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t5-small-finetuned-samsum-en

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update model card README.md

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@@ -20,7 +20,7 @@ model-index:
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  metrics:
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  - name: Rouge1
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  type: rouge
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- value: 42.3215
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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
@@ -30,11 +30,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.7863
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- - Rouge1: 42.3215
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- - Rouge2: 19.4644
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- - Rougel: 35.3715
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- - Rougelsum: 39.1274
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  ## Model description
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@@ -59,32 +59,22 @@ The following hyperparameters were used during training:
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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 |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
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- | 2.2448 | 1.0 | 300 | 1.8993 | 39.5059 | 17.0654 | 32.9974 | 36.6153 |
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- | 2.0428 | 2.0 | 600 | 1.8499 | 40.0529 | 17.4367 | 33.4804 | 37.057 |
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- | 1.9626 | 3.0 | 900 | 1.8278 | 40.7994 | 17.918 | 34.0773 | 37.6219 |
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- | 1.8992 | 4.0 | 1200 | 1.8118 | 41.3782 | 18.5579 | 34.7794 | 38.4994 |
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- | 1.8429 | 5.0 | 1500 | 1.8006 | 41.8624 | 18.7592 | 34.9262 | 38.7019 |
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- | 1.8057 | 6.0 | 1800 | 1.7988 | 41.1316 | 18.5242 | 34.7271 | 38.2821 |
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- | 1.775 | 7.0 | 2100 | 1.7856 | 42.2036 | 19.3343 | 35.4442 | 39.2114 |
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- | 1.7376 | 8.0 | 2400 | 1.7797 | 41.9569 | 18.9482 | 35.1953 | 38.7609 |
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- | 1.7096 | 9.0 | 2700 | 1.7780 | 42.6065 | 19.2152 | 35.4563 | 39.2736 |
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- | 1.6885 | 10.0 | 3000 | 1.7826 | 42.1595 | 18.8477 | 34.8679 | 38.9388 |
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- | 1.6581 | 11.0 | 3300 | 1.7809 | 42.291 | 19.0846 | 35.1938 | 38.894 |
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- | 1.6392 | 12.0 | 3600 | 1.7824 | 42.3588 | 19.4507 | 35.4588 | 39.2067 |
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- | 1.6258 | 13.0 | 3900 | 1.7806 | 42.0932 | 19.002 | 35.0112 | 38.8053 |
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- | 1.6042 | 14.0 | 4200 | 1.7828 | 42.0564 | 19.3141 | 35.2479 | 38.8301 |
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- | 1.5993 | 15.0 | 4500 | 1.7824 | 42.6056 | 19.5164 | 35.4112 | 39.2322 |
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- | 1.5869 | 16.0 | 4800 | 1.7839 | 42.1505 | 19.1529 | 35.0853 | 38.8788 |
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- | 1.5778 | 17.0 | 5100 | 1.7827 | 42.5416 | 19.5103 | 35.5507 | 39.293 |
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- | 1.5716 | 18.0 | 5400 | 1.7865 | 42.3028 | 19.3783 | 35.3466 | 39.0594 |
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- | 1.5615 | 19.0 | 5700 | 1.7857 | 42.4001 | 19.5111 | 35.4686 | 39.1614 |
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- | 1.5606 | 20.0 | 6000 | 1.7863 | 42.3215 | 19.4644 | 35.3715 | 39.1274 |
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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: 44.3313
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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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  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.9335
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+ - Rouge1: 44.3313
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+ - Rouge2: 20.71
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+ - Rougel: 37.221
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+ - Rougelsum: 40.9603
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  ## Model description
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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: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
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+ | 1.4912 | 1.0 | 300 | 1.9043 | 44.1517 | 20.0186 | 36.6053 | 40.5164 |
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+ | 1.5055 | 2.0 | 600 | 1.8912 | 44.1473 | 20.4456 | 37.069 | 40.6714 |
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+ | 1.4852 | 3.0 | 900 | 1.8986 | 44.7536 | 20.8646 | 37.525 | 41.2189 |
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+ | 1.4539 | 4.0 | 1200 | 1.9136 | 44.2144 | 20.3446 | 37.1088 | 40.7581 |
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+ | 1.4262 | 5.0 | 1500 | 1.9215 | 44.2656 | 20.6044 | 37.3267 | 40.9469 |
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+ | 1.4118 | 6.0 | 1800 | 1.9247 | 43.8793 | 20.4663 | 37.0614 | 40.6065 |
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+ | 1.3987 | 7.0 | 2100 | 1.9256 | 43.9981 | 20.2703 | 36.7856 | 40.6354 |
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+ | 1.3822 | 8.0 | 2400 | 1.9316 | 43.9732 | 20.4559 | 36.8039 | 40.5784 |
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+ | 1.3773 | 9.0 | 2700 | 1.9314 | 44.3075 | 20.5435 | 37.0457 | 40.832 |
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+ | 1.3795 | 10.0 | 3000 | 1.9335 | 44.3313 | 20.71 | 37.221 | 40.9603 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions