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---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: results_mt5_xl-_wiki
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results_mt5_xl-_wiki
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0048
- Rouge1: 0.1023
- Rouge2: 0.0125
- Rougel: 0.1022
- Rougelsum: 0.1019
- Gen Len: 18.9302
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 9.7309 | 0.1081 | 500 | 8.7752 | 0.0 | 0.0 | 0.0 | 0.0 | 19.0 |
| 8.2303 | 0.2161 | 1000 | 7.2210 | 0.0 | 0.0 | 0.0 | 0.0 | 19.0 |
| 7.2363 | 0.3242 | 1500 | 6.7270 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 7.0589 | 0.4322 | 2000 | 6.3511 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 6.7392 | 0.5403 | 2500 | 6.2383 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 6.716 | 0.6484 | 3000 | 6.2242 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 6.5459 | 0.7564 | 3500 | 6.1235 | 0.0112 | 0.0 | 0.0112 | 0.0112 | 19.0 |
| 6.3342 | 0.8645 | 4000 | 6.0783 | 0.0738 | 0.0 | 0.0736 | 0.0732 | 12.6417 |
| 6.2404 | 0.9726 | 4500 | 6.0705 | 0.0738 | 0.0 | 0.0736 | 0.0732 | 15.3982 |
| 6.1405 | 1.0806 | 5000 | 6.0811 | 0.0112 | 0.0 | 0.0112 | 0.0112 | 19.0 |
| 6.0387 | 1.1887 | 5500 | 5.8715 | 0.0112 | 0.0 | 0.0112 | 0.0112 | 18.951 |
| 5.7348 | 1.2967 | 6000 | 5.2644 | 0.0392 | 0.0 | 0.0392 | 0.0394 | 0.4314 |
| 5.2214 | 1.4048 | 6500 | 4.8743 | 0.0184 | 0.0 | 0.0184 | 0.0184 | 9.9584 |
| 4.9215 | 1.5129 | 7000 | 4.4677 | 0.0359 | 0.0 | 0.036 | 0.036 | 19.0 |
| 4.6441 | 1.6209 | 7500 | 4.0154 | 0.0738 | 0.0 | 0.0736 | 0.0732 | 19.0 |
| 4.2095 | 1.7290 | 8000 | 3.4679 | 0.0238 | 0.0 | 0.0238 | 0.0238 | 7.9318 |
| 3.6038 | 1.8370 | 8500 | 2.7153 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.9711 | 1.9451 | 9000 | 1.9488 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.2376 | 2.0532 | 9500 | 1.1301 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4721 | 2.1612 | 10000 | 0.5812 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0658 | 2.2693 | 10500 | 0.3416 | 0.0746 | 0.0002 | 0.0743 | 0.0741 | 18.8022 |
| 0.6153 | 2.3774 | 11000 | 0.2139 | 0.0442 | 0.0 | 0.0441 | 0.0441 | 2.7739 |
| 0.4134 | 2.4854 | 11500 | 0.1454 | 0.0 | 0.0 | 0.0 | 0.0 | 0.01 |
| 0.31 | 2.5935 | 12000 | 0.1086 | 0.0831 | 0.002 | 0.0824 | 0.0822 | 18.6766 |
| 0.2423 | 2.7015 | 12500 | 0.0821 | 0.0852 | 0.0038 | 0.0848 | 0.0847 | 18.7947 |
| 0.1862 | 2.8096 | 13000 | 0.0670 | 0.078 | 0.001 | 0.0777 | 0.0778 | 18.8196 |
| 0.1596 | 2.9177 | 13500 | 0.0546 | 0.0875 | 0.0042 | 0.0872 | 0.0871 | 18.8279 |
| 0.13 | 3.0257 | 14000 | 0.0586 | 0.0861 | 0.0047 | 0.0858 | 0.0859 | 18.857 |
| 0.1055 | 3.1338 | 14500 | 0.0393 | 0.081 | 0.003 | 0.0808 | 0.0808 | 18.655 |
