transformers safetensors distilbert text-classification torch code en dataset:stanfordnlp/imdb base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased autotrain_compatible endpoints_compatible region:us
---
base_model:
- distilbert/distilbert-base-uncased
datasets:
- stanfordnlp/imdb
language:
- en
library_name: transformers
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- torch
- code
---

# DistilBERT Fine-Tuned on IMDB Sentiment Analysis

This model is a fine-tuned version of `DistilBERT` for sentiment analysis on the IMDB movie reviews dataset. It classifies movie reviews into two categories: positive and negative sentiments.

## Model Details

### Model Description

This model has been fine-tuned on the IMDB dataset, which contains movie reviews labeled with sentiments: `positive` or `negative`. The model is based on the `DistilBERT` architecture, which is a lighter, more efficient variant of BERT, offering faster inference without significantly sacrificing accuracy.

- **Developed by:** Leonuraht/Scilineo
- **Model type:** Transformer-based model for text classification (sentiment analysis)
- **Language(s) (NLP):** English
- **Finetuned from model :** distilbert-base-uncased


## Uses

### Direct Use

This model is directly usable for sentiment analysis tasks. It predicts the sentiment of text by classifying it as either "positive" or "negative".

### Downstream Use [optional]

This model can be further fine-tuned for other text classification tasks or integrated into larger applications where sentiment analysis is required.

### Out-of-Scope Use

This model is not intended for multilingual sentiment analysis or for handling text outside of movie reviews. It may not perform well on domains with vastly different vocabularies or sentiment expression styles.

## Bias, Risks, and Limitations

The model has been trained on the IMDB movie reviews dataset, and as such, it may exhibit biases inherent in the data (e.g., biases in sentiment based on genre, culture, or language). It is important to be mindful of these limitations when using the model in real-world applications.

### Recommendations

Users should be aware of the model's biases and limitations. It is recommended to further fine-tune the model with a diverse dataset if it is to be used in domains beyond movie reviews.

## How to Get Started with the Model

To use the model for sentiment analysis, you can load it via the Hugging Face `transformers` library. Here's an example:

```python
from transformers import pipeline

# Load the fine-tuned model from Hugging Face
model = "Leonuraht/IMDBert"
classifier = pipeline("sentiment-analysis", model=model)

# Test the model with a sample text
result = classifier("This movie was amazing!")
print(result)  # Outputs: [{'label': 'POSITIVE' }]