Instructions to use levalencia/FineTunedHateSpeechDistilBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use levalencia/FineTunedHateSpeechDistilBert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="levalencia/FineTunedHateSpeechDistilBert")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("levalencia/FineTunedHateSpeechDistilBert", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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license: cc0-1.0
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license: cc0-1.0
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language:
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- en
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metrics:
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- accuracy
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library_name: transformers
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pipeline_tag: text-classification
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# Model Card for levalencia/FineTunedHateSpeechDistilBert
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## Model Details
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### Model Description
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Hate Speech Model, it will classify text as Hate Speech (0), Offensive (1) or Neither(2)
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- **Developed by:** Luis Valencia
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- **Language(s) (NLP):** English
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- **License:** CCO
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- **Finetuned from model [optional]:** Distilbert
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### Model Sources
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- **Repository:** https://github.com/levalencia/DataScience-Portfolio/tree/main/FineTuningDistilbert
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- **Blog Post [optional]:** https://medium.com/python-in-plain-english/fine-tuning-distilbert-with-your-own-dataset-for-multi-classification-task-69f944189648
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