| 0.0896 | 3.2418 | 15000 | 0.0348 | 0.0832 | 0.0049 | 0.0828 | 0.0827 | 18.7091 |
| 0.0854 | 3.3499 | 15500 | 0.0310 | 0.0858 | 0.0062 | 0.0854 | 0.0853 | 18.6268 |
| 0.0717 | 3.4580 | 16000 | 0.0284 | 0.091 | 0.0084 | 0.0907 | 0.0908 | 18.803 |
| 0.065 | 3.5660 | 16500 | 0.0250 | 0.0887 | 0.0093 | 0.0885 | 0.0885 | 18.7805 |
| 0.0584 | 3.6741 | 17000 | 0.0229 | 0.089 | 0.0109 | 0.0888 | 0.089 | 18.5179 |
| 0.0511 | 3.7821 | 17500 | 0.0209 | 0.0874 | 0.0093 | 0.0872 | 0.0874 | 18.5154 |
| 0.0502 | 3.8902 | 18000 | 0.0189 | 0.091 | 0.0077 | 0.0909 | 0.0906 | 18.4372 |
| 0.0429 | 3.9983 | 18500 | 0.0172 | 0.0865 | 0.0075 | 0.0863 | 0.0862 | 18.8645 |
| 0.0412 | 4.1063 | 19000 | 0.0160 | 0.0911 | 0.008 | 0.0909 | 0.0907 | 18.5395 |
| 0.0419 | 4.2144 | 19500 | 0.0148 | 0.0913 | 0.0118 | 0.091 | 0.0913 | 18.8736 |
| 0.0353 | 4.3225 | 20000 | 0.0137 | 0.0974 | 0.0103 | 0.097 | 0.097 | 18.6874 |
| 0.0321 | 4.4305 | 20500 | 0.0128 | 0.0936 | 0.0128 | 0.0936 | 0.0934 | 18.5578 |
| 0.029 | 4.5386 | 21000 | 0.0120 | 0.0944 | 0.0128 | 0.094 | 0.0939 | 18.7415 |
| 0.0275 | 4.6466 | 21500 | 0.0117 | 0.0907 | 0.0134 | 0.0905 | 0.0907 | 18.7889 |
| 0.0265 | 4.7547 | 22000 | 0.0108 | 0.0902 | 0.008 | 0.0902 | 0.0899 | 18.5669 |
| 0.2825 | 4.8628 | 22500 | 1.2607 | 0.0958 | 0.0112 | 0.0957 | 0.0957 | 18.749 |
| 0.5612 | 4.9708 | 23000 | 0.1068 | 0.0956 | 0.0108 | 0.0955 | 0.0952 | 18.3699 |
| 0.029 | 5.0789 | 23500 | 0.0090 | 0.0941 | 0.0098 | 0.094 | 0.0939 | 18.3973 |
| 0.023 | 5.1869 | 24000 | 0.0085 | 0.0947 | 0.0099 | 0.0945 | 0.0942 | 18.414 |
| 0.0205 | 5.2950 | 24500 | 0.0085 | 0.1042 | 0.0105 | 0.1038 | 0.1039 | 18.4996 |
| 0.0222 | 5.4031 | 25000 | 0.0081 | 0.0916 | 0.0093 | 0.0916 | 0.0914 | 18.6517 |
| 0.0188 | 5.5111 | 25500 | 0.0078 | 0.0964 | 0.0114 | 0.0961 | 0.0961 | 18.2236 |
| 0.0205 | 5.6192 | 26000 | 0.0074 | 0.1077 | 0.0137 | 0.1077 | 0.1076 | 18.325 |
| 0.018 | 5.7273 | 26500 | 0.0071 | 0.1001 | 0.0112 | 0.1 | 0.0995 | 18.6575 |
| 0.0173 | 5.8353 | 27000 | 0.0068 | 0.095 | 0.0096 | 0.0949 | 0.0947 | 18.5669 |
| 0.0162 | 5.9434 | 27500 | 0.0066 | 0.0946 | 0.0091 | 0.0948 | 0.0944 | 18.4871 |
| 0.0145 | 6.0514 | 28000 | 0.0064 | 0.098 | 0.0112 | 0.0979 | 0.0979 | 18.6301 |
| 0.0154 | 6.1595 | 28500 | 0.0064 | 0.0953 | 0.0101 | 0.095 | 0.0952 | 18.4838 |
| 0.0149 | 6.2676 | 29000 | 0.0062 | 0.1013 | 0.0137 | 0.1011 | 0.1011 | 18.2577 |
| 0.014 | 6.3756 | 29500 | 0.0062 | 0.0964 | 0.0141 | 0.0961 | 0.0966 | 18.5195 |
| 0.0142 | 6.4837 | 30000 | 0.0060 | 0.1028 | 0.0143 | 0.1022 | 0.1025 | 18.5062 |
| 0.0138 | 6.5917 | 30500 | 0.0060 | 0.0998 | 0.0141 | 0.0993 | 0.0994 | 18.5794 |
