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--- |
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license: apache-2.0 |
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base_model: google/mt5-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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- sacrebleu |
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model-index: |
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- name: mT5-TextSimp-LT-BatchSize2-lr1e-4 |
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results: [] |
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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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# mT5-TextSimp-LT-BatchSize2-lr1e-4 |
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co./google/mt5-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0672 |
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- Rouge1: 0.7548 |
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- Rouge2: 0.5989 |
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- Rougel: 0.7509 |
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- Sacrebleu: 49.0373 |
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- Gen Len: 38.0501 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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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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- 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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Sacrebleu | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| 25.6783 | 0.24 | 200 | 16.0497 | 0.0109 | 0.0005 | 0.0107 | 0.0029 | 512.0 | |
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| 1.9593 | 0.48 | 400 | 0.7780 | 0.014 | 0.0005 | 0.0136 | 0.0146 | 42.685 | |
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| 0.2778 | 0.72 | 600 | 0.1429 | 0.4924 | 0.3128 | 0.4803 | 20.3057 | 38.0382 | |
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| 0.1325 | 0.96 | 800 | 0.1039 | 0.6193 | 0.4369 | 0.6098 | 33.687 | 38.0501 | |
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| 0.1702 | 1.2 | 1000 | 0.0958 | 0.6697 | 0.5016 | 0.6613 | 38.0391 | 38.0501 | |
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| 0.13 | 1.44 | 1200 | 0.0880 | 0.6737 | 0.5051 | 0.6644 | 38.62 | 38.0501 | |
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| 0.1086 | 1.67 | 1400 | 0.0839 | 0.6964 | 0.5326 | 0.6884 | 40.9056 | 38.0501 | |
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| 0.0716 | 1.91 | 1600 | 0.0859 | 0.6933 | 0.5298 | 0.6862 | 40.7158 | 38.0501 | |
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| 0.1135 | 2.15 | 1800 | 0.0820 | 0.7017 | 0.5366 | 0.6936 | 40.7484 | 38.0501 | |
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| 0.0997 | 2.39 | 2000 | 0.0814 | 0.7011 | 0.5351 | 0.6945 | 41.1948 | 38.0501 | |
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| 0.0996 | 2.63 | 2200 | 0.0774 | 0.7103 | 0.5522 | 0.7049 | 42.5756 | 38.0501 | |
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| 1.1379 | 2.87 | 2400 | 0.0763 | 0.7211 | 0.5556 | 0.7152 | 43.2411 | 38.0501 | |
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| 0.0594 | 3.11 | 2600 | 0.0776 | 0.7261 | 0.5647 | 0.7201 | 44.2205 | 38.0501 | |
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| 0.0763 | 3.35 | 2800 | 0.0736 | 0.7309 | 0.5709 | 0.7251 | 45.2825 | 38.0501 | |
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| 0.1641 | 3.59 | 3000 | 0.0722 | 0.7297 | 0.5685 | 0.7242 | 44.9001 | 38.0501 | |
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| 0.1085 | 3.83 | 3200 | 0.0703 | 0.7377 | 0.5793 | 0.7319 | 45.7504 | 38.0501 | |
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| 0.0573 | 4.07 | 3400 | 0.0719 | 0.7393 | 0.5796 | 0.7335 | 45.86 | 38.0501 | |
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| 0.1149 | 4.31 | 3600 | 0.0705 | 0.7415 | 0.5787 | 0.7365 | 46.2652 | 38.0501 | |
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| 0.0843 | 4.55 | 3800 | 0.0703 | 0.7385 | 0.5754 | 0.7326 | 46.5292 | 38.0501 | |
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| 0.0658 | 4.78 | 4000 | 0.0705 | 0.7437 | 0.5855 | 0.7384 | 46.864 | 38.0501 | |
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| 0.0676 | 5.02 | 4200 | 0.0694 | 0.7437 | 0.584 | 0.7384 | 47.1268 | 38.0501 | |
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| 0.0657 | 5.26 | 4400 | 0.0711 | 0.7473 | 0.5913 | 0.7432 | 47.4413 | 38.0501 | |
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| 0.0679 | 5.5 | 4600 | 0.0702 | 0.7496 | 0.5908 | 0.7446 | 47.8281 | 38.0501 | |
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| 0.0664 | 5.74 | 4800 | 0.0671 | 0.7511 | 0.5929 | 0.7463 | 47.7693 | 38.0501 | |
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| 0.0446 | 5.98 | 5000 | 0.0685 | 0.7533 | 0.5932 | 0.7478 | 48.032 | 38.0501 | |
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| 0.0732 | 6.22 | 5200 | 0.0678 | 0.7523 | 0.5948 | 0.7472 | 48.3467 | 38.0501 | |
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| 0.0706 | 6.46 | 5400 | 0.0672 | 0.755 | 0.5983 | 0.7507 | 48.6158 | 38.0501 | |
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| 0.051 | 6.7 | 5600 | 0.0674 | 0.7523 | 0.5961 | 0.7478 | 48.4828 | 38.0501 | |
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| 0.067 | 6.94 | 5800 | 0.0681 | 0.7532 | 0.5978 | 0.7492 | 48.7253 | 38.0501 | |
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| 0.075 | 7.18 | 6000 | 0.0684 | 0.7534 | 0.5969 | 0.7492 | 48.7053 | 38.0501 | |
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| 0.1323 | 7.42 | 6200 | 0.0671 | 0.755 | 0.5991 | 0.7511 | 48.9922 | 38.0501 | |
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| 0.0383 | 7.66 | 6400 | 0.0671 | 0.7551 | 0.5994 | 0.7511 | 49.0028 | 38.0501 | |
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| 0.0599 | 7.89 | 6600 | 0.0672 | 0.7548 | 0.5989 | 0.7509 | 49.0373 | 38.0501 | |
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### Framework versions |
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- Transformers 4.33.0 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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