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mT5-TextSimp-LT-BatchSize4-lr5e-5

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community
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  1. README.md +25 -23
README.md CHANGED
@@ -5,22 +5,24 @@ tags:
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  - generated_from_trainer
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  metrics:
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  - rouge
 
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  model-index:
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- - name: mT5-TextSimp-LT-BatchSize12-lr1e-4
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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-TextSimp-LT-BatchSize12-lr1e-4
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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: 0.1611
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- - Rouge1: 0.4599
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- - Rouge2: 0.2777
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- - Rougel: 0.4471
 
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  - Gen Len: 39.0358
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  ## Model description
@@ -51,24 +53,24 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Gen Len |
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- |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:|
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- | 36.6446 | 0.48 | 200 | 31.2765 | 0.0004 | 0.0 | 0.0004 | 512.0 |
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- | 11.5223 | 0.96 | 400 | 6.7786 | 0.003 | 0.0 | 0.0031 | 89.2816 |
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- | 2.2686 | 1.44 | 600 | 0.6729 | 0.0053 | 0.0 | 0.0053 | 39.0501 |
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- | 0.7009 | 1.91 | 800 | 0.6529 | 0.0029 | 0.0 | 0.0027 | 41.401 |
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- | 0.6213 | 2.39 | 1000 | 0.5630 | 0.0057 | 0.0002 | 0.0056 | 39.0334 |
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- | 0.6435 | 2.87 | 1200 | 0.4697 | 0.0685 | 0.0084 | 0.0611 | 39.0453 |
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- | 0.4154 | 3.35 | 1400 | 10.4655 | 0.2093 | 0.1217 | 0.2006 | 350.0334 |
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- | 0.6289 | 3.83 | 1600 | 1.9257 | 0.317 | 0.1946 | 0.3064 | 138.7494 |
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- | 3.5542 | 4.31 | 1800 | 0.8459 | 0.3735 | 0.204 | 0.3613 | 59.8568 |
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- | 8.1736 | 4.78 | 2000 | 7.2350 | 0.3144 | 0.1819 | 0.3037 | 289.1432 |
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- | 2.3987 | 5.26 | 2200 | 0.8361 | 0.3621 | 0.1911 | 0.351 | 61.0668 |
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- | 0.9853 | 5.74 | 2400 | 0.4219 | 0.3634 | 0.2008 | 0.352 | 46.494 |
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- | 0.3575 | 6.22 | 2600 | 0.3516 | 0.3805 | 0.2128 | 0.3689 | 46.1623 |
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- | 0.4497 | 6.7 | 2800 | 0.2597 | 0.4388 | 0.2698 | 0.4269 | 42.2697 |
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- | 0.2582 | 7.18 | 3000 | 0.1583 | 0.4444 | 0.2587 | 0.4316 | 38.1671 |
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- | 0.2629 | 7.66 | 3200 | 0.1611 | 0.4599 | 0.2777 | 0.4471 | 39.0358 |
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  ### Framework versions
 
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  - generated_from_trainer
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  metrics:
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  - rouge
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+ - sacrebleu
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  model-index:
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+ - name: mT5-TextSimp-LT-BatchSize4-lr5e-5
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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-TextSimp-LT-BatchSize4-lr5e-5
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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: 0.1611
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+ - Rouge1: 0.46
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+ - Rouge2: 0.2767
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+ - Rougel: 0.4464
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+ - Sacrebleu: 23.2936
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  - Gen Len: 39.0358
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  ## Model description
 
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Sacrebleu | Gen Len |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
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+ | 36.6446 | 0.48 | 200 | 31.2765 | 0.0004 | 0.0 | 0.0004 | 0.0003 | 512.0 |
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+ | 11.5223 | 0.96 | 400 | 6.7786 | 0.0031 | 0.0 | 0.0031 | 0.0045 | 89.2816 |
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+ | 2.2686 | 1.44 | 600 | 0.6729 | 0.0054 | 0.0 | 0.0053 | 0.0196 | 39.0501 |
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+ | 0.7009 | 1.91 | 800 | 0.6529 | 0.0029 | 0.0 | 0.0027 | 0.0424 | 41.401 |
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+ | 0.6213 | 2.39 | 1000 | 0.5630 | 0.0058 | 0.0002 | 0.0056 | 0.0201 | 39.0334 |
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+ | 0.6435 | 2.87 | 1200 | 0.4697 | 0.0688 | 0.0084 | 0.0608 | 0.1156 | 39.0453 |
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+ | 0.4154 | 3.35 | 1400 | 10.4655 | 0.2098 | 0.1219 | 0.2011 | 0.671 | 350.0334 |
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+ | 0.6289 | 3.83 | 1600 | 1.9257 | 0.3176 | 0.1945 | 0.3072 | 3.6031 | 138.7494 |
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+ | 3.5542 | 4.31 | 1800 | 0.8459 | 0.373 | 0.2029 | 0.3615 | 16.8305 | 59.8568 |
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+ | 8.1736 | 4.78 | 2000 | 7.2350 | 0.3147 | 0.1815 | 0.3033 | 7.3572 | 289.1432 |
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+ | 2.3987 | 5.26 | 2200 | 0.8361 | 0.3616 | 0.1903 | 0.3501 | 16.2229 | 61.0668 |
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+ | 0.9853 | 5.74 | 2400 | 0.4219 | 0.3635 | 0.2004 | 0.3515 | 15.2744 | 46.494 |
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+ | 0.3575 | 6.22 | 2600 | 0.3516 | 0.3796 | 0.2121 | 0.3687 | 13.6464 | 46.1623 |
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+ | 0.4497 | 6.7 | 2800 | 0.2597 | 0.4392 | 0.2698 | 0.4263 | 18.9423 | 42.2697 |
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+ | 0.2582 | 7.18 | 3000 | 0.1583 | 0.4442 | 0.2579 | 0.431 | 21.5533 | 38.1671 |
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+ | 0.2629 | 7.66 | 3200 | 0.1611 | 0.46 | 0.2767 | 0.4464 | 23.2936 | 39.0358 |
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  ### Framework versions