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kozh_mbartLarge_50p_run3_tokenize
yesj1234/kozh_mbartLarge_50p_run3_tokenize
kozh_mbartLarge_50p_run3_tokenize 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. --
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At a glance
- Library
- transformers
- Model type
- mbart
- Access
- Public
- Created
- Nov 2, 2023
- Updated
- Nov 2, 2023
- SHA
- 0d6fb722
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. -->
kozh_mbartLarge_50p_run3_tokenize
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: 1.3103
- Bleu: 51.0209
- Gen Len: 13.3475
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: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 1.0808 | 2.0 | 7500 | 1.3103 | 51.0184 | 13.3575 |
| 0.6519 | 3.99 | 15000 | 1.3856 | 51.6667 | 13.4112 |
| 0.3867 | 5.99 | 22500 | 1.5243 | 51.5686 | 13.432 |
| 0.236 | 7.98 | 30000 | 1.6158 | 51.4674 | 13.291 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
- Library
- transformers
- Type
- mbart
- Languages
- ko, zh
- Created
- Nov 2, 2023
- Updated
- Nov 2, 2023