---
license: apache-2.0
base_model: line-corporation/line-distilbert-base-japanese
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: factual-consistency-classification-ja-avgpool
  results: []
---

<!-- 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. -->

# factual-consistency-classification-ja-avgpool

This model is a fine-tuned version of [line-corporation/line-distilbert-base-japanese](https://huggingface.co/line-corporation/line-distilbert-base-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4881
- Accuracy: 0.8223

## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 306  | 0.6837          | 0.7402   |
| 0.7763        | 2.0   | 612  | 0.6102          | 0.7734   |
| 0.7763        | 3.0   | 918  | 0.5782          | 0.7832   |
| 0.657         | 4.0   | 1224 | 0.5698          | 0.7949   |
| 0.6267        | 5.0   | 1530 | 0.5743          | 0.7793   |
| 0.6267        | 6.0   | 1836 | 0.5465          | 0.8066   |
| 0.6082        | 7.0   | 2142 | 0.5474          | 0.8066   |
| 0.6082        | 8.0   | 2448 | 0.5488          | 0.7949   |
| 0.5976        | 9.0   | 2754 | 0.5359          | 0.8125   |
| 0.5845        | 10.0  | 3060 | 0.5236          | 0.8086   |
| 0.5845        | 11.0  | 3366 | 0.5240          | 0.8027   |
| 0.5769        | 12.0  | 3672 | 0.5120          | 0.8125   |
| 0.5769        | 13.0  | 3978 | 0.5105          | 0.8125   |
| 0.5742        | 14.0  | 4284 | 0.5282          | 0.7969   |
| 0.5631        | 15.0  | 4590 | 0.5026          | 0.8086   |
| 0.5631        | 16.0  | 4896 | 0.5120          | 0.8125   |
| 0.5529        | 17.0  | 5202 | 0.4996          | 0.8145   |
| 0.5525        | 18.0  | 5508 | 0.4928          | 0.8145   |
| 0.5525        | 19.0  | 5814 | 0.5143          | 0.8027   |
| 0.5471        | 20.0  | 6120 | 0.4859          | 0.8203   |
| 0.5471        | 21.0  | 6426 | 0.4923          | 0.8145   |
| 0.5397        | 22.0  | 6732 | 0.4874          | 0.8242   |
| 0.5404        | 23.0  | 7038 | 0.4926          | 0.8184   |
| 0.5404        | 24.0  | 7344 | 0.4913          | 0.8223   |
| 0.5375        | 25.0  | 7650 | 0.4914          | 0.8223   |
| 0.5375        | 26.0  | 7956 | 0.4960          | 0.8047   |
| 0.5301        | 27.0  | 8262 | 0.4883          | 0.8203   |
| 0.5313        | 28.0  | 8568 | 0.4890          | 0.8223   |
| 0.5313        | 29.0  | 8874 | 0.4918          | 0.8203   |
| 0.5318        | 30.0  | 9180 | 0.4881          | 0.8223   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0