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elliotthwang
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t5-small-finetuned-xlsum-chinese-tradition

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update model card README.md

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@@ -19,7 +19,7 @@ model-index:
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  metrics:
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  - name: Rouge1
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  type: rouge
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- value: 0.7966
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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
@@ -29,12 +29,12 @@ 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 xlsum dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.2848
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- - Rouge1: 0.7966
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- - Rouge2: 0.0942
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- - Rougel: 0.7989
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- - Rougelsum: 0.7967
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- - Gen Len: 7.0854
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  ## Model description
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@@ -59,14 +59,19 @@ 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: 1
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  - mixed_precision_training: Native AMP
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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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- | 1.4457 | 1.0 | 2336 | 1.2848 | 0.7966 | 0.0942 | 0.7989 | 0.7967 | 7.0854 |
 
 
 
 
 
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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: 0.8887
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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 xlsum dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2061
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+ - Rouge1: 0.8887
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+ - Rouge2: 0.0671
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+ - Rougel: 0.889
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+ - Rougelsum: 0.8838
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+ - Gen Len: 6.8779
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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: 6
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  - mixed_precision_training: Native AMP
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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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+ | 1.4231 | 1.0 | 2336 | 1.2586 | 0.711 | 0.0528 | 0.7029 | 0.7053 | 7.3368 |
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+ | 1.378 | 2.0 | 4672 | 1.2281 | 0.9688 | 0.05 | 0.9574 | 0.9656 | 7.0392 |
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+ | 1.3567 | 3.0 | 7008 | 1.2182 | 0.9534 | 0.1035 | 0.9531 | 0.9472 | 6.7437 |
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+ | 1.3339 | 4.0 | 9344 | 1.2096 | 0.9969 | 0.0814 | 0.9969 | 0.9938 | 7.4503 |
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+ | 1.3537 | 5.0 | 11680 | 1.2072 | 0.8429 | 0.0742 | 0.8372 | 0.838 | 6.8049 |
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+ | 1.3351 | 6.0 | 14016 | 1.2061 | 0.8887 | 0.0671 | 0.889 | 0.8838 | 6.8779 |
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  ### Framework versions