
<!-- 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

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: 0.6901
- Accuracy: 0.6855

## 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: 1e-05
- 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: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log        | 1.0   | 306   | 1.0484          | 0.2773   |
| 1.044         | 2.0   | 612   | 1.0016          | 0.3320   |
| 1.044         | 3.0   | 918   | 0.9675          | 0.3633   |
| 0.9847        | 4.0   | 1224  | 0.9380          | 0.4434   |
| 0.9411        | 5.0   | 1530  | 0.9163          | 0.4863   |
| 0.9411        | 6.0   | 1836  | 0.8943          | 0.5742   |
| 0.9091        | 7.0   | 2142  | 0.8852          | 0.5137   |
| 0.9091        | 8.0   | 2448  | 0.8750          | 0.5039   |
| 0.8895        | 9.0   | 2754  | 0.8674          | 0.4941   |
| 0.8736        | 10.0  | 3060  | 0.8529          | 0.5254   |
| 0.8736        | 11.0  | 3366  | 0.8477          | 0.5195   |
| 0.8569        | 12.0  | 3672  | 0.8290          | 0.6133   |
| 0.8569        | 13.0  | 3978  | 0.8231          | 0.6055   |
| 0.8512        | 14.0  | 4284  | 0.8181          | 0.5879   |
| 0.8414        | 15.0  | 4590  | 0.8084          | 0.6211   |
| 0.8414        | 16.0  | 4896  | 0.8050          | 0.6152   |
| 0.8323        | 17.0  | 5202  | 0.8015          | 0.6016   |
| 0.8276        | 18.0  | 5508  | 0.7893          | 0.6426   |
| 0.8276        | 19.0  | 5814  | 0.7889          | 0.625    |
| 0.8248        | 20.0  | 6120  | 0.7829          | 0.6289   |
| 0.8248        | 21.0  | 6426  | 0.7796          | 0.6211   |
| 0.8166        | 22.0  | 6732  | 0.7759          | 0.6211   |
| 0.8107        | 23.0  | 7038  | 0.7720          | 0.6230   |
| 0.8107        | 24.0  | 7344  | 0.7691          | 0.625    |
| 0.8094        | 25.0  | 7650  | 0.7620          | 0.6426   |
| 0.8094        | 26.0  | 7956  | 0.7646          | 0.6230   |
| 0.8055        | 27.0  | 8262  | 0.7533          | 0.6582   |
| 0.8006        | 28.0  | 8568  | 0.7546          | 0.6348   |
| 0.8006        | 29.0  | 8874  | 0.7525          | 0.6348   |
| 0.7987        | 30.0  | 9180  | 0.7493          | 0.6465   |
| 0.7987        | 31.0  | 9486  | 0.7469          | 0.6484   |
| 0.7945        | 32.0  | 9792  | 0.7417          | 0.6562   |
| 0.7949        | 33.0  | 10098 | 0.7412          | 0.6465   |
| 0.7949        | 34.0  | 10404 | 0.7440          | 0.6367   |
| 0.7883        | 35.0  | 10710 | 0.7392          | 0.6504   |
| 0.7874        | 36.0  | 11016 | 0.7316          | 0.6660   |
| 0.7874        | 37.0  | 11322 | 0.7319          | 0.6543   |
| 0.7855        | 38.0  | 11628 | 0.7339          | 0.6504   |
| 0.7855        | 39.0  | 11934 | 0.7299          | 0.6562   |
| 0.7856        | 40.0  | 12240 | 0.7299          | 0.6504   |
| 0.7816        | 41.0  | 12546 | 0.7227          | 0.6738   |
| 0.7816        | 42.0  | 12852 | 0.7275          | 0.6504   |
| 0.7805        | 43.0  | 13158 | 0.7269          | 0.6543   |
| 0.7805        | 44.0  | 13464 | 0.7206          | 0.6641   |
| 0.7756        | 45.0  | 13770 | 0.7175          | 0.6777   |
| 0.7779        | 46.0  | 14076 | 0.7172          | 0.6660   |
| 0.7779        | 47.0  | 14382 | 0.7191          | 0.6582   |
| 0.7778        | 48.0  | 14688 | 0.7145          | 0.6680   |
| 0.7778        | 49.0  | 14994 | 0.7154          | 0.6602   |
| 0.7701        | 50.0  | 15300 | 0.7121          | 0.6719   |
