bert-base-cased-finetuned-conll2003-ner-v2
BERT ("bert-base-cased") finetuned on CoNLL-2003 (Conference on Computational Natural Language Learning).
The model performs named entity recognition (NER). It pertains to section 2 of chapter 7 of the Hugging Face "NLP Course" (https://huggingface.co/learn/nlp-course/chapter7/2).
It was trained using a custom PyTorch loop with Hugging Face Accelerate.
Experiment tracking: https://wandb.ai/sadhaklal/bert-base-cased-finetuned-conll2003-ner-v2
Usage
from transformers import pipeline
model_checkpoint = "sadhaklal/bert-base-cased-finetuned-conll2003-ner-v2"
token_classifier = pipeline("token-classification", model=model_checkpoint, aggregation_strategy="simple")
print(token_classifier("My name is Sylvain and I work at Hugging Face in Brooklyn."))
Dataset
From the dataset page:
The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups.
Examples: https://huggingface.co/datasets/conll2003/viewer
Metrics
Accuracy on the 'validation' split of CoNLL-2003: 0.9858
Precision on the 'validation' split of CoNLL-2003: 0.9243
Recall on the 'validation' split of CoNLL-2003: 0.947
F1 on the 'validation' split of CoNLL-2003: 0.9355
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