Text Classification
Transformers
Safetensors
English
magic-bert
binary-analysis
file-type-detection
byte-level
classification
mime-type
security
custom_code
Eval Results (legacy)
Instructions to use mjbommar/magic-bert-50m-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjbommar/magic-bert-50m-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mjbommar/magic-bert-50m-classification", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("mjbommar/magic-bert-50m-classification", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload magic-bert-50m-classification model files
Browse files
README.md
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## Related Models
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- **magic-bert-50m-mlm**: Base model before classification fine-tuning
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- **magic-bert-50m-roformer-mlm**: RoFormer variant with rotary position embeddings
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- **magic-bert-50m-roformer-classification**: RoFormer variant with higher classification accuracy (93.7%, recommended for production)
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## Related Work
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## Related Models
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- **[magic-bert-50m-mlm](https://huggingface.co/mjbommar/magic-bert-50m-mlm)**: Base model before classification fine-tuning
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- **[magic-bert-50m-roformer-mlm](https://huggingface.co/mjbommar/magic-bert-50m-roformer-mlm)**: RoFormer variant with rotary position embeddings
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- **[magic-bert-50m-roformer-classification](https://huggingface.co/mjbommar/magic-bert-50m-roformer-classification)**: RoFormer variant with higher classification accuracy (93.7%, recommended for production)
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## Related Work
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