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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- xsum |
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metrics: |
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- rouge |
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model-index: |
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- name: XSum_t5-small_800_adafactor |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: xsum |
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type: xsum |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 33.022 |
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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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# XSum_t5-small_800_adafactor |
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This model is a fine-tuned version of [/content/XSum_t5-small_800_adafactor/checkpoint-11000](https://huggingface.co//content/XSum_t5-small_800_adafactor/checkpoint-11000) on the xsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1714 |
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- Rouge1: 33.022 |
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- Rouge2: 11.9979 |
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- Rougel: 26.7476 |
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- Rougelsum: 26.7402 |
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- Gen Len: 18.7543 |
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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: 25 |
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- eval_batch_size: 25 |
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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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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 2.3404 | 0.01 | 100 | 2.2058 | 32.4826 | 11.5807 | 26.2716 | 26.2611 | 18.7842 | |
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| 2.3194 | 0.02 | 200 | 2.2028 | 32.6393 | 11.661 | 26.372 | 26.3643 | 18.788 | |
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| 2.3247 | 0.04 | 300 | 2.1999 | 32.6792 | 11.6985 | 26.3876 | 26.3786 | 18.7354 | |
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| 2.3276 | 0.05 | 400 | 2.1979 | 32.6668 | 11.7272 | 26.3964 | 26.3907 | 18.7957 | |
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| 2.317 | 0.06 | 500 | 2.1957 | 32.8267 | 11.8165 | 26.5075 | 26.4997 | 18.7543 | |
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| 2.3214 | 0.07 | 600 | 2.1942 | 32.8319 | 11.8064 | 26.5428 | 26.5448 | 18.7693 | |
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| 2.3014 | 0.09 | 700 | 2.1931 | 32.7136 | 11.7334 | 26.4958 | 26.486 | 18.7759 | |
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| 2.3294 | 0.1 | 800 | 2.1902 | 32.6818 | 11.7684 | 26.4314 | 26.4242 | 18.785 | |
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| 2.299 | 0.11 | 900 | 2.1914 | 32.672 | 11.7606 | 26.4475 | 26.4367 | 18.7853 | |
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| 2.3009 | 0.12 | 1000 | 2.1900 | 32.7816 | 11.7958 | 26.5167 | 26.5099 | 18.7685 | |
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| 2.2913 | 0.13 | 1100 | 2.1885 | 32.6438 | 11.7398 | 26.4077 | 26.4051 | 18.7742 | |
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| 2.293 | 0.15 | 1200 | 2.1854 | 32.8228 | 11.841 | 26.548 | 26.5415 | 18.7899 | |
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| 2.2857 | 0.16 | 1300 | 2.1853 | 32.7118 | 11.7439 | 26.4989 | 26.4941 | 18.7998 | |
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| 2.2921 | 0.17 | 1400 | 2.1832 | 32.6705 | 11.7333 | 26.4076 | 26.4082 | 18.8017 | |
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| 2.3074 | 0.18 | 1500 | 2.1827 | 32.7543 | 11.7787 | 26.4904 | 26.4923 | 18.7827 | |
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| 2.3044 | 0.2 | 1600 | 2.1806 | 32.8573 | 11.8672 | 26.5655 | 26.5619 | 18.8097 | |
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| 2.2922 | 0.21 | 1700 | 2.1819 | 32.8394 | 11.8158 | 26.5523 | 26.5467 | 18.7891 | |
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| 2.2901 | 0.22 | 1800 | 2.1803 | 32.7219 | 11.7493 | 26.4644 | 26.4572 | 18.7882 | |
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| 2.286 | 0.23 | 1900 | 2.1790 | 32.7474 | 11.852 | 26.5078 | 26.5014 | 18.7699 | |
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| 2.298 | 0.25 | 2000 | 2.1781 | 32.8662 | 11.8878 | 26.618 | 26.6174 | 18.7979 | |
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| 2.2787 | 0.26 | 2100 | 2.1775 | 32.9621 | 11.9521 | 26.6955 | 26.6914 | 18.7934 | |
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| 2.2823 | 0.27 | 2200 | 2.1777 | 33.0633 | 12.0622 | 26.7715 | 26.7597 | 18.7954 | |
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| 2.2889 | 0.28 | 2300 | 2.1742 | 32.9637 | 12.0154 | 26.6771 | 26.6721 | 18.7844 | |
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| 2.2847 | 0.29 | 2400 | 2.1774 | 32.7435 | 11.8869 | 26.5334 | 26.5306 | 18.756 | |
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| 2.2923 | 0.31 | 2500 | 2.1754 | 32.8437 | 11.8977 | 26.59 | 26.587 | 18.7964 | |
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| 2.2877 | 0.32 | 2600 | 2.1740 | 32.9137 | 11.9267 | 26.618 | 26.6046 | 18.7678 | |
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| 2.2976 | 0.33 | 2700 | 2.1728 | 32.9372 | 11.9048 | 26.6412 | 26.6345 | 18.7838 | |
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| 2.2935 | 0.34 | 2800 | 2.1719 | 32.7338 | 11.7836 | 26.5667 | 26.5629 | 18.7659 | |
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| 2.2622 | 0.36 | 2900 | 2.1718 | 32.9847 | 11.978 | 26.7093 | 26.7008 | 18.7627 | |
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| 2.2749 | 0.37 | 3000 | 2.1710 | 32.9835 | 11.9809 | 26.7034 | 26.6946 | 18.8016 | |
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| 2.2615 | 0.38 | 3100 | 2.1721 | 32.9343 | 11.9317 | 26.6752 | 26.6695 | 18.7689 | |
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| 2.2825 | 0.39 | 3200 | 2.1714 | 33.022 | 11.9979 | 26.7476 | 26.7402 | 18.7543 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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