---
license: apache-2.0
datasets:
- stanfordnlp/imdb
language:
- en
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
library_name: transformers
---
# Sentiment Analysis Model

This model is a fine-tuned version of `distilbert-base-uncased` on the IMDb dataset for sentiment analysis.

## Model Details
- **Base model**: [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased)
- **Fine-tuning dataset**: IMDb
- **Task**: Sentiment analysis (text classification)

## Intended Use
The model is designed to classify text into positive or negative sentiment. You can use it for tasks such as:
- Analyzing product reviews.
- Social media sentiment analysis.
- General text classification tasks involving sentiment.

## Limitations
- The model is fine-tuned on the IMDb dataset and may not generalize well to all domains or datasets.
- It may inherit biases from the IMDb dataset.

## Example Usage
```python
from transformers import pipeline

# Load the model
model_pipeline = pipeline("text-classification", model="proc015/sentiment-model")

# Run sentiment analysis
result = model_pipeline("I love this product!")
print(result)