Text Classification
Transformers
Safetensors
English
mimelens
feature-extraction
file-type-detection
mime-classification
binary-content
binary-analysis
position-agnostic
libmagic
forensics
packet-inspection
byte-level
custom_code
Eval Results (legacy)
Instructions to use mjbommar/mimelens-001-tiny-byte-s1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjbommar/mimelens-001-tiny-byte-s1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mjbommar/mimelens-001-tiny-byte-s1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mjbommar/mimelens-001-tiny-byte-s1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6283d05c5e39ff8b414834d1fe0531b980c17a4ea04587b4510d6e76306cf0e6
- Size of remote file:
- 13.1 MB
- SHA256:
- c0c14090d9c21d55fb3a114e603655631171a9a553bab910bbfa6d50611f5aea
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