This is a token classification model fine-tuned for Named Entity Recognition (NER) on the CoNLL-2003 dataset, built on the bert-base-uncased architecture. It identifies entities like people, organizations, and locations in text. Optimized for CPU use. Uploaded by harpertoken.

Intended Uses & Limitations

Intended Uses

Extracting named entities from unstructured text (e.g., news articles, reports)
Powering NLP pipelines on CPU-based systems
Research or lightweight production use

Limitations

Trained on English text from CoNLL-2003, so it may not generalize well to other languages or domains
Uses bert-base-uncased tokenization (lowercase-only), potentially missing case-sensitive nuances
Optimized for NER; additional tuning needed for other token-classification tasks

Training and Evaluation Data

The model was trained and evaluated on the CoNLL-2003 dataset, a standard NER benchmark. It features annotated English news articles with entities like persons, organizations, and locations, split into training, validation, and test sets. Metrics here reflect the evaluation subset.

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3

Training Results

Training Loss	Epoch	Step	Validation Loss
0.048	1.0	1756	0.0531
0.0251	2.0	3512	0.0473
0.016	3.0	5268	0.0474

Framework Versions

Transformers: 4.28.1
PyTorch: 2.0.1
Datasets: 1.18.3
Tokenizers: 0.13.3

bniladridas/token-classification-ai-fine-tune

harpertokenNER

Model Description