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 pytorch_model.bin from graphcore-rahult/bert-base-uncased-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 218 MB
-
https://huggingface.co/graphcore-rahult/bert-base-uncased-finetuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://graphcore-rahult/bert-base-uncased-finetuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/graphcore-rahult/bert-base-uncased-finetuned-ner/resolve/main/pytorch_model.bin
218 MB
- Xet hash:
- 297d9c214318a9d25b6e1bad308abb2713ac96238f4c0cc0f1afe8063dcd5e09
- Size of remote file:
- 218 MB
- SHA256:
- b1488a45dbea7292aa1ebe3f3bdfd94786df1ae583f7ef911ab319a555068179
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