Instructions to use attacker8324/tc271 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use attacker8324/tc271 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="attacker8324/tc271")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("attacker8324/tc271") model = AutoModelForTokenClassification.from_pretrained("attacker8324/tc271", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from attacker8324/tc271: direct link, hf CLI and curl.
- Browser
- Download file 380 kB
-
https://huggingface.co/attacker8324/tc271/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://attacker8324/tc271/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/attacker8324/tc271/resolve/main/pytorch_model.bin
380 kB
- Xet hash:
- edfd0a83cc156193d05e75a2203000633e3480cac8a8b7ccf25928d0c3da131a
- Size of remote file:
- 380 kB
- SHA256:
- 3df1e80ed9efe08ff7dc7bb18158aa852500f93f064bdcf60682e8bb0cea925a
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