Instructions to use Shrav20/job-ner-deberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shrav20/job-ner-deberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Shrav20/job-ner-deberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Shrav20/job-ner-deberta") model = AutoModelForTokenClassification.from_pretrained("Shrav20/job-ner-deberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Shrav20/job-ner-deberta: direct link, hf CLI and curl.
- Browser
- Download file 8.65 MB
-
https://huggingface.co/Shrav20/job-ner-deberta/resolve/main/tokenizer.json
- Command line
-
hf download hf://Shrav20/job-ner-deberta/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/Shrav20/job-ner-deberta/resolve/main/tokenizer.json
8.65 MB
File too large to display, you can check the raw version instead.