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learnrr/
bert-large-ontonotes5-ner

Name: bert-large-ontonotes5-ner
Availability: InStock

safetensors

bert

token-classification

ontonotes/conll2012_ontonotesv5

google-bert/bert-large-cased

finetune

mit

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BERT-large-cased fine-tuned on OntoNotes 5.0

This model is a fine-tuned version of google-bert/bert-large-cased on the English subset of the OntoNotes 5.0 (CoNLL-2012) dataset. It is optimized for high-precision Named Entity Recognition (NER) across 18 entity categories.

📊 Performance

The model achieves the following results on the OntoNotes 5.0 test set:

Entity	Precision	Recall	F1-Score	Support
CARDINAL	0.7813	0.7891	0.7851	1005
DATE	0.7988	0.8516	0.8244	1786
EVENT	0.5619	0.6941	0.6211	85
FAC	0.6880	0.5772	0.6277	149
GPE	0.9185	0.9207	0.9196	2546
LANGUAGE	0.8421	0.7273	0.7805	22
LAW	0.4762	0.6818	0.5607	44
LOC	0.6337	0.7163	0.6725	215
MONEY	0.8636	0.9099	0.8861	355
NORP	0.8481	0.8909	0.8690	990
ORDINAL	0.7054	0.7633	0.7332	207
ORG	0.8690	0.9046	0.8864	2002
PERCENT	0.8467	0.8821	0.8640	407
PERSON	0.9090	0.9217	0.9153	2134
PRODUCT	0.6667	0.6889	0.6776	90
QUANTITY	0.6972	0.6471	0.6712	153
TIME	0.6106	0.6133	0.6120	225
WORK_OF_ART	0.6354	0.6805	0.6571	169
micro avg	0.8412	0.8675	0.8542	12584
macro avg	0.7418	0.7700	0.7535	12584
weighted avg	0.8427	0.8675	0.8546	12584

🛠 Training Details

Architecture: BertForTokenClassification (Large)
Tokenizer: BertTokenizerFast (using is_split_into_words=True)
Epochs: 5
Learning Rate: 1e-5
Batch Size: 4 per device (2x V100 GPUs)
Gradient Accumulation: 4 steps (Effective Batch Size = 32)
Max Sequence Length: 128
Weight Decay: 0.01
Mixed Precision (FP16): Enabled

📂 Labels Mapping

The model identifies 18 entity types from OntoNotes 5.0: CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART.

📂 Project Assets

GitHub Repository: https://github.com/Learnrr/ontonotes5_ner_evaluation.git

Asset	File	Description
Model Weights	`model.safetensors`	Large-scale checkpoint (~1.2 GB).
Configuration	`config.json`	Model architecture & `id2label` mapping.
Vocabulary	`vocab.txt`	BERT-cased specific vocabulary.
Tokenizer	`tokenizer.json`	Optimized fast tokenizer configuration.
Special Tokens	`special_tokens_map.json`	Definitions for BOS, EOS, and Padding tokens.
Training Args	`training_args.bin`	Detailed hyperparameter dump from the Trainer.

🚀 Usage

python

from transformers import pipeline

model_checkpoint = "learnrr/bert-large-ontonotes5-ner"
token_classifier = pipeline(
    "token-classification", 
    model=model_checkpoint, 
    aggregation_strategy="simple"
)

text = "The United Nations is headquartered in New York City."
results = token_classifier(text)

for entity in results:
    print(f"Entity: {entity['word']} | Label: {entity['entity_group']} | Score: {entity['score']:.4f}")