Instructions to use Kimty/Ner_custom_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kimty/Ner_custom_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Kimty/Ner_custom_data")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Kimty/Ner_custom_data") model = AutoModelForTokenClassification.from_pretrained("Kimty/Ner_custom_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Kimty/Ner_custom_data: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/Kimty/Ner_custom_data/resolve/main/training_args.bin
- Command line
-
hf download hf://Kimty/Ner_custom_data/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Kimty/Ner_custom_data/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- da4ed450aa325a01fabdd9722f74fd89b54b82862cde0ff33bf6e21a83557314
- Size of remote file:
- 5.24 kB
- SHA256:
- 1523e2806e210338e87d2ffbbe1d42d47d637430ae84cdccca80feb2cc54d4c2
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