Instructions to use beyhan/checkpoint-3750 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beyhan/checkpoint-3750 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="beyhan/checkpoint-3750")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("beyhan/checkpoint-3750") model = AutoModelForTokenClassification.from_pretrained("beyhan/checkpoint-3750", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from beyhan/checkpoint-3750: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
-
https://huggingface.co/beyhan/checkpoint-3750/resolve/main/training_args.bin
- Command line
-
hf download hf://beyhan/checkpoint-3750/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/beyhan/checkpoint-3750/resolve/main/training_args.bin
1.03 kB
- Xet hash:
- 967ba054574d7a517938f2ec164d2aadab3ade192cc9d8503216dfac0f093c89
- Size of remote file:
- 1.03 kB
- SHA256:
- 979d5ed54fe9a526f7ad12de16c8e47ae37fa75b4edc84a3d9265f27e26f31f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.