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 training_args.bin from mackseem/biored: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/mackseem/biored/resolve/main/training_args.bin
- Command line
-
hf download hf://mackseem/biored/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mackseem/biored/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- 1c52f10c63af4bcddda60c2527b939b4725eba522cca7055eba86d41d1352f94
- Size of remote file:
- 3.45 kB
- SHA256:
- e08bb5637dd41df2fb08da4bc023edec09a2e29f5deda7ede8a358e51836244a
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