---
model-index:
- name: distilbert-base-fire-class
  results: []
---


# distilbert-base-fire-class


## Model description

The distilbert-base-fire-class model is a distilled version of BERT, specifically fine-tuned for classification tasks related to the dataset on tweets about fire incidents. This model can be used for analyzing and classifying tweets that mention fire-related events or discussions.

## Intended uses & limitations

### Intended Uses
The distilbert-base-fire-class model is specifically designed for distinguishing whether a text is discussing fire-related incidents or not. It can be applied in various contexts such as:
- Monitoring social media: Detecting and analyzing tweets or posts about fire incidents to help in early detection and response.
- News filtering: Identifying news articles or reports that focus on fire events for more efficient information processing.
- Emergency response systems: Assisting in categorizing incoming reports or alerts based on their relevance to fire-related topics.
- Research and analysis: Supporting studies on public reactions and discussions about fire incidents by classifying relevant texts.

### Limitations
While the model is effective in its intended use, there are several limitations to consider:
- Dataset Specificity: The model is trained on a specific dataset of tweets about fire, which may limit its accuracy when applied to other types of texts or platforms.
- Language and Context: The model may not perform as well on texts that include slang, regional dialects, or highly context-specific language that was not well-represented in the training data.
- Generalization: This model is focused on fire-related text classification and may not generalize well to other topics without further fine-tuning or additional training data.

## Training and evaluation data

The model was trained using the [tweets_about_fire](https://huggingface.co/datasets/JudeChaer/tweets_about_fire) dataset. This dataset consists of tweets specifically mentioning fire incidents, providing a focused context for training the classification model.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4456        | 1.0   | 368  | 0.4997          |
| 0.2904        | 2.0   | 736  | 0.6664          |
| 0.2408        | 3.0   | 1104 | 0.5989          |

### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

---