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t5-small-mlm-pubmed-35

Text2Text Generation
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t5
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: t5-small-mlm-pubmed-35
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+ results: []
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+ ---
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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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+
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+ # t5-small-mlm-pubmed-35
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+
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+ This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.1101
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+ - Rouge2 Precision: 0.4758
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+ - Rouge2 Recall: 0.3498
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+ - Rouge2 Fmeasure: 0.3927
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-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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+ - num_epochs: 40
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
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+ |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
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+ | 1.8404 | 0.75 | 500 | 1.5005 | 0.4265 | 0.2786 | 0.3273 |
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+ | 1.6858 | 1.51 | 1000 | 1.4216 | 0.4318 | 0.2946 | 0.3404 |
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+ | 1.6071 | 2.26 | 1500 | 1.3777 | 0.4472 | 0.3148 | 0.3598 |
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+ | 1.5551 | 3.02 | 2000 | 1.3360 | 0.4406 | 0.3168 | 0.3586 |
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+ | 1.5116 | 3.77 | 2500 | 1.3128 | 0.4523 | 0.3234 | 0.3671 |
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+ | 1.4837 | 4.52 | 3000 | 1.2937 | 0.4477 | 0.3215 | 0.3645 |
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+ | 1.4513 | 5.28 | 3500 | 1.2766 | 0.4511 | 0.3262 | 0.3689 |
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+ | 1.4336 | 6.03 | 4000 | 1.2626 | 0.4548 | 0.3283 | 0.3718 |
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+ | 1.4149 | 6.79 | 4500 | 1.2449 | 0.4495 | 0.3274 | 0.3687 |
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+ | 1.3977 | 7.54 | 5000 | 1.2349 | 0.4507 | 0.3305 | 0.3712 |
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+ | 1.3763 | 8.3 | 5500 | 1.2239 | 0.4519 | 0.3266 | 0.3688 |
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+ | 1.371 | 9.05 | 6000 | 1.2171 | 0.4546 | 0.3305 | 0.3727 |
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+ | 1.3501 | 9.8 | 6500 | 1.2080 | 0.4575 | 0.3329 | 0.3755 |
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+ | 1.3443 | 10.56 | 7000 | 1.2017 | 0.4576 | 0.3314 | 0.3742 |
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+ | 1.326 | 11.31 | 7500 | 1.1926 | 0.4578 | 0.333 | 0.3757 |
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+ | 1.3231 | 12.07 | 8000 | 1.1866 | 0.4606 | 0.3357 | 0.3782 |
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+ | 1.3089 | 12.82 | 8500 | 1.1816 | 0.4591 | 0.3338 | 0.3765 |
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+ | 1.3007 | 13.57 | 9000 | 1.1764 | 0.4589 | 0.3361 | 0.3777 |
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+ | 1.2943 | 14.33 | 9500 | 1.1717 | 0.4641 | 0.3382 | 0.3811 |
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+ | 1.2854 | 15.08 | 10000 | 1.1655 | 0.4617 | 0.3378 | 0.38 |
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+ | 1.2777 | 15.84 | 10500 | 1.1612 | 0.464 | 0.3401 | 0.3823 |
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+ | 1.2684 | 16.59 | 11000 | 1.1581 | 0.4608 | 0.3367 | 0.3789 |
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+ | 1.2612 | 17.35 | 11500 | 1.1554 | 0.4623 | 0.3402 | 0.3818 |
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+ | 1.2625 | 18.1 | 12000 | 1.1497 | 0.4613 | 0.3381 | 0.3802 |
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+ | 1.2529 | 18.85 | 12500 | 1.1465 | 0.4671 | 0.3419 | 0.3848 |
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+ | 1.2461 | 19.61 | 13000 | 1.1431 | 0.4646 | 0.3399 | 0.3824 |
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+ | 1.2415 | 20.36 | 13500 | 1.1419 | 0.4659 | 0.341 | 0.3835 |
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+ | 1.2375 | 21.12 | 14000 | 1.1377 | 0.4693 | 0.3447 | 0.3873 |
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+ | 1.2315 | 21.87 | 14500 | 1.1353 | 0.4672 | 0.3433 | 0.3855 |
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+ | 1.2263 | 22.62 | 15000 | 1.1333 | 0.467 | 0.3433 | 0.3854 |
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+ | 1.2214 | 23.38 | 15500 | 1.1305 | 0.4682 | 0.3446 | 0.3869 |
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+ | 1.2202 | 24.13 | 16000 | 1.1291 | 0.4703 | 0.3465 | 0.3888 |
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+ | 1.2155 | 24.89 | 16500 | 1.1270 | 0.472 | 0.348 | 0.3903 |
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+ | 1.2064 | 25.64 | 17000 | 1.1261 | 0.4724 | 0.3479 | 0.3905 |
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+ | 1.2173 | 26.4 | 17500 | 1.1236 | 0.4734 | 0.3485 | 0.3912 |
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+ | 1.1994 | 27.15 | 18000 | 1.1220 | 0.4739 | 0.3486 | 0.3915 |
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+ | 1.2018 | 27.9 | 18500 | 1.1217 | 0.4747 | 0.3489 | 0.3921 |
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+ | 1.2045 | 28.66 | 19000 | 1.1194 | 0.4735 | 0.3488 | 0.3916 |
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+ | 1.1949 | 29.41 | 19500 | 1.1182 | 0.4732 | 0.3484 | 0.3911 |
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+ | 1.19 | 30.17 | 20000 | 1.1166 | 0.4724 | 0.3479 | 0.3904 |
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+ | 1.1932 | 30.92 | 20500 | 1.1164 | 0.4753 | 0.3494 | 0.3924 |
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+ | 1.1952 | 31.67 | 21000 | 1.1147 | 0.4733 | 0.3485 | 0.3911 |
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+ | 1.1922 | 32.43 | 21500 | 1.1146 | 0.475 | 0.3494 | 0.3923 |
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+ | 1.1889 | 33.18 | 22000 | 1.1132 | 0.4765 | 0.3499 | 0.3933 |
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+ | 1.1836 | 33.94 | 22500 | 1.1131 | 0.4768 | 0.351 | 0.3939 |
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+ | 1.191 | 34.69 | 23000 | 1.1127 | 0.4755 | 0.3495 | 0.3926 |
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+ | 1.1811 | 35.44 | 23500 | 1.1113 | 0.4748 | 0.349 | 0.3919 |
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+ | 1.1864 | 36.2 | 24000 | 1.1107 | 0.4751 | 0.3494 | 0.3921 |
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+ | 1.1789 | 36.95 | 24500 | 1.1103 | 0.4756 | 0.3499 | 0.3927 |
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+ | 1.1819 | 37.71 | 25000 | 1.1101 | 0.4758 | 0.35 | 0.3932 |
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+ | 1.1862 | 38.46 | 25500 | 1.1099 | 0.4755 | 0.3497 | 0.3926 |
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+ | 1.1764 | 39.22 | 26000 | 1.1101 | 0.4759 | 0.3498 | 0.3928 |
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+ | 1.1819 | 39.97 | 26500 | 1.1101 | 0.4758 | 0.3498 | 0.3927 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.12.5
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+ - Pytorch 1.10.0+cu111
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+ - Datasets 1.15.1
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+ - Tokenizers 0.10.3