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yesj1234
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enko_mbartLarge_36p_tokenize_run1

Text2Text Generation
Transformers
PyTorch
English
Korean
mbart
Generated from Trainer
Inference Endpoints
Model card Files Community
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  1. .gitattributes +3 -0
  2. README.md +81 -0
  3. all_results.json +22 -0
  4. checkpoint-50000/config.json +59 -0
  5. checkpoint-50000/generation_config.json +12 -0
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  35. eval_results.json +10 -0
  36. generated_predictions.txt +0 -0
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  44. train_results.json +8 -0
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.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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+ checkpoint-50000/tokenizer.json 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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+ - en
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+ - ko
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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: enko_mbartLarge_36p_tokenize_run1
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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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+ # enko_mbartLarge_36p_tokenize_run1
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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: 1.1249
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+ - Bleu: 38.8566
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+ - Gen Len: 16.4716
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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: 4
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+ - eval_batch_size: 4
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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: 2
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+ - total_train_batch_size: 32
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+ - total_eval_batch_size: 16
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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: 10
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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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+ | 1.3157 | 0.46 | 5000 | 1.2895 | 34.4176 | 16.4931 |
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+ | 1.2575 | 0.93 | 10000 | 1.2279 | 35.0029 | 16.8009 |
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+ | 1.1578 | 1.39 | 15000 | 1.1733 | 36.9282 | 16.5838 |
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+ | 1.0885 | 1.86 | 20000 | 1.1464 | 37.6913 | 16.6649 |
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+ | 1.0451 | 2.32 | 25000 | 1.1437 | 37.7875 | 16.5188 |
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+ | 1.0465 | 2.79 | 30000 | 1.1425 | 37.895 | 16.4987 |
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+ | 1.0156 | 3.25 | 35000 | 1.1464 | 37.8434 | 16.5515 |
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+ | 0.9893 | 3.72 | 40000 | 1.1544 | 37.358 | 16.6096 |
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+ | 0.8779 | 4.18 | 45000 | 1.1419 | 38.1772 | 16.457 |
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+ | 0.8565 | 4.65 | 50000 | 1.1249 | 38.8455 | 16.4749 |
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+ | 0.7293 | 5.11 | 55000 | 1.1566 | 38.4853 | 16.3462 |
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+ | 0.7294 | 5.57 | 60000 | 1.1824 | 37.8822 | 16.3295 |
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+ | 0.7254 | 6.04 | 65000 | 1.2153 | 37.3612 | 16.381 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.34.1
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.14.6
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+ - Tokenizers 0.14.1
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+ {
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+ "eval_steps_per_second": 1.045,
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+ "predict_runtime": 1297.778,
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+ "train_samples_per_second": 26.411,
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+ "train_steps_per_second": 1.651
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+ }
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