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: bert-finetuned-ner
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: 0.9345484672742337
- name: Recall
type: recall
value: 0.9491753618310333
- name: F1
type: f1
value: 0.941805126492444
- name: Accuracy
type: accuracy
value: 0.9858862659680933
bert-finetuned-ner
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: 0.0635
- Precision: 0.9345
- Recall: 0.9492
- F1: 0.9418
- Accuracy: 0.9859
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
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: 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.076 | 1.0 | 1756 | 0.0621 | 0.9058 | 0.9354 | 0.9204 | 0.9820 |
| 0.0363 | 2.0 | 3512 | 0.0629 | 0.9349 | 0.9482 | 0.9415 | 0.9857 |
| 0.0232 | 3.0 | 5268 | 0.0635 | 0.9345 | 0.9492 | 0.9418 | 0.9859 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0