ZaharHR/bert-conll2003-ner

BERT fine-tuned on CoNLL-2003 (NER)

bert-base-cased fine-tuned for Named Entity Recognition on CoNLL-2003.

Recognizes 4 entity types: PER, ORG, LOC, MISC.

Evaluation results

Metric	Score
Precision	0.7058
Recall	0.5080
F1	0.5908
Accuracy	0.9015

Evaluated with seqeval on the CoNLL-2003 test split.

Usage

from transformers import pipeline

ner = pipeline("ner", model="ZaharHR/bert-conll2003-ner", aggregation_strategy="simple")
ner("Elon Musk founded SpaceX in California.")

Training details

• Base model: bert-base-cased
• Dataset: CoNLL-2003
• Epochs: 1
• Effective batch size: 16 (gradient accumulation)
• Optimizer: AdamW, weight decay 0.01
• Warmup steps: 500

Label scheme

O, B-PER, I-PER, B-ORG, I-ORG, B-LOC, I-LOC, B-MISC, I-MISC