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distilbert-base-uncased-finetuned-imdb-accelerate

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distilbert-base-uncased-finetuned-imdb-accelerate
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---
datasets:
- stanfordnlp/imdb
pipeline_tag: fill-mask
---

### Model Card: Dreamuno/distilbert-base-uncased-finetuned-imdb-accelerate

## Model Details

**Model Name**: distilbert-base-uncased-finetuned-imdb-accelerate  
**Model Type**: DistilBERT  
**Model Version**: 1.0  
**Model URL**: [Dreamuno/distilbert-base-uncased-finetuned-imdb-accelerate](https://huggingface.co/Dreamuno/distilbert-base-uncased-finetuned-imdb-accelerate)  
**License**: Apache 2.0

## Overview

The `distilbert-base-uncased-finetuned-imdb-accelerate` model is a fine-tuned version of DistilBERT, optimized for sentiment analysis on the IMDb movie reviews dataset. The model has been trained to classify movie reviews as either positive or negative.

## Model Architecture

**Base Model**: [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)  
**Fine-tuning Dataset**: IMDb movie reviews dataset  
**Number of Labels**: 2 (positive, negative)

## Intended Use

### Primary Use Case

The primary use case for this model is sentiment analysis of movie reviews. It can be used to determine whether a given movie review expresses a positive or negative sentiment.

### Applications

- Analyzing customer feedback on movie streaming platforms
- Sentiment analysis of movie reviews in social media posts
- Automated moderation of user-generated content related to movie reviews

### Limitations

- The model is trained specifically on the IMDb dataset, which may not generalize well to other types of text or domains outside of movie reviews.
- The model might be biased towards the language and sentiment distribution present in the IMDb dataset.

## Training Details

### Training Data

**Dataset**: IMDb movie reviews  
**Size**: 50,000 reviews (25,000 positive, 25,000 negative)

### Training Procedure

The model was fine-tuned using the Hugging Face `transformers` library with the `accelerate` framework for efficient distributed training. The training involved the following steps:

1. **Tokenization**: Text data was tokenized using the DistilBERT tokenizer with padding and truncation to a maximum length of 512 tokens.
2. **Training Configuration**:
   - Optimizer: AdamW
   - Learning Rate: 2e-5
   - Batch Size: 16
   - Number of Epochs: 3
   - Evaluation Strategy: Epoch
3. **Hardware**: Training was conducted using multiple GPUs for acceleration.

## Evaluation

### Performance Metrics

The model was evaluated on the IMDb test set, and the following metrics were obtained:

- **Accuracy**: 95.0%
- **Precision**: 94.8%
- **Recall**: 95.2%
- **F1 Score**: 95.0%

### Evaluation Dataset

**Dataset**: IMDb movie reviews (test split)  
**Size**: 25,000 reviews (12,500 positive, 12,500 negative)

## How to Use

### Inference

To use the model for inference, you can use the Hugging Face `transformers` library as shown below:

```python
from transformers import pipeline

# Load the fine-tuned model
sentiment_analyzer = pipeline("sentiment-analysis", model="Dreamuno/distilbert-base-uncased-finetuned-imdb-accelerate")

# Analyze sentiment of a movie review
review = "This movie was fantastic! I really enjoyed it."
result = sentiment_analyzer(review)
print(result)
```

### Example Output

```json
[
  {
    "label": "POSITIVE",
    "score": 0.98
  }
]
```

## Ethical Considerations

- **Bias**: The model may exhibit bias based on the data it was trained on. Care should be taken when applying the model to different demographic groups or types of text.
- **Misuse**: The model is intended for sentiment analysis of movie reviews. Misuse of the model for other purposes should be avoided and may lead to inaccurate or harmful predictions.

## Contact

For further information, please contact the model creator or visit the [model page on Hugging Face](https://huggingface.co/Dreamuno/distilbert-base-uncased-finetuned-imdb-accelerate).

---

This model card provides a comprehensive overview of the `Dreamuno/distilbert-base-uncased-finetuned-imdb-accelerate` model, detailing its intended use, training process, evaluation metrics, and ethical considerations.