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R-facebook-bart-base-full-ft-with-tum-nlp-german-gpt2_easy-prior-pp-no-ls-4c77

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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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+ metrics:
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+ - sacrebleu
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+ - bleu
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+ - rouge
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+ model-index:
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+ - name: R-facebook-bart-base-full-ft-with-tum-nlp-german-gpt2_easy-prior-pp-no-ls-4c77
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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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+ # R-facebook-bart-base-full-ft-with-tum-nlp-german-gpt2_easy-prior-pp-no-ls-4c77
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+
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+ This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 4.1506
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+ - Sacrebleu: 7.6134
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+ - Bleu: 0.0761
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+ - Rouge1: 0.3006
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+ - Rouge2: 0.1038
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+ - Rougel: 0.2079
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+ - Sari: 39.5909
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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: 5e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 32
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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_steps: 100
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+ - num_epochs: 15
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+ - mixed_precision_training: Native AMP
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+ - label_smoothing_factor: 0.1
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Sacrebleu | Bleu | Rouge1 | Rouge2 | Rougel | Sari |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:------:|:------:|:-------:|
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+ | 6.9721 | 0.25 | 100 | 4.1739 | 1.8048 | 0.0180 | 0.1980 | 0.0611 | 0.1541 | 37.1235 |
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+ | 3.8977 | 0.5 | 200 | 4.0984 | 1.2756 | 0.0128 | 0.2076 | 0.0678 | 0.1581 | 37.6186 |
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+ | 4.035 | 0.75 | 300 | 4.0622 | 2.6499 | 0.0265 | 0.2271 | 0.0740 | 0.1741 | 38.1373 |
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+ | 8.2055 | 0.99 | 400 | 4.0561 | 2.7363 | 0.0274 | 0.2332 | 0.0804 | 0.1716 | 38.0851 |
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+ | 3.6957 | 1.24 | 500 | 4.0262 | 3.5110 | 0.0351 | 0.2560 | 0.0852 | 0.1852 | 37.9403 |
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+ | 3.0846 | 1.49 | 600 | 4.0121 | 3.2967 | 0.0330 | 0.2471 | 0.0815 | 0.1799 | 37.5590 |
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+ | 3.283 | 1.74 | 700 | 4.0510 | 3.8512 | 0.0385 | 0.2602 | 0.0917 | 0.1951 | 38.0037 |
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+ | 4.7429 | 1.99 | 800 | 4.0048 | 3.4891 | 0.0349 | 0.2524 | 0.0850 | 0.1877 | 38.0324 |
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+ | 3.024 | 2.24 | 900 | 3.9860 | 3.9202 | 0.0392 | 0.2633 | 0.0844 | 0.1891 | 37.9931 |
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+ | 5.6861 | 2.49 | 1000 | 4.0493 | 4.4801 | 0.0448 | 0.2622 | 0.0878 | 0.1926 | 38.2052 |
