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: 0.5834885164494104
- name: Recall
type: recall
value: 0.6555090655509066
- name: F1
type: f1
value: 0.6174055829228243
- name: Accuracy
type: accuracy
value: 0.9235426702611924
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: 0.2397
- Precision: 0.5835
- Recall: 0.6555
- F1: 0.6174
- Accuracy: 0.9235
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 | 0.2569 | 0.5378 | 0.6297 | 0.5801 | 0.9190 |
| 0.3194 | 2.0 | 626 | 0.2397 | 0.5835 | 0.6555 | 0.6174 | 0.9235 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3