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license: apache-2.0 |
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pipeline_tag: text-classification |
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--- |
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# Model Card: Fine-Tuned DistilBERT for Offensive/Hate Speech Detection |
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## Model Description |
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The **Fine-Tuned DistilBERT** is a variant of the BERT transformer model, |
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distilled for efficient performance while maintaining high accuracy. |
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It has been adapted and fine-tuned for the specific task of offensive/hate speech detection in text data. |
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The model, named "distilbert-base-uncased," is pre-trained on a substantial amount of text data, |
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which allows it to capture semantic nuances and contextual information present in natural language text. |
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It has been fine-tuned with meticulous attention to hyperparameter settings, including batch size and learning rate, to ensure optimal model performance for the offensive/hate speech detection task. |
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During the fine-tuning process, a batch size of 16 for efficient computation and learning was chosen. |
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Additionally, a learning rate (2e-5) was selected to strike a balance between rapid convergence and steady optimization, |
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ensuring the model not only learns quickly but also steadily refines its capabilities throughout training. |
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This model has been trained on a proprietary dataset < 100k, specifically designed for offensive/hate speech detection. |
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The dataset consists of text samples, each labeled as "non-offensive" or "offensive." |
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The diversity within the dataset allowed the model to learn to identify offensive content accurately. |
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The goal of this meticulous training process is to equip the model with the ability to detect offensive and hate speech in text data effectively. The result is a model ready to contribute significantly to content moderation and safety, while maintaining high standards of accuracy and reliability. |
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## Intended Uses & Limitations |
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### Intended Uses |
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- **Offensive/Hate Speech Detection**: The primary intended use of this model is to detect offensive or hate speech in text data. It is well-suited for filtering and identifying inappropriate content in various applications. |
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### How to Use |
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To use this model for offensive/hate speech detection, you can follow these steps: |
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```markdown |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="Falconsai/offensive_speech_detection") |
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text = "Your text to classify here." |
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result = classifier(text) |
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``` |
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### Limitations |
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- **Specialized Task Fine-Tuning**: While the model is adept at offensive/hate speech detection, its performance may vary when applied to other natural language processing tasks. |
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- Users interested in employing this model for different tasks should explore fine-tuned versions available in the model hub for optimal results. |
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## Training Data |
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The model's training data includes a proprietary dataset designed for offensive/hate speech detection. This dataset comprises a diverse collection of text samples, categorized into "non-offensive" and "offensive" classes. The training process aimed to equip the model with the ability to distinguish between offensive and non-offensive content effectively. |
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### Training Stats |
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- Evaluation Loss: 0.018403256312012672 |
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- Evaluation Accuracy: 0.9973234886940471 |
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- Evaluation Runtime: 85.0789 |
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- Evaluation Samples per Second: 127.352 |
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- Evaluation Steps per Second: 7.969 |
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**Note:** Specific evaluation statistics should be provided based on the model's performance. |
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## Responsible Usage |
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It is essential to use this model responsibly and ethically, adhering to content guidelines and applicable regulations when implementing it in real-world applications, particularly those involving potentially sensitive content. |
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## References |
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- [Hugging Face Model Hub](https://huggingface.co/models) |
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- [DistilBERT Paper](https://arxiv.org/abs/1910.01108) |
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**Disclaimer:** The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets. |
