Instructions to use dexay/f_ner_rober with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dexay/f_ner_rober with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dexay/f_ner_rober")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dexay/f_ner_rober") model = AutoModelForTokenClassification.from_pretrained("dexay/f_ner_rober", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dexay/f_ner_rober: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/dexay/f_ner_rober/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dexay/f_ner_rober/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dexay/f_ner_rober/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- d98d529e89e73eabd2fa9cca84cff54c4b0257a127025cc8f94af5cc98e9f1b9
- Size of remote file:
- 496 MB
- SHA256:
- 63c6caa031d904507cc3daa25b91d45cc3f74c96e2761bee7127886c1d9d9b8b
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