metadata
base_model: klue/roberta-base
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: klue_ner_roberta_model
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: 0.7949828178694158
- name: Recall
type: recall
value: 0.8113207547169812
- name: F1
type: f1
value: 0.8030686985802062
- name: Accuracy
type: accuracy
value: 0.9595964075839893
klue_ner_roberta_model
This model is a fine-tuned version of klue/roberta-base on the klue dataset. It achieves the following results on the evaluation set:
- Loss: 0.1434
- Precision: 0.7950
- Recall: 0.8113
- F1: 0.8031
- Accuracy: 0.9596
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 |
|---|---|---|---|---|---|---|---|
| 0.1526 | 1.0 | 2626 | 0.1732 | 0.7105 | 0.7480 | 0.7288 | 0.9450 |
| 0.1019 | 2.0 | 5252 | 0.1395 | 0.7717 | 0.7894 | 0.7804 | 0.9566 |
| 0.0728 | 3.0 | 7878 | 0.1434 | 0.7950 | 0.8113 | 0.8031 | 0.9596 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3