metadata
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token_classification
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: Precision
type: precision
value: 0.5973782771535581
- name: Recall
type: recall
value: 0.6673640167364017
- name: F1
type: f1
value: 0.6304347826086957
- name: Accuracy
type: accuracy
value: 0.9227913554602908
token_classification
This model is a fine-tuned version of klue/bert-base on the klue dataset. It achieves the following results on the evaluation set:
- Loss: 0.2382
- Precision: 0.5974
- Recall: 0.6674
- F1: 0.6304
- Accuracy: 0.9228
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 313 | 0.2539 | 0.5534 | 0.6395 | 0.5933 | 0.9205 |
| 0.3052 | 2.0 | 626 | 0.2382 | 0.5974 | 0.6674 | 0.6304 | 0.9228 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3