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model-index: |
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- name: distilbert-base-fire-class |
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results: [] |
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--- |
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# distilbert-base-fire-class |
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## Model description |
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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. |
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## Intended uses & limitations |
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### Intended Uses |
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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: |
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- Monitoring social media: Detecting and analyzing tweets or posts about fire incidents to help in early detection and response. |
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- News filtering: Identifying news articles or reports that focus on fire events for more efficient information processing. |
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- Emergency response systems: Assisting in categorizing incoming reports or alerts based on their relevance to fire-related topics. |
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- Research and analysis: Supporting studies on public reactions and discussions about fire incidents by classifying relevant texts. |
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### Limitations |
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While the model is effective in its intended use, there are several limitations to consider: |
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- 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. |
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- 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. |
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- 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. |
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## Training and evaluation data |
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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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.4456 | 1.0 | 368 | 0.4997 | |
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| 0.2904 | 2.0 | 736 | 0.6664 | |
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| 0.2408 | 3.0 | 1104 | 0.5989 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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