| 0.0124 | 6.6998 | 31000 | 0.0059 | 0.0957 | 0.0113 | 0.0955 | 0.0957 | 18.4938 |
| 0.0119 | 6.8079 | 31500 | 0.0058 | 0.0968 | 0.0113 | 0.0963 | 0.0965 | 18.6401 |
| 0.0132 | 6.9159 | 32000 | 0.0057 | 0.0949 | 0.0104 | 0.0946 | 0.0947 | 18.5628 |
| 0.0129 | 7.0240 | 32500 | 0.0056 | 0.0952 | 0.0146 | 0.0947 | 0.0949 | 18.4406 |
| 0.0125 | 7.1321 | 33000 | 0.0055 | 0.0986 | 0.0127 | 0.0983 | 0.0983 | 18.5777 |
| 0.0107 | 7.2401 | 33500 | 0.0054 | 0.0985 | 0.0137 | 0.0979 | 0.0982 | 18.4264 |
| 0.0228 | 7.3482 | 34000 | 0.0054 | 0.0971 | 0.0113 | 0.0968 | 0.0969 | 18.744 |
| 0.011 | 7.4562 | 34500 | 0.0054 | 0.1089 | 0.0136 | 0.1088 | 0.1086 | 18.9069 |
| 0.0106 | 7.5643 | 35000 | 0.0054 | 0.1058 | 0.0133 | 0.1057 | 0.1055 | 18.7149 |
| 0.0102 | 7.6724 | 35500 | 0.0053 | 0.0957 | 0.0105 | 0.0954 | 0.0953 | 18.6342 |
| 0.0108 | 7.7804 | 36000 | 0.0052 | 0.1028 | 0.0131 | 0.1027 | 0.1024 | 18.9069 |
| 0.0106 | 7.8885 | 36500 | 0.0052 | 0.1075 | 0.0153 | 0.1074 | 0.1073 | 18.6841 |
| 0.0122 | 7.9965 | 37000 | 0.0051 | 0.0995 | 0.0106 | 0.0995 | 0.0993 | 18.7606 |
| 0.0097 | 8.1046 | 37500 | 0.0051 | 0.1128 | 0.0175 | 0.1125 | 0.1125 | 18.985 |
| 0.0098 | 8.2127 | 38000 | 0.0051 | 0.1006 | 0.0102 | 0.1003 | 0.1003 | 18.9701 |
| 0.0095 | 8.3207 | 38500 | 0.0050 | 0.1025 | 0.0105 | 0.1023 | 0.1021 | 18.7897 |
| 0.009 | 8.4288 | 39000 | 0.0050 | 0.1004 | 0.0088 | 0.1003 | 0.1002 | 18.7697 |
| 0.0095 | 8.5368 | 39500 | 0.0050 | 0.1023 | 0.0097 | 0.1023 | 0.1023 | 18.8238 |
| 0.0095 | 8.6449 | 40000 | 0.0050 | 0.0979 | 0.0091 | 0.0979 | 0.0977 | 18.8196 |
| 0.0092 | 8.7530 | 40500 | 0.0049 | 0.1011 | 0.01 | 0.1011 | 0.1009 | 18.926 |
| 0.0091 | 8.8610 | 41000 | 0.0049 | 0.1016 | 0.0103 | 0.1017 | 0.1015 | 18.9302 |
| 0.0091 | 8.9691 | 41500 | 0.0049 | 0.1016 | 0.0111 | 0.1013 | 0.1012 | 18.9302 |
| 0.0089 | 9.0772 | 42000 | 0.0049 | 0.1044 | 0.0113 | 0.1041 | 0.104 | 18.9302 |
| 0.0089 | 9.1852 | 42500 | 0.0049 | 0.1016 | 0.0111 | 0.1013 | 0.1012 | 18.9302 |
| 0.0089 | 9.2933 | 43000 | 0.0049 | 0.1031 | 0.0125 | 0.103 | 0.1027 | 18.9135 |
| 0.0088 | 9.4013 | 43500 | 0.0049 | 0.1009 | 0.0111 | 0.1007 | 0.1004 | 18.9302 |
| 0.0084 | 9.5094 | 44000 | 0.0049 | 0.1022 | 0.0114 | 0.1022 | 0.1019 | 18.9302 |
| 0.0087 | 9.6175 | 44500 | 0.0048 | 0.1031 | 0.0125 | 0.103 | 0.1027 | 18.9302 |
| 0.0082 | 9.7255 | 45000 | 0.0049 | 0.1015 | 0.0114 | 0.1014 | 0.1011 | 18.9302 |
| 0.0084 | 9.8336 | 45500 | 0.0048 | 0.1023 | 0.0125 | 0.1022 | 0.1019 | 18.9302 |
| 0.0081 | 9.9416 | 46000 | 0.0048 | 0.1023 | 0.0125 | 0.1022 | 0.1019 | 18.9302 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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