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: 0.9295401918623883
name: Precision
- type: recall
value: 0.9458094917536183
name: Recall
- type: f1
value: 0.9376042709376042
name: F1
- type: accuracy
value: 0.9848413492670866
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: 0.0673
- Precision: 0.9295
- Recall: 0.9458
- F1: 0.9376
- Accuracy: 0.9848
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 |
|---|---|---|---|---|---|---|---|
| 0.0846 | 1.0 | 1756 | 0.0660 | 0.9073 | 0.9344 | 0.9207 | 0.9820 |
| 0.0409 | 2.0 | 3512 | 0.0622 | 0.9230 | 0.9456 | 0.9342 | 0.9851 |
| 0.0202 | 3.0 | 5268 | 0.0673 | 0.9295 | 0.9458 | 0.9376 | 0.9848 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1