Instructions to use wt3639/NER_skill_extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wt3639/NER_skill_extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wt3639/NER_skill_extractor")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wt3639/NER_skill_extractor") model = AutoModelForTokenClassification.from_pretrained("wt3639/NER_skill_extractor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from wt3639/NER_skill_extractor: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/wt3639/NER_skill_extractor/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://wt3639/NER_skill_extractor/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/wt3639/NER_skill_extractor/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- e78e8a82fd1bf70a867873b6767e60e212c4f134a38e6cded04a46d90d89851b
- Size of remote file:
- 1.34 GB
- SHA256:
- 88460ba7ed2c2f0ef0fdfd341fe4eb1a6fc0b1629cef1a37caf588b55daeb253
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