metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CoNLL2003_NER_BERT_Base_Cased
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9249506254114549
- name: Recall
type: recall
value: 0.9458094917536183
- name: F1
type: f1
value: 0.9352637710101515
- name: Accuracy
type: accuracy
value: 0.9854594690057102
CoNLL2003_NER_BERT_Base_Cased
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0571
- Precision: 0.9250
- Recall: 0.9458
- F1: 0.9353
- Accuracy: 0.9855
Model description
BERT Base Cased Model available at: bert-base-cased
Intended uses & limitations
Named Entity Recognition Task (English)
Training and evaluation data
CONLL2023 Dataset available at: conll2003 dataset
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.298 | 1.0 | 220 | 0.0792 | 0.8463 | 0.8995 | 0.8721 | 0.9769 |
| 0.0647 | 2.0 | 440 | 0.0617 | 0.9088 | 0.9362 | 0.9223 | 0.9830 |
| 0.0394 | 3.0 | 660 | 0.0574 | 0.9207 | 0.9443 | 0.9324 | 0.9846 |
| 0.0286 | 4.0 | 880 | 0.0559 | 0.9195 | 0.9438 | 0.9315 | 0.9855 |
| 0.0222 | 5.0 | 1100 | 0.0571 | 0.9250 | 0.9458 | 0.9353 | 0.9855 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1