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ko-en-m2m
Stxlla/ko-en-m2m
ko-en-m2m is a machine learning model from Stxlla. 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. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- mit
- Model type
- m2m_100
- Access
- Public
- Created
- Dec 9, 2022
- Updated
- Dec 10, 2022
- SHA
- eff65c1c
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-m2m
This model is a fine-tuned version of facebook/m2m100_418M on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4282
- Bleu: 25.8137
- Gen Len: 10.9556
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 0.5891 | 0.3 | 5000 | 0.7640 | 12.7212 | 10.465 |
| 0.5653 | 0.6 | 10000 | 0.7211 | 13.4957 | 11.5844 |
| 0.5464 | 0.91 | 15000 | 0.6875 | 13.5204 | 10.6604 |
| 0.5254 | 1.21 | 20000 | 0.6690 | 14.5273 | 10.5754 |
| 0.5308 | 1.51 | 25000 | 0.6757 | 14.1623 | 11.9493 |
| 0.5192 | 1.81 | 30000 | 0.6458 | 15.1048 | 10.8811 |
| 0.502 | 2.11 | 35000 | 0.6423 | 14.7989 | 11.047 |
| 0.4971 | 2.42 | 40000 | 0.6259 | 15.6324 | 11.0428 |
| 0.502 | 2.72 | 45000 | 0.6047 | 16.684 | 10.9814 |
| 0.4544 | 3.02 | 50000 | 0.5834 | 16.9704 | 10.9722 |
| 0.4541 | 3.32 | 55000 | 0.5722 | 17.6061 | 10.8485 |
| 0.4362 | 3.63 | 60000 | 0.5523 | 19.1337 | 10.7972 |
| 0.4285 | 3.93 | 65000 | 0.5325 | 19.4546 | 10.6665 |
| 0.3851 | 4.23 | 70000 | 0.5159 | 20.4035 | 10.6171 |
| 0.3891 | 4.53 | 75000 | 0.4926 | 21.8822 | 10.8857 |
| 0.3602 | 4.83 | 80000 | 0.4740 | 22.737 | 11.0248 |
| 0.336 | 5.14 | 85000 | 0.4570 | 23.7202 | 10.7115 |
| 0.3355 | 5.44 | 90000 | 0.4415 | 24.9891 | 10.9077 |
| 0.3244 | 5.74 | 95000 | 0.4282 | 25.8137 | 10.9556 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
- Library
- transformers
- Type
- m2m_100
- License
- mit
- Created
- Dec 9, 2022
- Updated
- Dec 10, 2022