Instructions to use romanenko02/ner-hw2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use romanenko02/ner-hw2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="romanenko02/ner-hw2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("romanenko02/ner-hw2") model = AutoModelForTokenClassification.from_pretrained("romanenko02/ner-hw2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from romanenko02/ner-hw2: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/romanenko02/ner-hw2/resolve/main/training_args.bin
- Command line
-
hf download hf://romanenko02/ner-hw2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/romanenko02/ner-hw2/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- e05ca9502dafa90b32d7319fff4f5299ec3c44a906b9df1b5ddeb0239b04e630
- Size of remote file:
- 5.2 kB
- SHA256:
- 3258536311bbc542e563048e8b84d31e2b90599ef0073e20d9f7538e8048d3c9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.