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---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model_index:
- name: bert-base-finetuned-ynat
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: ynat
metric:
name: F1
type: f1
value: 0.8691323654981199
---
<!-- 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. -->
# bert-base-finetuned-ynat
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3636
- F1: 0.8691
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4713 | 1.0 | 714 | 0.3839 | 0.8670 |
| 0.3157 | 2.0 | 1428 | 0.3636 | 0.8691 |
| 0.2153 | 3.0 | 2142 | 0.3837 | 0.8657 |
| 0.1839 | 4.0 | 2856 | 0.4168 | 0.8629 |
| 0.132 | 5.0 | 3570 | 0.4334 | 0.8680 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
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