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: 0.9346675487925902
- name: Recall
type: recall
value: 0.9510265903736116
- name: F1
type: f1
value: 0.9427761094427761
- name: Accuracy
type: accuracy
value: 0.9862983457938423
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: 0.0614
- Precision: 0.9347
- Recall: 0.9510
- F1: 0.9428
- Accuracy: 0.9863
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 |
|---|---|---|---|---|---|---|---|
| 0.0753 | 1.0 | 1756 | 0.0631 | 0.9104 | 0.9374 | 0.9237 | 0.9819 |
| 0.0348 | 2.0 | 3512 | 0.0622 | 0.9309 | 0.9478 | 0.9393 | 0.9854 |
| 0.0213 | 3.0 | 5268 | 0.0614 | 0.9347 | 0.9510 | 0.9428 | 0.9863 |
Framework versions
- Transformers 4.53.0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2