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+ | 3.6185 | 2.74 | 1100 | 4.0394 | 3.6710 | 0.0367 | 0.2608 | 0.0857 | 0.1866 | 37.9620 |
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+ | 3.3582 | 2.98 | 1200 | 4.0004 | 5.1257 | 0.0513 | 0.2695 | 0.0922 | 0.1956 | 38.4845 |
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+ | 5.0036 | 3.23 | 1300 | 4.0223 | 5.3256 | 0.0533 | 0.2752 | 0.0938 | 0.1975 | 38.6943 |
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+ | 3.9904 | 3.48 | 1400 | 4.0040 | 5.0070 | 0.0501 | 0.2744 | 0.0927 | 0.1951 | 38.5338 |
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+ | 3.1496 | 3.73 | 1500 | 4.0282 | 5.9234 | 0.0592 | 0.2803 | 0.0907 | 0.2002 | 38.2119 |
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+ | 3.9604 | 3.98 | 1600 | 4.0253 | 5.1875 | 0.0519 | 0.2658 | 0.0864 | 0.1920 | 38.2336 |
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+ | 2.9813 | 4.23 | 1700 | 4.0148 | 5.9589 | 0.0596 | 0.2891 | 0.0976 | 0.2028 | 38.8216 |
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+ | 3.5448 | 4.48 | 1800 | 4.0071 | 5.2759 | 0.0528 | 0.2736 | 0.0867 | 0.1894 | 37.8800 |
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+ | 3.6836 | 4.72 | 1900 | 4.0105 | 5.1414 | 0.0514 | 0.2750 | 0.0894 | 0.1982 | 38.3898 |
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+ | 4.0471 | 4.97 | 2000 | 3.9788 | 5.5747 | 0.0557 | 0.2792 | 0.0932 | 0.1973 | 38.5705 |
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+ | 3.3437 | 5.22 | 2100 | 4.0057 | 5.3969 | 0.0540 | 0.2827 | 0.0926 | 0.1978 | 38.3453 |
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+ | 3.1657 | 5.47 | 2200 | 4.0439 | 5.4820 | 0.0548 | 0.2861 | 0.0946 | 0.2071 | 38.4004 |
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+ | 2.5486 | 5.72 | 2300 | 4.0315 | 6.1738 | 0.0617 | 0.2896 | 0.0966 | 0.2048 | 38.5404 |
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+ | 3.6148 | 5.97 | 2400 | 4.0056 | 6.5570 | 0.0656 | 0.2941 | 0.1046 | 0.2072 | 39.0698 |
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+ | 3.1477 | 6.22 | 2500 | 4.0612 | 6.2221 | 0.0622 | 0.2806 | 0.0932 | 0.1998 | 38.5211 |
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+ | 3.175 | 6.47 | 2600 | 4.0126 | 6.6920 | 0.0669 | 0.2916 | 0.1037 | 0.2122 | 39.1438 |
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+ | 4.6616 | 6.71 | 2700 | 4.0467 | 6.0344 | 0.0603 | 0.2804 | 0.0953 | 0.1983 | 38.4171 |
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+ | 3.109 | 6.96 | 2800 | 4.0420 | 5.8656 | 0.0587 | 0.2864 | 0.0983 | 0.2034 | 38.7225 |
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+ | 3.0659 | 7.21 | 2900 | 4.0613 | 5.6029 | 0.0560 | 0.2839 | 0.0938 | 0.1980 | 38.7136 |
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+ | 2.658 | 7.46 | 3000 | 4.0726 | 6.2791 | 0.0628 | 0.2824 | 0.0947 | 0.1972 | 38.6330 |
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+ | 3.178 | 7.71 | 3100 | 4.0437 | 6.4351 | 0.0644 | 0.2924 | 0.0956 | 0.2032 | 38.6577 |
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+ | 4.0606 | 7.96 | 3200 | 4.0644 | 6.6271 | 0.0663 | 0.2966 | 0.1019 | 0.2088 | 39.1513 |
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+ | 3.664 | 8.21 | 3300 | 4.0615 | 6.3354 | 0.0634 | 0.2961 | 0.0981 | 0.2024 | 38.6904 |
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+ | 2.8457 | 8.46 | 3400 | 4.0861 | 7.4278 | 0.0743 | 0.2975 | 0.1025 | 0.2017 | 39.0452 |
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+ | 3.3883 | 8.7 | 3500 | 4.1037 | 6.4498 | 0.0645 | 0.2826 | 0.0955 | 0.2008 | 38.5961 |
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+ | 5.4189 | 8.95 | 3600 | 4.1099 | 6.0065 | 0.0601 | 0.2946 | 0.0952 | 0.2020 | 38.6177 |
