Instructions to use jacksontran/bert-base-uncased-conll2003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jacksontran/bert-base-uncased-conll2003 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jacksontran/bert-base-uncased-conll2003")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jacksontran/bert-base-uncased-conll2003") model = AutoModelForTokenClassification.from_pretrained("jacksontran/bert-base-uncased-conll2003") - Notebooks
- Google Colab
- Kaggle
bert-base-uncased-conll2003
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset.
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Evaluation results
- Precision on conll2003test set self-reported0.886
- Recall on conll2003test set self-reported0.908
- F1 on conll2003test set self-reported0.897
- Accuracy on conll2003test set self-reported0.979