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")# 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
- Xet hash:
- d0582d420d63966145814f19f8fe751c4e20c47c6e37ee63ef9ee4f1cae5c0cb
- Size of remote file:
- 2.24 GB
- SHA256:
- 9855f894262da3db4e34c8e1773b22dba9e66b509bd460947896fe32d915322a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.