| 0.774         | 51.0  | 15606 | 0.7142          | 0.6641   |
| 0.774         | 52.0  | 15912 | 0.7132          | 0.6719   |
| 0.7732        | 53.0  | 16218 | 0.7078          | 0.6836   |
| 0.768         | 54.0  | 16524 | 0.7123          | 0.6641   |
| 0.768         | 55.0  | 16830 | 0.7048          | 0.6855   |
| 0.7681        | 56.0  | 17136 | 0.7091          | 0.6641   |
| 0.7681        | 57.0  | 17442 | 0.7055          | 0.6797   |
| 0.7685        | 58.0  | 17748 | 0.7047          | 0.6816   |
| 0.7684        | 59.0  | 18054 | 0.7036          | 0.6836   |
| 0.7684        | 60.0  | 18360 | 0.7025          | 0.6836   |
| 0.7633        | 61.0  | 18666 | 0.7042          | 0.6699   |
| 0.7633        | 62.0  | 18972 | 0.7040          | 0.6699   |
| 0.7659        | 63.0  | 19278 | 0.7017          | 0.6777   |
| 0.7647        | 64.0  | 19584 | 0.7003          | 0.6836   |
| 0.7647        | 65.0  | 19890 | 0.7015          | 0.6738   |
| 0.7676        | 66.0  | 20196 | 0.6987          | 0.6816   |
| 0.7607        | 67.0  | 20502 | 0.6972          | 0.6875   |
| 0.7607        | 68.0  | 20808 | 0.6988          | 0.6777   |
| 0.7637        | 69.0  | 21114 | 0.6968          | 0.6875   |
| 0.7637        | 70.0  | 21420 | 0.6968          | 0.6816   |
| 0.7556        | 71.0  | 21726 | 0.6980          | 0.6738   |
| 0.7608        | 72.0  | 22032 | 0.6983          | 0.6758   |
| 0.7608        | 73.0  | 22338 | 0.6967          | 0.6758   |
| 0.7532        | 74.0  | 22644 | 0.6950          | 0.6816   |
| 0.7532        | 75.0  | 22950 | 0.6961          | 0.6738   |
| 0.7592        | 76.0  | 23256 | 0.6949          | 0.6797   |
| 0.7553        | 77.0  | 23562 | 0.6936          | 0.6836   |
| 0.7553        | 78.0  | 23868 | 0.6939          | 0.6855   |
| 0.7581        | 79.0  | 24174 | 0.6937          | 0.6816   |
| 0.7581        | 80.0  | 24480 | 0.6922          | 0.6836   |
| 0.7558        | 81.0  | 24786 | 0.6934          | 0.6758   |
| 0.7581        | 82.0  | 25092 | 0.6922          | 0.6855   |
| 0.7581        | 83.0  | 25398 | 0.6939          | 0.6738   |
| 0.7561        | 84.0  | 25704 | 0.6931          | 0.6797   |
| 0.7581        | 85.0  | 26010 | 0.6914          | 0.6836   |
| 0.7581        | 86.0  | 26316 | 0.6923          | 0.6797   |
| 0.7553        | 87.0  | 26622 | 0.6921          | 0.6816   |
| 0.7553        | 88.0  | 26928 | 0.6923          | 0.6797   |
| 0.7553        | 89.0  | 27234 | 0.6913          | 0.6816   |
| 0.7551        | 90.0  | 27540 | 0.6911          | 0.6816   |
| 0.7551        | 91.0  | 27846 | 0.6920          | 0.6777   |
| 0.7515        | 92.0  | 28152 | 0.6907          | 0.6797   |
| 0.7515        | 93.0  | 28458 | 0.6914          | 0.6797   |
| 0.7574        | 94.0  | 28764 | 0.6912          | 0.6797   |
| 0.7525        | 95.0  | 29070 | 0.6906          | 0.6836   |
| 0.7525        | 96.0  | 29376 | 0.6905          | 0.6836   |
| 0.7539        | 97.0  | 29682 | 0.6899          | 0.6855   |
| 0.7539        | 98.0  | 29988 | 0.6899          | 0.6855   |
| 0.754         | 99.0  | 30294 | 0.6901          | 0.6855   |
| 0.7573        | 100.0 | 30600 | 0.6901          | 0.6855   |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "line-corporation/line-distilbert-base-japanese", "model-index": [{"name": "factual-consistency-classification-ja", "results": []}]}
transformers pytorch distilbert generated_from_trainer base_model:line-corporation/line-distilbert-base-japanese license:apache-2.0 endpoints_compatible region:us