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-small-byte-s2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjbommar/mimelens-001-small-byte-s2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mjbommar/mimelens-001-small-byte-s2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mjbommar/mimelens-001-small-byte-s2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b01ecd737b9b46876965249d7f8aa31eb12ee376e457f9bcb6e4af05d878f97
- Size of remote file:
- 57.5 MB
- SHA256:
- 1f1aba49b7aea6a62a7f3d57175049193a70c0087bcd717a37766576db585734
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