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 optimizer.pt from wt3639/NER_skill_extractor: direct link, hf CLI and curl.
- Browser
- Download file 2.68 GB
-
https://huggingface.co/wt3639/NER_skill_extractor/resolve/main/optimizer.pt
- Command line
-
hf download hf://wt3639/NER_skill_extractor/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/wt3639/NER_skill_extractor/resolve/main/optimizer.pt
2.68 GB
- Xet hash:
- 99444dce2ec07f3abafa1300aeb40e6e75c44fb7498ce2275ce1e53146f276a8
- Size of remote file:
- 2.68 GB
- SHA256:
- 1c257e5b373b0b939801e338458560e991c838acc0612c4e84160446dac004ad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.