Instructions to use zagibest/roberta-base-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zagibest/roberta-base-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="zagibest/roberta-base-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("zagibest/roberta-base-ner") model = AutoModelForTokenClassification.from_pretrained("zagibest/roberta-base-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from zagibest/roberta-base-ner: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/zagibest/roberta-base-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://zagibest/roberta-base-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/zagibest/roberta-base-ner/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- c1d5aa035293b97dba3d4befb1bf36d759e73ffa57e2ee360e491cf8339bf1ec
- Size of remote file:
- 496 MB
- SHA256:
- 9b4dde67b56ed891119fc319818fd40f3dedc6de93461074bd357f1bd5bbcc54
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