Instructions to use leemeng/core-term-ner-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leemeng/core-term-ner-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="leemeng/core-term-ner-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("leemeng/core-term-ner-v1") model = AutoModelForTokenClassification.from_pretrained("leemeng/core-term-ner-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from leemeng/core-term-ner-v1: direct link, hf CLI and curl.
- Browser
- Download file 407 MB
-
https://huggingface.co/leemeng/core-term-ner-v1/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://leemeng/core-term-ner-v1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/leemeng/core-term-ner-v1/resolve/main/flax_model.msgpack
407 MB
- Xet hash:
- d148e149cd4be4f548c4bef430d4d52e11ae3c70041f6129553bba0ff57b61bf
- Size of remote file:
- 407 MB
- SHA256:
- 2b9649bc0bbb8707c23c2e53152a01dd927793dcfc76c538f4c83ddeddad03c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.