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oMateos2020
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t5-small_adafactor

Text2Text Generation
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PyTorch
t5
generated_from_trainer
Eval Results
Inference Endpoints
text-generation-inference
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@@ -18,7 +18,7 @@ model-index:
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  metrics:
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  - name: Rouge1
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  type: rouge
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- value: 32.3784
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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
@@ -26,14 +26,14 @@ should probably proofread and complete it, then remove this comment. -->
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  # t5-small_adafactor
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- This model was trained from scratch on the xsum dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.1513
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- - Rouge1: 32.3784
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- - Rouge2: 11.2335
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- - Rougel: 26.1197
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- - Rougelsum: 26.1212
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- - Gen Len: 18.8066
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  ## Model description
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@@ -52,7 +52,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.001
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  - train_batch_size: 24
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  - eval_batch_size: 24
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  - seed: 42
@@ -65,48 +65,48 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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- | 2.4206 | 0.02 | 200 | 2.2951 | 30.6414 | 9.9248 | 24.5953 | 24.6021 | 18.7814 |
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- | 2.4363 | 0.05 | 400 | 2.3041 | 30.969 | 9.9594 | 24.9531 | 24.9484 | 18.7812 |
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- | 2.4442 | 0.07 | 600 | 2.3042 | 30.9605 | 9.8821 | 24.9273 | 24.9343 | 18.787 |
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- | 2.4402 | 0.09 | 800 | 2.2985 | 31.1667 | 9.9976 | 25.034 | 25.0346 | 18.7505 |
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- | 2.4394 | 0.12 | 1000 | 2.2951 | 30.8935 | 9.8125 | 24.8084 | 24.8066 | 18.878 |
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- | 2.4148 | 0.14 | 1200 | 2.2965 | 31.4419 | 10.1935 | 25.1234 | 25.1165 | 18.8134 |
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- | 2.4329 | 0.16 | 1400 | 2.2891 | 30.735 | 9.7912 | 24.6127 | 24.6084 | 18.7797 |
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- | 2.4308 | 0.19 | 1600 | 2.2950 | 31.0388 | 10.13 | 24.9166 | 24.9086 | 18.8409 |
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- | 2.4302 | 0.21 | 1800 | 2.2808 | 30.978 | 10.0544 | 24.9191 | 24.9158 | 18.8147 |
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- | 2.4165 | 0.24 | 2000 | 2.2785 | 31.2423 | 10.2329 | 25.2027 | 25.192 | 18.7531 |
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- | 2.4227 | 0.26 | 2200 | 2.2705 | 30.8977 | 10.0552 | 24.8875 | 24.8869 | 18.8472 |
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- | 2.4117 | 0.28 | 2400 | 2.2691 | 30.9478 | 10.1551 | 24.8565 | 24.8527 | 18.8049 |
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- | 2.4229 | 0.31 | 2600 | 2.2635 | 31.1634 | 10.2055 | 25.0868 | 25.084 | 18.8424 |
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- | 2.4163 | 0.33 | 2800 | 2.2554 | 31.2877 | 10.4018 | 25.2972 | 25.2924 | 18.8127 |
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- | 2.4109 | 0.35 | 3000 | 2.2498 | 31.5192 | 10.3888 | 25.3461 | 25.3489 | 18.8066 |
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- | 2.3883 | 0.38 | 3200 | 2.2473 | 31.4033 | 10.3393 | 25.2324 | 25.2297 | 18.8657 |
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- | 2.3946 | 0.4 | 3400 | 2.2443 | 31.9869 | 10.7348 | 25.7509 | 25.7521 | 18.7703 |
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- | 2.3726 | 0.42 | 3600 | 2.2398 | 31.6649 | 10.4532 | 25.4268 | 25.4221 | 18.8244 |
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- | 2.3949 | 0.45 | 3800 | 2.2335 | 31.7186 | 10.6587 | 25.5281 | 25.5234 | 18.7766 |
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- | 2.387 | 0.47 | 4000 | 2.2267 | 32.015 | 10.7906 | 25.7612 | 25.7634 | 18.7552 |
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- | 2.3737 | 0.49 | 4200 | 2.2262 | 31.7823 | 10.7758 | 25.6306 | 25.6343 | 18.7436 |
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- | 2.37 | 0.52 | 4400 | 2.2238 | 31.5111 | 10.6443 | 25.3768 | 25.3782 | 18.7801 |
