Text Classification
Transformers
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-4k-s2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjbommar/mimelens-001-medium-bpe-4k-s2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mjbommar/mimelens-001-medium-bpe-4k-s2", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("mjbommar/mimelens-001-medium-bpe-4k-s2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40a73e112581492389ad3c9ea994ad2e3cd686863da68d9a8dd7428d783cfe97
- Size of remote file:
- 160 MB
- SHA256:
- 9cf5a04206e21ef42a3eb020cce76538023758b12cb253a315bf7ea2423d1133
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