metadata
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: klue-roberta-large-ner
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: 0.7881991814461119
- name: Recall
type: recall
value: 0.8104790629164621
- name: F1
type: f1
value: 0.7991838710792959
- name: Accuracy
type: accuracy
value: 0.9590597627231401
klue-roberta-large-ner
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: 0.1432
- Precision: 0.7882
- Recall: 0.8105
- F1: 0.7992
- Accuracy: 0.9591
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 |
---|---|---|---|---|---|---|---|
0.1585 | 1.0 | 2626 | 0.1648 | 0.7517 | 0.7499 | 0.7508 | 0.9489 |
0.1092 | 2.0 | 5252 | 0.1457 | 0.7776 | 0.7909 | 0.7842 | 0.9557 |
0.0714 | 3.0 | 7878 | 0.1432 | 0.7882 | 0.8105 | 0.7992 | 0.9591 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.2