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- | 2.3748 | 0.54 | 4600 | 2.2166 | 31.6585 | 10.5958 | 25.4283 | 25.4321 | 18.7989 |
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- | 2.3789 | 0.56 | 4800 | 2.2100 | 31.829 | 10.7779 | 25.6561 | 25.648 | 18.7688 |
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- | 2.3659 | 0.59 | 5000 | 2.2064 | 32.0499 | 10.9069 | 25.8784 | 25.8725 | 18.8464 |
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- | 2.3656 | 0.61 | 5200 | 2.2032 | 31.8874 | 10.7972 | 25.6996 | 25.6948 | 18.75 |
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- | 2.3593 | 0.64 | 5400 | 2.1987 | 31.9182 | 10.7176 | 25.672 | 25.6662 | 18.8595 |
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- | 2.3445 | 0.66 | 5600 | 2.1935 | 31.9871 | 10.803 | 25.7289 | 25.7247 | 18.7972 |
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- | 2.3439 | 0.68 | 5800 | 2.1870 | 32.1788 | 10.9332 | 25.9597 | 25.9605 | 18.8062 |
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- | 2.3489 | 0.71 | 6000 | 2.1845 | 32.0946 | 10.9864 | 25.9296 | 25.9342 | 18.8307 |
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- | 2.3759 | 0.73 | 6200 | 2.1796 | 32.3321 | 11.0971 | 26.084 | 26.0843 | 18.7956 |
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- | 2.3611 | 0.75 | 6400 | 2.1759 | 32.0703 | 10.8886 | 25.8437 | 25.8369 | 18.7629 |
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- | 2.3319 | 0.78 | 6600 | 2.1722 | 31.8674 | 10.8993 | 25.6791 | 25.686 | 18.8292 |
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- | 2.3445 | 0.8 | 6800 | 2.1686 | 32.1679 | 11.0594 | 25.8591 | 25.8604 | 18.817 |
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- | 2.3523 | 0.82 | 7000 | 2.1667 | 32.2232 | 11.1537 | 25.9326 | 25.9359 | 18.8073 |
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- | 2.3439 | 0.85 | 7200 | 2.1641 | 32.246 | 11.1854 | 26.015 | 26.0097 | 18.7954 |
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- | 2.3496 | 0.87 | 7400 | 2.1603 | 32.1141 | 11.0758 | 25.9561 | 25.9623 | 18.7639 |
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- | 2.3368 | 0.89 | 7600 | 2.1580 | 32.3447 | 11.1661 | 26.0906 | 26.0888 | 18.7936 |
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- | 2.3634 | 0.92 | 7800 | 2.1553 | 32.3039 | 11.2246 | 26.0819 | 26.0828 | 18.7922 |
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- | 2.3396 | 0.94 | 8000 | 2.1534 | 32.2979 | 11.262 | 26.0726 | 26.071 | 18.8069 |
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- | 2.3645 | 0.96 | 8200 | 2.1520 | 32.4169 | 11.292 | 26.1811 | 26.187 | 18.7921 |
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- | 2.341 | 0.99 | 8400 | 2.1513 | 32.3784 | 11.2335 | 26.1197 | 26.1212 | 18.8066 |
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  ### Framework versions
 
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  metrics:
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  - name: Rouge1
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  type: rouge
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+ value: 32.8631
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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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  # t5-small_adafactor
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+ This model is a fine-tuned version of [oMateos2020/t5-small_adafactor](https://huggingface.co/oMateos2020/t5-small_adafactor) on the xsum dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.1167
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+ - Rouge1: 32.8631
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+ - Rouge2: 11.658
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+ - Rougel: 26.6192
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+ - Rougelsum: 26.6224
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+ - Gen Len: 18.7663
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0005
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  - train_batch_size: 24
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  - eval_batch_size: 24
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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+ | 2.1315 | 0.02 | 200 | 2.1865 | 31.9486 | 10.9605 | 25.7418 | 25.7408 | 18.8466 |
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+ | 2.1297 | 0.05 | 400 | 2.1965 | 31.9598 | 10.9463 | 25.784 | 25.7867 | 18.8525 |
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+ | 2.1284 | 0.07 | 600 | 2.1981 | 32.231 | 11.1003 | 26.0155 | 26.0226 | 18.8466 |
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+ | 2.1315 | 0.09 | 800 | 2.1873 | 31.9161 | 10.8642 | 25.7166 | 25.7273 | 18.8227 |
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+ | 2.1212 | 0.12 | 1000 | 2.1892 | 32.4646 | 11.1852 | 26.2451 | 26.2439 | 18.8259 |
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+ | 2.1028 | 0.14 | 1200 | 2.1978 | 32.2886 | 11.1346 | 26.0795 | 26.0827 | 18.7685 |
