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--- |
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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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model-index: |
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- name: flan-t5-base-lamp-7t-finetuned-1 |
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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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# flan-t5-base-lamp-7t-finetuned-1 |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2121 |
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- Rouge1: 0.5299 |
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- Rouge2: 0.2730 |
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- Rougel: 0.4776 |
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- Rougelsum: 0.4930 |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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_ratio: 0.05 |
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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 | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 7.0613 | 1.0 | 840 | 1.2605 | 0.5053 | 0.2462 | 0.4513 | 0.4657 | |
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| 1.3665 | 2.0 | 1680 | 1.2271 | 0.5124 | 0.2562 | 0.4600 | 0.4747 | |
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| 1.3009 | 3.0 | 2520 | 1.2114 | 0.5179 | 0.2613 | 0.4652 | 0.4802 | |
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| 1.2594 | 4.0 | 3360 | 1.2039 | 0.5229 | 0.2656 | 0.4677 | 0.4839 | |
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| 1.227 | 5.0 | 4200 | 1.1988 | 0.5247 | 0.2669 | 0.4698 | 0.4860 | |
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| 1.1918 | 6.0 | 5040 | 1.1966 | 0.5238 | 0.2698 | 0.4701 | 0.4860 | |
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| 1.1696 | 7.0 | 5880 | 1.1934 | 0.5261 | 0.2702 | 0.4723 | 0.4880 | |
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| 1.1285 | 8.0 | 6720 | 1.1933 | 0.5237 | 0.2693 | 0.4701 | 0.4849 | |
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| 1.1153 | 9.0 | 7560 | 1.1961 | 0.5263 | 0.2708 | 0.4724 | 0.4880 | |
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| 1.0927 | 10.0 | 8400 | 1.1961 | 0.5254 | 0.2691 | 0.4720 | 0.4874 | |
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| 1.0661 | 11.0 | 9240 | 1.2010 | 0.5234 | 0.2684 | 0.4698 | 0.4854 | |
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| 1.0634 | 12.0 | 10080 | 1.2003 | 0.5259 | 0.2723 | 0.4729 | 0.4885 | |
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| 1.046 | 13.0 | 10920 | 1.2019 | 0.5277 | 0.2726 | 0.4747 | 0.4907 | |
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| 1.0273 | 14.0 | 11760 | 1.2045 | 0.5309 | 0.2749 | 0.4776 | 0.4940 | |
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| 1.0218 | 15.0 | 12600 | 1.2077 | 0.5295 | 0.2728 | 0.4771 | 0.4925 | |
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| 1.0208 | 16.0 | 13440 | 1.2095 | 0.5303 | 0.2728 | 0.4775 | 0.4928 | |
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| 1.003 | 17.0 | 14280 | 1.2110 | 0.5301 | 0.2726 | 0.4772 | 0.4929 | |
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| 1.003 | 18.0 | 15120 | 1.2099 | 0.5314 | 0.2729 | 0.4781 | 0.4941 | |
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| 0.9893 | 19.0 | 15960 | 1.2115 | 0.5302 | 0.2737 | 0.4779 | 0.4933 | |
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| 0.9918 | 20.0 | 16800 | 1.2121 | 0.5299 | 0.2730 | 0.4776 | 0.4930 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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