ke_t5_base_aihub
This model is a fine-tuned version of KETI-AIR/ke-t5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 0.0
Model description
KE-T5 is a pretrained-model of t5 text-to-text transfer transformers using the Korean and English corpus developed by KETI (한국전자연구원). The vocabulary used by KE-T5 consists of 64,000 sub-word tokens and was created using Google's sentencepiece. The Sentencepiece model was trained to cover 99.95% of a 30GB corpus with an approximate 7:3 mix of Korean and English.
Intended uses & limitations
This is an excersize for ke-t5 summarization finetuning using pre-trained ke-t5-base using the data from aihub.
Training and evaluation data
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 0.0 | 1.0 | 743 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 2.0 | 1486 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 3.0 | 2229 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 4.0 | 2972 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3