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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model-index:
- name: kobert-finetuned-klue-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: F1
type: f1
value: 0.26395413647583404
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kobert-finetuned-klue-ner
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4238
- F1: 0.2640
## 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: 5e-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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5975 | 1.0 | 1313 | 0.5314 | 0.1794 |
| 0.4068 | 2.0 | 2626 | 0.4611 | 0.2331 |
| 0.3366 | 3.0 | 3939 | 0.4264 | 0.2598 |
| 0.2933 | 4.0 | 5252 | 0.4238 | 0.2640 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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