Downloads · 30 days
10
28% of all-time downloads
ko-en_mbartLarge_exp20
yesj1234/ko-en_mbartLarge_exp20
ko-en_mbartLarge_exp20 is a machine learning model from yesj1234. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
All-time downloads
36
Public
Repo size
9.8 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin4.9 GB · 50%
At a glance
- Library
- transformers
- Model type
- mbart
- Access
- Public
- Created
- Oct 21, 2023
- Updated
- Oct 21, 2023
- SHA
- df896a4a
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
ko-en_mbartLarge_exp20p
This model is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1451
- Bleu: 28.9507
- Gen Len: 18.6702
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 2000
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 1.4008 | 0.46 | 4000 | 1.3739 | 22.7174 | 18.7094 |
| 1.2847 | 0.93 | 8000 | 1.2652 | 24.8557 | 18.7254 |
| 1.2009 | 1.39 | 12000 | 1.2082 | 26.2074 | 18.7513 |
| 1.1686 | 1.86 | 16000 | 1.1841 | 26.304 | 19.161 |
| 1.0205 | 2.32 | 20000 | 1.1441 | 27.8937 | 18.6638 |
| 1.0217 | 2.78 | 24000 | 1.1301 | 28.4149 | 18.6666 |
| 0.8876 | 3.25 | 28000 | 1.1270 | 28.5803 | 18.6229 |
| 0.9024 | 3.71 | 32000 | 1.1181 | 28.852 | 18.7813 |
| 0.7927 | 4.18 | 36000 | 1.1393 | 28.3975 | 18.4863 |
| 0.8174 | 4.64 | 40000 | 1.1249 | 28.6313 | 18.3916 |
| 0.7434 | 5.11 | 44000 | 1.1696 | 28.2898 | 18.7739 |
| 0.7416 | 5.57 | 48000 | 1.1451 | 28.9507 | 18.6744 |
| 0.689 | 6.03 | 52000 | 1.1759 | 28.3532 | 18.4481 |
| 0.7238 | 6.5 | 56000 | 1.1825 | 28.3827 | 18.7038 |
| 0.7238 | 6.96 | 60000 | 1.1676 | 28.8248 | 18.5073 |
| 0.657 | 7.43 | 64000 | 1.2514 | 27.4378 | 18.4196 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
- Library
- transformers
- Type
- mbart
- Languages
- ko, en
- Created
- Oct 21, 2023
- Updated
- Oct 21, 2023