Text Classification
Transformers
Safetensors
English
mimelens
feature-extraction
file-type-detection
mime-classification
binary-content
binary-analysis
position-agnostic
libmagic
forensics
packet-inspection
bpe
byte-pair-encoding
custom_code
Eval Results (legacy)
Instructions to use mjbommar/mimelens-001-tiny-bpe-64k-s2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjbommar/mimelens-001-tiny-bpe-64k-s2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mjbommar/mimelens-001-tiny-bpe-64k-s2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mjbommar/mimelens-001-tiny-bpe-64k-s2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- abdcfbf406b9d2d7cf824a7d4417d72a605a4f514abdcec22b5d839cc1c35706
- Size of remote file:
- 80 MB
- SHA256:
- b2c5a5c935b300ad14eb2fd5efcb39dc2af784aa8fc40ca5ac7f3d8ed5b5d8ae
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