Instructions to use graphcore-rahult/bert-base-uncased-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use graphcore-rahult/bert-base-uncased-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="graphcore-rahult/bert-base-uncased-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("graphcore-rahult/bert-base-uncased-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("graphcore-rahult/bert-base-uncased-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from graphcore-rahult/bert-base-uncased-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 2.74 kB
-
https://huggingface.co/graphcore-rahult/bert-base-uncased-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://graphcore-rahult/bert-base-uncased-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/graphcore-rahult/bert-base-uncased-finetuned-ner/resolve/main/training_args.bin
2.74 kB
- Xet hash:
- 20ffa594b25353cb2bca9920a7238585b4792de1031dc907d3e86c371f8fa0b6
- Size of remote file:
- 2.74 kB
- SHA256:
- 941748645259e4a9e04fa517703d9b1d3e701a5d192f13e4b32935a850e9d15a
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