---
library_name: transformers
license: mit
base_model: base-uncased
tags:
- bert
- fine-tuning
- text-classification
model-index:
- name: NLP_with_Disaster_Tweets
  results:
  - task:
      type: text-classification
      name: Text Classification
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.835
language:
- en
---

# Disaster Tweets Classification

This model is fine-tuned BERT for classifying whether a tweet is about a real disaster or not.

## Model Description

- Based on `bert-base-uncased`
- Fine-tuned for binary classification task
- Achieves 83.5% accuracy on validation set
- Trained on Kaggle's "Natural Language Processing with Disaster Tweets" competition dataset

## How to Use

```python
from transformers import AutoConfig, AutoTokenizer, AutoModelForSequenceClassification

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("real-jiakai/NLP_with_Disaster_Tweets")
model = AutoModelForSequenceClassification.from_pretrained("real-jiakai/NLP_with_Disaster_Tweets")

# Example usage
text = "There was a major earthquake in California"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(-1).item()
```

## License

This model is licensed under the [MIT](https://opensource.org/license/mit) License.

## Citation

If you use this model in your work, please cite:

```
@misc{NLP_with_Disaster_Tweets,
  author       = {real-jiakai},
  title        = {NLP_with_Disaster_Tweets},
  year         = {2024},
  url          = {https://huggingface.co/real-jiakai/NLP_with_Disaster_Tweets},
  publisher    = {Hugging Face}
}
```