BERT-base-cased fine-tuned on OntoNotes 5.0

This model is a fine-tuned version of google-bert/bert-base-cased on the English subset of the OntoNotes 5.0 (CoNLL-2012) dataset. It is designed for Named Entity Recognition (NER) and can identify 18 types of entities.

📊 Performance

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

🛠 Training Details

• Architecture: BertForTokenClassification
• Tokenizer: BertTokenizerFast (using is_split_into_words=True for alignment)
• Epochs: 5
• Learning Rate: 2e-5
• Batch Size: 16 per device (Total 32 on 2x V100 GPUs)
• Max Sequence Length: 128
• Weight Decay: 0.01
• Mixed Precision (FP16): Enabled

📂 Labels Mapping

The model was trained with the following label mapping (18 OntoNotes entities + BIO tags): 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 | Main checkpoint in Safetensors format (safe, fast loading, ~431 MB). | | Configuration | config.json | Model architecture settings and id2label entity mapping. | | Vocabulary | vocab.txt | BERT-cased WordPiece vocabulary for tokenization. | | Tokenizer | tokenizer.json / tokenizer_config.json | Optimized fast tokenizer configuration and serialization. | | Special Tokens | special_tokens_map.json | Definitions for special tokens like [CLS], [SEP], etc. | | Training Args | training_args.bin | Detailed hyperparameter settings used during the training run. |

🚀 Usage

You can use this model directly with a pipeline for token classification:

from transformers import pipeline

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

text = "Apple was founded by Steve Jobs in Cupertino."
results = token_classifier(text)

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