Text Classification
Transformers
ONNX
Safetensors
English
mimelens
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-medium-bpe-16k-s1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjbommar/mimelens-001-medium-bpe-16k-s1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mjbommar/mimelens-001-medium-bpe-16k-s1", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("mjbommar/mimelens-001-medium-bpe-16k-s1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "mimelens_release": "001", | |
| "cell_id": "medium/bpe-16k/s1", | |
| "ckpt_source": "/data0/binary-embedding/phase-b/runs/medium/bpe-16k/s1/checkpoints/best.safetensors", | |
| "ckpt_sha256": "c5d88f07f6db988db23d79246603ae3c208552eb15c72a78a51aa642c0c73f04", | |
| "magicfiles_top1": 0.83203125, | |
| "magicfiles_f1": 0.7237467507224439, | |
| "magicfrags_top1": 0.798828125, | |
| "magicfrags_f1": 0.6374760391685423, | |
| "params_m": 37.76 | |
| } |