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mt5-base_V25775

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End of training

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  1. README.md +37 -25
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -6,23 +6,23 @@ tags:
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  metrics:
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  - rouge
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  model-index:
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- - name: mt5-base-finetuned-test_30483_prefix_summarize
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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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- # mt5-base-finetuned-test_30483_prefix_summarize
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  This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.3527
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- - Rouge1: 23.6141
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- - Rouge2: 7.1791
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- - Rougel: 16.0152
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- - Rougelsum: 21.8213
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- - Gen Len: 69.64
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  ## Model description
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@@ -42,29 +42,41 @@ More information needed
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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: 2
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- - eval_batch_size: 2
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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: 15
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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- |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
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- | 7.4668 | 1.25 | 500 | 2.6478 | 10.4597 | 4.2457 | 8.7184 | 9.8473 | 18.49 |
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- | 3.3231 | 2.5 | 1000 | 2.5146 | 17.7315 | 6.0795 | 13.5384 | 16.6839 | 40.55 |
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- | 3.0956 | 3.75 | 1500 | 2.4512 | 20.2871 | 6.4051 | 14.8508 | 18.8768 | 51.35 |
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- | 2.9928 | 5.0 | 2000 | 2.4180 | 21.4196 | 6.629 | 15.2607 | 20.1471 | 57.92 |
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- | 2.8802 | 6.25 | 2500 | 2.4030 | 21.7949 | 6.7926 | 15.2506 | 20.338 | 61.05 |
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- | 2.8243 | 7.5 | 3000 | 2.3856 | 21.7075 | 6.7397 | 15.0044 | 20.1744 | 61.19 |
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- | 2.7646 | 8.75 | 3500 | 2.3847 | 22.4137 | 6.7644 | 14.9987 | 20.7797 | 63.81 |
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- | 2.7096 | 10.0 | 4000 | 2.3691 | 22.3403 | 6.9812 | 15.5411 | 20.6166 | 62.79 |
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- | 2.6758 | 11.25 | 4500 | 2.3612 | 23.6542 | 7.2355 | 15.9979 | 21.9807 | 69.83 |
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- | 2.6579 | 12.5 | 5000 | 2.3556 | 23.7473 | 7.5446 | 16.0314 | 21.917 | 69.75 |
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- | 2.651 | 13.75 | 5500 | 2.3557 | 23.9711 | 7.5018 | 16.2033 | 22.2811 | 69.29 |
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- | 2.639 | 15.0 | 6000 | 2.3527 | 23.6141 | 7.1791 | 16.0152 | 21.8213 | 69.64 |
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - rouge
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  model-index:
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+ - name: mt5-base_V25775
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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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+ # mt5-base_V25775
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  This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.1251
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+ - Rouge1: 28.2174
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+ - Rouge2: 10.5032
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+ - Rougel: 19.8511
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+ - Rougelsum: 23.3756
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+ - Gen Len: 72.3391
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  ## Model description
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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: 3
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+ - eval_batch_size: 3
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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: 20
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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+ |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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+ | 6.8396 | 0.81 | 500 | 2.5158 | 15.7446 | 6.4867 | 12.6043 | 13.8967 | 31.1588 |
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+ | 3.1783 | 1.61 | 1000 | 2.3673 | 21.0031 | 8.2119 | 15.9097 | 17.9958 | 46.0086 |
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+ | 3.0094 | 2.42 | 1500 | 2.3091 | 20.5903 | 8.1394 | 15.7354 | 17.696 | 44.7339 |
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+ | 2.8754 | 3.23 | 2000 | 2.2652 | 22.3129 | 8.6681 | 16.4687 | 18.8755 | 48.9485 |
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+ | 2.7643 | 4.03 | 2500 | 2.2320 | 22.6675 | 8.8846 | 16.7258 | 19.0948 | 48.6781 |
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+ | 2.7 | 4.84 | 3000 | 2.2190 | 24.1409 | 9.4362 | 17.7197 | 20.2512 | 52.8498 |
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+ | 2.6373 | 5.65 | 3500 | 2.2100 | 24.594 | 9.4296 | 18.0182 | 20.6398 | 55.0687 |
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+ | 2.6182 | 6.45 | 4000 | 2.2016 | 25.0763 | 9.432 | 18.1113 | 20.6752 | 57.4549 |
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+ | 2.5552 | 7.26 | 4500 | 2.1767 | 26.6143 | 10.1357 | 19.004 | 22.0372 | 62.6738 |
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+ | 2.5319 | 8.06 | 5000 | 2.1665 | 27.0349 | 10.3809 | 19.3472 | 22.5876 | 64.7167 |
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+ | 2.5145 | 8.87 | 5500 | 2.1705 | 26.6323 | 9.956 | 18.9994 | 22.119 | 62.3176 |
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+ | 2.4923 | 9.68 | 6000 | 2.1499 | 27.0052 | 10.0351 | 19.2887 | 22.4559 | 64.2747 |
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+ | 2.4367 | 10.48 | 6500 | 2.1418 | 27.0134 | 10.1253 | 19.2614 | 22.4648 | 65.2661 |
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+ | 2.4312 | 11.29 | 7000 | 2.1503 | 27.1655 | 9.9501 | 19.1768 | 22.3967 | 66.6953 |
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+ | 2.4186 | 12.1 | 7500 | 2.1370 | 26.6422 | 9.7971 | 19.0065 | 22.0444 | 65.9571 |
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+ | 2.3977 | 12.9 | 8000 | 2.1395 | 27.5204 | 10.3095 | 19.4189 | 22.7497 | 69.0901 |
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+ | 2.3596 | 13.71 | 8500 | 2.1302 | 27.685 | 10.1479 | 19.4521 | 22.7892 | 70.0644 |
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+ | 2.3951 | 14.52 | 9000 | 2.1298 | 27.8389 | 10.2493 | 19.6671 | 22.933 | 70.7897 |
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+ | 2.3433 | 15.32 | 9500 | 2.1238 | 27.9095 | 10.33 | 19.6428 | 22.9721 | 70.4206 |
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+ | 2.3789 | 16.13 | 10000 | 2.1271 | 28.0755 | 10.5819 | 19.9535 | 23.2605 | 69.97 |
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+ | 2.3331 | 16.94 | 10500 | 2.1240 | 28.1362 | 10.4656 | 19.8198 | 23.1857 | 70.9485 |
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+ | 2.3395 | 17.74 | 11000 | 2.1245 | 28.1459 | 10.4803 | 19.801 | 23.2469 | 71.1288 |
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+ | 2.3238 | 18.55 | 11500 | 2.1273 | 28.2156 | 10.4437 | 19.858 | 23.3457 | 73.485 |
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+ | 2.3181 | 19.35 | 12000 | 2.1251 | 28.2174 | 10.5032 | 19.8511 | 23.3756 | 72.3391 |
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  ### Framework versions
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