Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use sagerpascal/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sagerpascal/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sagerpascal/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sagerpascal/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("sagerpascal/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sagerpascal/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/sagerpascal/bert-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://sagerpascal/bert-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sagerpascal/bert-finetuned-ner/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- b4e7d918ebd2bd763bc72ae3911f8b867f66f1b436516254833b8415d5bd7c47
- Size of remote file:
- 3.06 kB
- SHA256:
- 78ee28f100a6e6e6164477676cf1a364293f03c30c430dca80d603b8e1c5ce7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.