metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: koelectra-base-v3-discriminator-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
args: ner
metrics:
- name: Precision
type: precision
value: 0.6665182546749777
- name: Recall
type: recall
value: 0.7350073648032546
- name: F1
type: f1
value: 0.6990893625537877
- name: Accuracy
type: accuracy
value: 0.9395764497172635
koelectra-base-v3-discriminator-finetuned-ner
This model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on the klue dataset. It achieves the following results on the evaluation set:
- Loss: 0.1957
- Precision: 0.6665
- Recall: 0.7350
- F1: 0.6991
- Accuracy: 0.9396
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: 48
- eval_batch_size: 48
- 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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 438 | 0.2588 | 0.5701 | 0.6655 | 0.6141 | 0.9212 |
| 0.4333 | 2.0 | 876 | 0.2060 | 0.6671 | 0.7134 | 0.6895 | 0.9373 |
| 0.1944 | 3.0 | 1314 | 0.1957 | 0.6665 | 0.7350 | 0.6991 | 0.9396 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0+cu102
- Datasets 1.14.0
- Tokenizers 0.10.3