-tuned on a custom dataset to improve performance on specific sentiment tasks compared to the base generic model.

- **Optimizer:** AdamW
- **Framework:** PyTorch
- **Base Model:** [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)

## ⚠️ Limitations
- The model performance depends on the domain of the data it was trained on.
- It may not detect sarcasm or subtle nuances in complex sentences.