Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use Helenn25/bert-ner-conll2003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helenn25/bert-ner-conll2003 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Helenn25/bert-ner-conll2003")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Helenn25/bert-ner-conll2003") model = AutoModelForTokenClassification.from_pretrained("Helenn25/bert-ner-conll2003", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Helenn25/bert-ner-conll2003: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Helenn25/bert-ner-conll2003/resolve/main/training_args.bin
- Command line
-
hf download hf://Helenn25/bert-ner-conll2003/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Helenn25/bert-ner-conll2003/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 697a7c9545f4f02bcfc3209c10e28636621140c51dc46c78f5e279706ed02c28
- Size of remote file:
- 5.11 kB
- SHA256:
- 30c2049abbe3b0968c281882fb3e96b68fbea21d747de4f36efc2decd1a58c86
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.