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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/flan-t5-small |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: flan-t5-small-squad-qag |
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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-small-squad-qag |
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This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 6.1573 |
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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: 3e-05 |
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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: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-------:|:----:|:---------------:| |
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| 40.773 | 0.5714 | 1 | 41.7049 | |
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| 58.3411 | 1.5714 | 2 | 39.3183 | |
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| 54.8652 | 2.5714 | 3 | 37.3843 | |
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| 53.7579 | 3.5714 | 4 | 35.8088 | |
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| 52.5214 | 4.5714 | 5 | 34.5335 | |
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| 50.0236 | 5.5714 | 6 | 33.5388 | |
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| 49.5252 | 6.5714 | 7 | 32.7734 | |
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| 48.018 | 7.5714 | 8 | 32.1632 | |
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| 46.7346 | 8.5714 | 9 | 31.6080 | |
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| 45.4348 | 9.5714 | 10 | 31.0589 | |
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| 44.8246 | 10.5714 | 11 | 30.5032 | |
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| 44.1633 | 11.5714 | 12 | 29.9093 | |
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| 42.8213 | 12.5714 | 13 | 29.2965 | |
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| 43.2365 | 13.5714 | 14 | 28.6880 | |
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| 41.5266 | 14.5714 | 15 | 28.0847 | |
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| 40.6435 | 15.5714 | 16 | 27.4881 | |
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| 40.1899 | 16.5714 | 17 | 26.9148 | |
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| 39.3795 | 17.5714 | 18 | 26.3482 | |
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| 38.4061 | 18.5714 | 19 | 25.8042 | |
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| 38.4415 | 19.5714 | 20 | 25.2741 | |
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| 36.9642 | 20.5714 | 21 | 24.7624 | |
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| 36.3868 | 21.5714 | 22 | 24.2690 | |
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| 36.2422 | 22.5714 | 23 | 23.7877 | |
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| 35.3793 | 23.5714 | 24 | 23.3194 | |
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| 34.9853 | 24.5714 | 25 | 22.8591 | |
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| 34.0927 | 25.5714 | 26 | 22.4058 | |
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| 33.2451 | 26.5714 | 27 | 21.9624 | |
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| 32.8551 | 27.5714 | 28 | 21.5381 | |
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| 32.1326 | 28.5714 | 29 | 21.1176 | |
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| 31.84 | 29.5714 | 30 | 20.6980 | |
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| 31.2982 | 30.5714 | 31 | 20.2775 | |
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| 30.8415 | 31.5714 | 32 | 19.8578 | |
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| 30.073 | 32.5714 | 33 | 19.4395 | |
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| 29.8896 | 33.5714 | 34 | 19.0213 | |
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| 29.2583 | 34.5714 | 35 | 18.6041 | |
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| 28.5195 | 35.5714 | 36 | 18.1902 | |
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| 27.7352 | 36.5714 | 37 | 17.7715 | |
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| 28.0043 | 37.5714 | 38 | 17.3529 | |
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| 26.7202 | 38.5714 | 39 | 16.9311 | |
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| 26.8391 | 39.5714 | 40 | 16.5091 | |
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| 26.0355 | 40.5714 | 41 | 16.0881 | |
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| 25.5678 | 41.5714 | 42 | 15.6670 | |
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| 25.281 | 42.5714 | 43 | 15.2460 | |
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| 24.9389 | 43.5714 | 44 | 14.8265 | |
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| 24.2087 | 44.5714 | 45 | 14.4072 | |
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| 24.0442 | 45.5714 | 46 | 13.9871 | |
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| 23.5964 | 46.5714 | 47 | 13.5686 | |
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| 22.5465 | 47.5714 | 48 | 13.1483 | |
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| 22.0742 | 48.5714 | 49 | 12.7263 | |
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| 21.9666 | 49.5714 | 50 | 12.3055 | |
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| 21.1685 | 50.5714 | 51 | 11.8917 | |
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| 21.1257 | 51.5714 | 52 | 11.4814 | |
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| 20.2889 | 52.5714 | 53 | 11.0750 | |
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| 20.3047 | 53.5714 | 54 | 10.6724 | |
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| 19.8761 | 54.5714 | 55 | 10.2840 | |
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| 19.0577 | 55.5714 | 56 | 9.9060 | |
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| 18.6548 | 56.5714 | 57 | 9.5428 | |
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| 18.7313 | 57.5714 | 58 | 9.2004 | |
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| 18.247 | 58.5714 | 59 | 8.8795 | |
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| 17.7508 | 59.5714 | 60 | 8.5831 | |
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| 17.1485 | 60.5714 | 61 | 8.3108 | |
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| 16.8734 | 61.5714 | 62 | 8.0638 | |
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| 16.7851 | 62.5714 | 63 | 7.8416 | |
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| 16.2609 | 63.5714 | 64 | 7.6450 | |
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| 16.1574 | 64.5714 | 65 | 7.4740 | |
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| 15.8518 | 65.5714 | 66 | 7.3281 | |
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| 15.8425 | 66.5714 | 67 | 7.2009 | |
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| 15.3619 | 67.5714 | 68 | 7.0914 | |
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| 15.5268 | 68.5714 | 69 | 6.9991 | |
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| 15.3891 | 69.5714 | 70 | 6.9188 | |
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| 14.7154 | 70.5714 | 71 | 6.8483 | |
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| 14.5997 | 71.5714 | 72 | 6.7852 | |
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| 14.6067 | 72.5714 | 73 | 6.7290 | |
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| 14.4925 | 73.5714 | 74 | 6.6800 | |
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| 14.326 | 74.5714 | 75 | 6.6356 | |
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| 14.0346 | 75.5714 | 76 | 6.5929 | |
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| 13.9427 | 76.5714 | 77 | 6.5531 | |
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| 13.8931 | 77.5714 | 78 | 6.5155 | |
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| 13.6341 | 78.5714 | 79 | 6.4793 | |
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| 13.7549 | 79.5714 | 80 | 6.4462 | |
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| 13.4067 | 80.5714 | 81 | 6.4152 | |
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| 13.4218 | 81.5714 | 82 | 6.3872 | |
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| 13.1982 | 82.5714 | 83 | 6.3615 | |
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| 13.0855 | 83.5714 | 84 | 6.3381 | |
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| 12.9228 | 84.5714 | 85 | 6.3163 | |
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| 12.8098 | 85.5714 | 86 | 6.2966 | |
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| 12.9304 | 86.5714 | 87 | 6.2780 | |
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| 13.0 | 87.5714 | 88 | 6.2604 | |
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| 12.6473 | 88.5714 | 89 | 6.2440 | |
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| 12.4884 | 89.5714 | 90 | 6.2286 | |
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| 12.8845 | 90.5714 | 91 | 6.2152 | |
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| 12.3722 | 91.5714 | 92 | 6.2033 | |
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| 12.5444 | 92.5714 | 93 | 6.1931 | |
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| 12.3583 | 93.5714 | 94 | 6.1844 | |
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| 12.3182 | 94.5714 | 95 | 6.1766 | |
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| 12.345 | 95.5714 | 96 | 6.1702 | |
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| 12.3766 | 96.5714 | 97 | 6.1649 | |
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| 12.7799 | 97.5714 | 98 | 6.1610 | |
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| 12.505 | 98.5714 | 99 | 6.1586 | |
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| 12.2264 | 99.5714 | 100 | 6.1573 | |
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### Framework versions |
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- Transformers 4.48.3 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.3.0 |
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- Tokenizers 0.21.0 |
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