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
| { | |
| "mimelens_release": "001", | |
| "cell_id": "tiny/byte/s1", | |
| "ckpt_source": "/data0/binary-embedding/phase-b/runs/tiny/byte/s1/checkpoints/best.safetensors", | |
| "ckpt_sha256": "c0c14090d9c21d55fb3a114e603655631171a9a553bab910bbfa6d50611f5aea", | |
| "magicfiles_top1": 0.740234375, | |
| "magicfiles_f1": 0.6016066453075543, | |
| "magicfrags_top1": 0.740234375, | |
| "magicfrags_f1": 0.6016066453075543, | |
| "params_m": 3.15 | |
| } |