Instructions to use Xiaoman/NER-CoNLL2003-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Xiaoman/NER-CoNLL2003-V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Xiaoman/NER-CoNLL2003-V2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Xiaoman/NER-CoNLL2003-V2") model = AutoModelForTokenClassification.from_pretrained("Xiaoman/NER-CoNLL2003-V2") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Training hyperparameters The following hyperparameters were used during training:
learning_rate: 7.961395091713594e-05 train_batch_size: 32 eval_batch_size: 32 seed: 27 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 5
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