metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: ner_conll2003
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9772880710440217
ner_conll2003
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1495
- Precision: 0.8985
- Recall: 0.9130
- F1: 0.9057
- Accuracy: 0.9773
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: 3e-05
- train_batch_size: 16
- 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_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.423 | 1.0 | 877 | 0.0656 | 0.9158 | 0.9268 | 0.9213 | 0.9818 |
| 0.0575 | 2.0 | 1754 | 0.0574 | 0.9285 | 0.9445 | 0.9364 | 0.9847 |
| 0.0295 | 3.0 | 2631 | 0.0631 | 0.9414 | 0.9456 | 0.9435 | 0.9859 |
| 0.0155 | 4.0 | 3508 | 0.0680 | 0.9395 | 0.9467 | 0.9431 | 0.9860 |
| 0.0097 | 5.0 | 4385 | 0.0694 | 0.9385 | 0.9513 | 0.9449 | 0.9863 |
| 0.0059 | 6.0 | 5262 | 0.0743 | 0.9363 | 0.9471 | 0.9416 | 0.9860 |
| 0.0041 | 7.0 | 6139 | 0.0803 | 0.9371 | 0.9518 | 0.9444 | 0.9862 |
Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.2