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---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
base_model: klue/bert-base
model-index:
- name: bert-base-finetuned-ynat
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: klue
type: klue
config: ynat
split: train
args: ynat
metrics:
- type: f1
value: 0.871180664370084
name: F1
---
<!-- 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: 0.3609
- F1: 0.8712
## 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: 256
- eval_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 179 | 0.3979 | 0.8611 |
| No log | 2.0 | 358 | 0.3773 | 0.8669 |
| 0.3007 | 3.0 | 537 | 0.3609 | 0.8712 |
| 0.3007 | 4.0 | 716 | 0.3708 | 0.8708 |
| 0.3007 | 5.0 | 895 | 0.3720 | 0.8697 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|