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+ | 3.2093 | 9.2 | 3700 | 4.1074 | 6.2514 | 0.0625 | 0.2933 | 0.0942 | 0.2014 | 38.7227 |
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+ | 3.9625 | 9.45 | 3800 | 4.0937 | 6.6653 | 0.0667 | 0.2912 | 0.0970 | 0.2020 | 38.4853 |
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+ | 2.7172 | 9.7 | 3900 | 4.1130 | 6.1736 | 0.0617 | 0.2860 | 0.0898 | 0.1948 | 38.5064 |
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+ | 2.4973 | 9.95 | 4000 | 4.0737 | 7.4889 | 0.0749 | 0.2986 | 0.1023 | 0.2060 | 39.2124 |
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+ | 2.7371 | 10.2 | 4100 | 4.1032 | 6.4897 | 0.0649 | 0.2985 | 0.0990 | 0.2031 | 38.3514 |
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+ | 3.9244 | 10.44 | 4200 | 4.0880 | 6.7268 | 0.0673 | 0.2906 | 0.1006 | 0.2012 | 38.6404 |
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+ | 3.2153 | 10.69 | 4300 | 4.0961 | 6.7780 | 0.0678 | 0.2953 | 0.0977 | 0.2008 | 38.7091 |
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+ | 3.0715 | 10.94 | 4400 | 4.1005 | 7.1435 | 0.0714 | 0.2870 | 0.0937 | 0.1950 | 38.5542 |
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+ | 2.7833 | 11.19 | 4500 | 4.1112 | 7.5856 | 0.0759 | 0.3008 | 0.1037 | 0.2063 | 38.8659 |
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+ | 5.6278 | 11.44 | 4600 | 4.0988 | 7.8870 | 0.0789 | 0.2962 | 0.1019 | 0.2025 | 38.8174 |
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+ | 4.3557 | 11.69 | 4700 | 4.1049 | 7.9121 | 0.0791 | 0.3105 | 0.1076 | 0.2106 | 39.2476 |
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+ | 3.4938 | 11.94 | 4800 | 4.1067 | 7.1602 | 0.0716 | 0.2961 | 0.1009 | 0.2039 | 38.9165 |
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+ | 5.6848 | 12.19 | 4900 | 4.1140 | 7.8746 | 0.0787 | 0.2951 | 0.0996 | 0.2005 | 38.7719 |
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+ | 3.4738 | 12.43 | 5000 | 4.0969 | 7.8672 | 0.0787 | 0.3055 | 0.1087 | 0.2092 | 39.0808 |
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+ | 2.9039 | 12.68 | 5100 | 4.1185 | 7.6696 | 0.0767 | 0.3033 | 0.1071 | 0.2092 | 39.0788 |
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+ | 4.4091 | 12.93 | 5200 | 4.1346 | 7.9896 | 0.0799 | 0.3014 | 0.1046 | 0.2070 | 39.2032 |
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+ | 3.102 | 13.18 | 5300 | 4.1308 | 7.2969 | 0.0730 | 0.3030 | 0.1032 | 0.2039 | 39.1031 |
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+ | 2.9972 | 13.43 | 5400 | 4.1518 | 7.7779 | 0.0778 | 0.3017 | 0.1053 | 0.2090 | 39.4092 |
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+ | 2.7672 | 13.68 | 5500 | 4.1515 | 7.7545 | 0.0775 | 0.3010 | 0.1079 | 0.2091 | 39.0093 |
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+ | 3.7358 | 13.93 | 5600 | 4.1360 | 7.5980 | 0.0760 | 0.2970 | 0.1036 | 0.2080 | 39.0873 |
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+ | 3.4363 | 14.17 | 5700 | 4.1367 | 7.2901 | 0.0729 | 0.3013 | 0.1057 | 0.2084 | 39.3389 |
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+ | 3.3451 | 14.42 | 5800 | 4.1500 | 7.5605 | 0.0756 | 0.2984 | 0.0979 | 0.2074 | 39.0107 |
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+ | 2.8616 | 14.67 | 5900 | 4.1447 | 7.8204 | 0.0782 | 0.3020 | 0.1059 | 0.2127 | 39.7465 |
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+ | 3.1149 | 14.92 | 6000 | 4.1506 | 7.6134 | 0.0761 | 0.3006 | 0.1038 | 0.2079 | 39.5909 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.29.2
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+ - Pytorch 2.0.0+cu117
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+ - Datasets 2.12.0
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+ - Tokenizers 0.13.3