Instructions to use mackseem/biored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mackseem/biored with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mackseem/biored")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mackseem/biored") model = AutoModelForTokenClassification.from_pretrained("mackseem/biored", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mackseem/biored: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/mackseem/biored/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mackseem/biored/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mackseem/biored/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- c8ad970aa07602e09e405e64aaa0ab3c142ae6ad75036fd4647eac74e5b7f747
- Size of remote file:
- 436 MB
- SHA256:
- 6e0df4c96610ff6c63b1c8354c41ceea5b506710f01ccd0a8772f351dcc3d4fb
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