---
library_name: transformers
datasets:
- stanfordnlp/imdb
metrics:
- accuracy
tags:
- PyTorch
model-index:
- name: distilbert-imdb
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: imdb
      type: imdb
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9316
pipeline_tag: text-classification
license: apache-2.0
language:
- en
---
# distilbert-imdb

This is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on imdb dataset.

## Performance
- Loss: 0.1958
- Accuracy: 0.932

## How to Get Started with the Model

Use the code below to get started with the model:

```python
from transformers import pipeline,DistilBertTokenizer

tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
classifier = pipeline("sentiment-analysis", model="3oclock/distilbert-imdb", tokenizer=tokenizer)
result = classifier("I love this movie!")
print(result)
```
## Model Details

### Model Description

This is the model card for a fine-tuned 🤗 transformers model on the IMDb dataset.

- **Developed by:** Ge Li
- **Model type:** DistilBERT for Sequence Classification
- **Language(s) (NLP):** English
- **License:** [Specify License, e.g., Apache 2.0]
- **Finetuned from model:** `distilbert-base-uncased`


## Uses

### Direct Use

This model can be used directly for sentiment analysis on movie reviews. It is best suited for classifying English-language text that is similar in nature to movie reviews.

### Downstream Use [optional]

This model can be fine-tuned on other sentiment analysis tasks or adapted for tasks like text classification in domains similar to IMDb movie reviews.

### Out-of-Scope Use

The model may not perform well on non-English text or text that is significantly different in style and content from the IMDb dataset (e.g., technical documents, social media posts).

## Bias, Risks, and Limitations

### Bias

The IMDb dataset primarily consists of English-language movie reviews and may not generalize well to other languages or types of reviews.

### Risks

Misclassification in sentiment analysis can lead to incorrect conclusions in applications relying on this model.

### Limitations

The model was trained on a dataset of movie reviews, so it may not perform as well on other types of text data.

### Recommendations

Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model.