metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: bert-base-cased
model-index:
- name: bert-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- type: precision
value: 0.9392329403951519
name: Precision
- type: recall
value: 0.9520363513968361
name: Recall
- type: f1
value: 0.9455913079816131
name: F1
- type: accuracy
value: 0.9864308000235474
name: Accuracy
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: 0.0634
- Precision: 0.9392
- Recall: 0.9520
- F1: 0.9456
- Accuracy: 0.9864
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0866 | 1.0 | 1756 | 0.0736 | 0.9157 | 0.9322 | 0.9239 | 0.9816 |
| 0.0382 | 2.0 | 3512 | 0.0663 | 0.9326 | 0.9472 | 0.9398 | 0.9855 |
| 0.0226 | 3.0 | 5268 | 0.0634 | 0.9392 | 0.9520 | 0.9456 | 0.9864 |
Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3