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+ | 2.1221 | 0.16 | 1400 | 2.1936 | 32.2901 | 11.0821 | 25.9983 | 26.0024 | 18.7798 |
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+ | 2.1168 | 0.19 | 1600 | 2.1922 | 32.1655 | 11.1451 | 25.986 | 25.9893 | 18.8232 |
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+ | 2.1166 | 0.21 | 1800 | 2.1836 | 32.2611 | 11.174 | 26.0594 | 26.0688 | 18.7633 |
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+ | 2.1053 | 0.24 | 2000 | 2.1929 | 32.3321 | 11.213 | 26.1859 | 26.1903 | 18.7758 |
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+ | 2.1126 | 0.26 | 2200 | 2.1811 | 32.2078 | 11.1792 | 26.0776 | 26.0817 | 18.8197 |
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+ | 2.1038 | 0.28 | 2400 | 2.1836 | 32.2799 | 11.2511 | 26.1191 | 26.1251 | 18.7884 |
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+ | 2.1181 | 0.31 | 2600 | 2.1805 | 32.1197 | 11.1586 | 26.0441 | 26.0441 | 18.8045 |
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+ | 2.1217 | 0.33 | 2800 | 2.1806 | 32.3051 | 11.2638 | 26.1319 | 26.1386 | 18.7886 |
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+ | 2.116 | 0.35 | 3000 | 2.1741 | 32.2799 | 11.1887 | 26.1224 | 26.1363 | 18.7769 |
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+ | 2.1118 | 0.38 | 3200 | 2.1767 | 32.387 | 11.2053 | 26.077 | 26.0845 | 18.8407 |
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+ | 2.1164 | 0.4 | 3400 | 2.1743 | 32.5008 | 11.4021 | 26.3291 | 26.3297 | 18.7731 |
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+ | 2.1068 | 0.42 | 3600 | 2.1673 | 32.2347 | 11.1676 | 26.0657 | 26.0662 | 18.817 |
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+ | 2.1276 | 0.45 | 3800 | 2.1664 | 32.2434 | 11.2862 | 26.094 | 26.0994 | 18.7713 |
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+ | 2.1313 | 0.47 | 4000 | 2.1636 | 32.694 | 11.3724 | 26.4071 | 26.4008 | 18.7709 |
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+ | 2.1229 | 0.49 | 4200 | 2.1633 | 32.456 | 11.4057 | 26.2733 | 26.2689 | 18.7586 |
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+ | 2.129 | 0.52 | 4400 | 2.1641 | 32.309 | 11.2133 | 26.1062 | 26.1121 | 18.7729 |
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+ | 2.1425 | 0.54 | 4600 | 2.1577 | 32.5879 | 11.4001 | 26.3045 | 26.3078 | 18.8104 |
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+ | 2.1536 | 0.56 | 4800 | 2.1507 | 32.5152 | 11.4035 | 26.3054 | 26.3116 | 18.7941 |
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+ | 2.148 | 0.59 | 5000 | 2.1503 | 32.8088 | 11.5641 | 26.5346 | 26.5311 | 18.7602 |
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+ | 2.1541 | 0.61 | 5200 | 2.1491 | 32.8185 | 11.5816 | 26.5261 | 26.527 | 18.7654 |
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+ | 2.155 | 0.64 | 5400 | 2.1466 | 32.7229 | 11.5339 | 26.4363 | 26.442 | 18.8404 |
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+ | 2.1579 | 0.66 | 5600 | 2.1435 | 32.884 | 11.6042 | 26.5862 | 26.5891 | 18.7713 |
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+ | 2.1601 | 0.68 | 5800 | 2.1393 | 32.8027 | 11.5328 | 26.4521 | 26.4567 | 18.7904 |
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+ | 2.1765 | 0.71 | 6000 | 2.1393 | 32.8059 | 11.5751 | 26.5499 | 26.5551 | 18.7768 |
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+ | 2.2176 | 0.73 | 6200 | 2.1345 | 33.0734 | 11.8056 | 26.7546 | 26.7607 | 18.7756 |
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+ | 2.2126 | 0.75 | 6400 | 2.1328 | 32.7478 | 11.5925 | 26.5333 | 26.5359 | 18.7819 |
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+ | 2.1916 | 0.78 | 6600 | 2.1298 | 32.658 | 11.491 | 26.379 | 26.3869 | 18.8101 |
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+ | 2.2162 | 0.8 | 6800 | 2.1297 | 32.7843 | 11.5629 | 26.4736 | 26.4728 | 18.8187 |
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+ | 2.2358 | 0.82 | 7000 | 2.1287 | 32.9181 | 11.6378 | 26.5966 | 26.5987 | 18.8039 |
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+ | 2.2371 | 0.85 | 7200 | 2.1265 | 32.8413 | 11.674 | 26.5905 | 26.5831 | 18.7962 |
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+ | 2.256 | 0.87 | 7400 | 2.1245 | 32.7412 | 11.5627 | 26.4976 | 26.503 | 18.7728 |
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+ | 2.2566 | 0.89 | 7600 | 2.1220 | 32.8165 | 11.6069 | 26.5301 | 26.5295 | 18.7871 |
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+ | 2.2954 | 0.92 | 7800 | 2.1197 | 32.7399 | 11.5417 | 26.4914 | 26.4938 | 18.7752 |
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+ | 2.2766 | 0.94 | 8000 | 2.1187 | 32.853 | 11.6411 | 26.5909 | 26.5938 | 18.7852 |
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+ | 2.3273 | 0.96 | 8200 | 2.1169 | 32.9376 | 11.709 | 26.6665 | 26.6672 | 18.7734 |
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+ | 2.3182 | 0.99 | 8400 | 2.1167 | 32.8631 | 11.658 | 26.6192 | 26.6224 | 18.7663 |
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  ### Framework versions