Instructions to use dhtocks/Named-Entity-Recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dhtocks/Named-Entity-Recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dhtocks/Named-Entity-Recognition")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dhtocks/Named-Entity-Recognition") model = AutoModelForTokenClassification.from_pretrained("dhtocks/Named-Entity-Recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dhtocks/Named-Entity-Recognition: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/dhtocks/Named-Entity-Recognition/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dhtocks/Named-Entity-Recognition/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dhtocks/Named-Entity-Recognition/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- f5dfbedf7fb0bf3be6a441d72ca5d441878e02e8467fa19231023017d2059cb5
- Size of remote file:
- 496 MB
- SHA256:
- cb4e428fa867576985563df92f79fdf2fbdac3de6ef3b6f216b70d4cdb242eb6
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