ke_t5_base_bongsoo_ko_en_epoch2
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
Translation from Korean to English : epoch = 2
1>>> from transformers import pipeline
2>>> translator = pipeline('translation', model='chunwoolee0/ke_t5_base_bongsoo_en_ko')
3
4>>> translator("나는 습관적으로 점심식사 후에 산책을 한다.")
5[{'translation_text': 'I habitally walk after lunch.'}]
6
7>>> translator("이 강좌는 허깅페이스가 만든 거야.")
8[{'translation_text': 'This class was created by Huggface.'}]
9
10>>> translator("오늘은 늦게 일어났다.")
11[{'translation_text': 'This day I woke up earlier.'}]
Training and evaluation data
train : 360000 rows
test: 20000 rows
validation 20000 rows
Training procedure
Use chunwoolee0/ke_t5_base_bongsoo_ko_en as a pretrained model checkpoint.
max_token_length is set to 64 for stable training.
learing rate is reduced from 0.0005 for epoch 1 to 0.00002 here.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|---|
| No log | 1.0 | 5625 | 1.6646 | 12.5566 |
TrainOutput(global_step=5625, training_loss=1.8157017361111112,
metrics={'train_runtime': 11137.6996, 'train_samples_per_second': 32.323,
'train_steps_per_second': 0.505, 'total_flos': 2.056934156746752e+16,
'train_loss': 1.8157017361111112, 'epoch': 1.0})
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
