Instructions to use jjzha/escoxlmr_knowledge_extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jjzha/escoxlmr_knowledge_extraction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jjzha/escoxlmr_knowledge_extraction")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jjzha/escoxlmr_knowledge_extraction") model = AutoModelForTokenClassification.from_pretrained("jjzha/escoxlmr_knowledge_extraction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from jjzha/escoxlmr_knowledge_extraction: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/jjzha/escoxlmr_knowledge_extraction/resolve/main/tokenizer.json
- Command line
-
hf download hf://jjzha/escoxlmr_knowledge_extraction/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/jjzha/escoxlmr_knowledge_extraction/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- f512c789411bcc1ac9f24c5f74bb2a3a795247cb96832019d34ea167adac2a94
- Size of remote file:
- 17.1 MB
- SHA256:
- 384f4900cbdbb808269c3d858ed426e2d66bdf41a5da49bbd92bda0e31d7ea8b
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