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--- |
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license: apache-2.0 |
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base_model: google/mt5-small |
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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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- bleu |
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model-index: |
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- name: mt5-small_large_lr |
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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-small_large_lr |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9688 |
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- Rouge1: 38.8633 |
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- Rouge2: 33.0802 |
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- Rougel: 37.6956 |
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- Rougelsum: 37.7116 |
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- Bleu: 26.6301 |
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- Gen Len: 11.5566 |
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- Meteor: 0.3519 |
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- No ans accuracy: 22.99 |
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- Av cosine sim: 0.6861 |
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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: 0.005 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 9 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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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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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | Meteor | No ans accuracy | Av cosine sim | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:------:|:---------------:|:-------------:| |
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| 5.4434 | 1.0 | 175 | 2.1918 | 1.8449 | 1.2024 | 1.7039 | 1.7116 | 0.0 | 2.7672 | 0.0145 | 28.9700 | 0.1363 | |
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| 1.8436 | 1.99 | 350 | 1.1852 | 33.6062 | 26.8725 | 32.2258 | 32.241 | 20.3395 | 12.2528 | 0.2957 | 17.3800 | 0.636 | |
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| 1.2276 | 2.99 | 525 | 1.0630 | 33.186 | 27.4949 | 32.0715 | 32.0522 | 20.3232 | 11.0301 | 0.2957 | 21.18 | 0.6109 | |
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| 0.9589 | 3.98 | 700 | 1.0083 | 40.265 | 33.6652 | 38.9503 | 38.9661 | 28.0884 | 12.8545 | 0.3623 | 17.54 | 0.7157 | |
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| 0.7931 | 4.98 | 875 | 0.9682 | 37.9437 | 31.7611 | 36.7618 | 36.7671 | 25.7738 | 12.0286 | 0.3424 | 20.66 | 0.6825 | |
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| 0.6686 | 5.97 | 1050 | 0.9601 | 37.5742 | 31.9098 | 36.4225 | 36.4381 | 24.9584 | 11.4169 | 0.3398 | 22.56 | 0.6713 | |
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| 0.5686 | 6.97 | 1225 | 0.9620 | 43.1436 | 36.6363 | 41.7279 | 41.7571 | 32.4301 | 13.6142 | 0.3893 | 16.9400 | 0.757 | |
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| 0.4939 | 7.96 | 1400 | 0.9688 | 38.8633 | 33.0802 | 37.6956 | 37.7116 | 26.6301 | 11.5566 | 0.3519 | 22.99 | 0.6861 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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