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yesj1234
/
mbart-mmt_mid1_ko-ja

Text2Text Generation
Transformers
PyTorch
Korean
Japanese
mbart
Generated from Trainer
Inference Endpoints
Model card Files Community
yesj1234 commited on
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  1. .gitattributes +3 -0
  2. README.md +74 -0
  3. added_tokens.json +59 -0
  4. all_results.json +22 -0
  5. checkpoint-3000/added_tokens.json +59 -0
  6. checkpoint-3000/config.json +59 -0
  7. checkpoint-3000/generation_config.json +12 -0
  8. checkpoint-3000/optimizer.pt +3 -0
  9. checkpoint-3000/pytorch_model.bin +3 -0
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  13. checkpoint-3000/rng_state_3.pth +3 -0
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  15. checkpoint-3000/sentencepiece.bpe.model +3 -0
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  37. config.json +59 -0
  38. eval_results.json +10 -0
  39. generated_predictions.txt +0 -0
  40. generation_config.json +12 -0
  41. predict_results.json +9 -0
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  43. sentencepiece.bpe.model +3 -0
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  47. train_results.json +8 -0
  48. trainer_state.json +196 -0
  49. training_args.bin +3 -0
.gitattributes CHANGED
@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ language:
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+ - ko
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+ - ja
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+ base_model: facebook/mbart-large-50-many-to-many-mmt
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - bleu
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+ model-index:
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+ - name: tst-translation-output
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # tst-translation-output
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+
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+ This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8761
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+ - Bleu: 11.9991
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+ - Gen Len: 17.5742
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - distributed_type: multi-GPU
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+ - num_devices: 4
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 64
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+ - total_eval_batch_size: 8
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 35
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
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+ | 1.014 | 0.91 | 1500 | 0.9561 | 5.9697 | 18.2307 |
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+ | 0.7888 | 1.82 | 3000 | 0.8761 | 10.9388 | 17.4752 |
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+ | 0.6166 | 2.73 | 4500 | 0.8778 | 11.0165 | 17.1855 |
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+ | 0.474 | 3.64 | 6000 | 0.9008 | 11.8779 | 17.1838 |
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+ | 0.3559 | 4.55 | 7500 | 0.9504 | 12.6024 | 17.3179 |
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+ | 0.2588 | 5.47 | 9000 | 0.9978 | 13.4321 | 17.1999 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.34.0
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.14.5
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+ - Tokenizers 0.14.1
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