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--- |
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language: |
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- de |
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- en |
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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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datasets: |
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- paulh27/alignment_iwslt2017_de_en |
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metrics: |
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- bleu |
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model-index: |
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- name: iwslt_aligned_smallT5_cont0 |
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results: |
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- task: |
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name: Translation |
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type: translation |
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dataset: |
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name: paulh27/alignment_iwslt2017_de_en |
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type: paulh27/alignment_iwslt2017_de_en |
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metrics: |
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- name: Bleu |
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type: bleu |
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value: 65.6358 |
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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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# iwslt_aligned_smallT5_cont0 |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the paulh27/alignment_iwslt2017_de_en dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5612 |
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- Bleu: 65.6358 |
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- Gen Len: 28.7691 |
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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: 0.0002 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Adafactor |
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- lr_scheduler_type: constant |
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- training_steps: 500000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
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|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:| |
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| 1.2426 | 0.78 | 10000 | 0.8300 | 46.2793 | 28.6532 | |
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| 0.9931 | 1.55 | 20000 | 0.6756 | 52.2709 | 28.6441 | |
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| 0.8573 | 2.33 | 30000 | 0.6143 | 55.8294 | 28.5405 | |
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| 0.762 | 3.11 | 40000 | 0.5811 | 57.5135 | 28.366 | |
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| 0.734 | 3.88 | 50000 | 0.5499 | 58.6125 | 28.5101 | |
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| 0.6722 | 4.66 | 60000 | 0.5228 | 59.6427 | 28.8356 | |
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| 0.6215 | 5.43 | 70000 | 0.5161 | 60.4701 | 28.7534 | |
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| 0.5756 | 6.21 | 80000 | 0.5068 | 62.0864 | 28.6498 | |
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| 0.5738 | 6.99 | 90000 | 0.5005 | 61.9714 | 28.5788 | |
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| 0.5384 | 7.76 | 100000 | 0.4909 | 62.407 | 28.5282 | |
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| 0.5109 | 8.54 | 110000 | 0.4902 | 62.1452 | 28.4617 | |
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| 0.4816 | 9.32 | 120000 | 0.4875 | 62.6499 | 28.5518 | |
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| 0.4493 | 10.09 | 130000 | 0.4867 | 62.6694 | 28.6993 | |
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| 0.4648 | 10.87 | 140000 | 0.4775 | 63.3179 | 28.5495 | |
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| 0.4414 | 11.64 | 150000 | 0.4787 | 63.6928 | 28.4673 | |
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| 0.4158 | 12.42 | 160000 | 0.4792 | 63.8752 | 28.5011 | |
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| 0.3895 | 13.2 | 170000 | 0.4794 | 63.8429 | 28.6498 | |
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| 0.4031 | 13.97 | 180000 | 0.4757 | 63.9496 | 28.7264 | |
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| 0.3844 | 14.75 | 190000 | 0.4855 | 63.7498 | 28.8288 | |
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| 0.3637 | 15.53 | 200000 | 0.4800 | 64.2277 | 28.661 | |
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| 0.3473 | 16.3 | 210000 | 0.4854 | 64.4683 | 28.786 | |
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| 0.3243 | 17.08 | 220000 | 0.4903 | 64.7805 | 28.6791 | |
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| 0.3426 | 17.85 | 230000 | 0.4819 | 64.679 | 28.4809 | |
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| 0.3295 | 18.63 | 240000 | 0.4852 | 65.3735 | 28.6014 | |
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| 0.3124 | 19.41 | 250000 | 0.4947 | 64.5641 | 28.6745 | |
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| 0.2933 | 20.18 | 260000 | 0.4972 | 65.1364 | 28.6419 | |
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| 0.3101 | 20.96 | 270000 | 0.4902 | 64.6747 | 28.6802 | |
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| 0.2991 | 21.74 | 280000 | 0.4907 | 64.9732 | 28.5653 | |
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| 0.2828 | 22.51 | 290000 | 0.5038 | 64.7552 | 28.6261 | |
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| 0.2688 | 23.29 | 300000 | 0.5042 | 65.0702 | 28.7534 | |
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| 0.2555 | 24.06 | 310000 | 0.5101 | 65.0378 | 29.089 | |
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| 0.2692 | 24.84 | 320000 | 0.5022 | 64.9991 | 28.6937 | |
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| 0.2593 | 25.62 | 330000 | 0.5085 | 65.2478 | 28.6137 | |
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| 0.2439 | 26.39 | 340000 | 0.5152 | 64.863 | 28.6464 | |
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| 0.2327 | 27.17 | 350000 | 0.5165 | 65.0748 | 28.7286 | |
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| 0.249 | 27.95 | 360000 | 0.5116 | 64.7249 | 28.6137 | |
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| 0.238 | 28.72 | 370000 | 0.5202 | 64.7651 | 28.5968 | |
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| 0.2297 | 29.5 | 380000 | 0.5243 | 65.3334 | 28.7005 | |
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| 0.2152 | 30.27 | 390000 | 0.5336 | 64.9364 | 28.6081 | |
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| 0.2106 | 31.05 | 400000 | 0.5408 | 65.117 | 28.6745 | |
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| 0.2234 | 31.83 | 410000 | 0.5249 | 64.8926 | 28.6318 | |
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| 0.2085 | 32.6 | 420000 | 0.5306 | 65.5715 | 28.7984 | |
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| 0.2018 | 33.38 | 430000 | 0.5429 | 64.9154 | 28.6351 | |
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| 0.1885 | 34.16 | 440000 | 0.5453 | 65.0538 | 28.8525 | |
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| 0.2049 | 34.93 | 450000 | 0.5434 | 65.2857 | 28.7207 | |
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| 0.1957 | 35.71 | 460000 | 0.5491 | 65.3436 | 28.714 | |
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| 0.1867 | 36.49 | 470000 | 0.5536 | 65.4934 | 28.7939 | |
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| 0.1765 | 37.26 | 480000 | 0.5583 | 65.5595 | 28.8255 | |
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| 0.1786 | 38.04 | 490000 | 0.5612 | 65.6358 | 28.7691 | |
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| 0.1809 | 38.81 | 500000 | 0.5573 | 65.0266 | 28.7455 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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