README.md

---
license: mit
language:
- en
pipeline_tag: text-classification
tags:
- sentiment-analysis
- nlp
- distilbert
base_model: distilbert-base-uncased
---

# 🤖 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:

```python
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.