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eschorn
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2_smtg

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
Eval Results
Inference Endpoints
text-generation-inference
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update model card README.md

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@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Rouge1
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  type: rouge
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- value: 0.083
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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
@@ -31,12 +31,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the billsum dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: nan
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- - Rouge1: 0.083
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- - Rouge2: 0.0213
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- - Rougel: 0.0689
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- - Rougelsum: 0.0688
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- - Gen Len: 13.4718
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  ## Model description
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@@ -62,32 +62,31 @@ The following hyperparameters were used during training:
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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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- - 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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- | No log | 1.0 | 124 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | No log | 2.0 | 248 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | No log | 3.0 | 372 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | No log | 4.0 | 496 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 5.0 | 620 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 6.0 | 744 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 7.0 | 868 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 8.0 | 992 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 9.0 | 1116 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 10.0 | 1240 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 11.0 | 1364 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 12.0 | 1488 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 13.0 | 1612 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 14.0 | 1736 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 15.0 | 1860 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 16.0 | 1984 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 17.0 | 2108 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 18.0 | 2232 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 19.0 | 2356 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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- | 0.0 | 20.0 | 2480 | nan | 0.083 | 0.0213 | 0.0689 | 0.0688 | 13.4718 |
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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: 0.1982
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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 [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the billsum dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.9346
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+ - Rouge1: 0.1982
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+ - Rouge2: 0.1052
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+ - Rougel: 0.1709
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+ - Rougelsum: 0.1711
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+ - Gen Len: 19.0
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  ## Model description
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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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+ | No log | 1.0 | 124 | 2.2154 | 0.1881 | 0.0892 | 0.1571 | 0.157 | 18.996 |
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+ | No log | 2.0 | 248 | 2.1455 | 0.2003 | 0.1039 | 0.1695 | 0.1696 | 19.0 |
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+ | No log | 3.0 | 372 | 2.0963 | 0.2011 | 0.1043 | 0.1706 | 0.1706 | 19.0 |
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+ | No log | 4.0 | 496 | 2.0696 | 0.2014 | 0.105 | 0.1708 | 0.1708 | 19.0 |
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+ | 2.4198 | 5.0 | 620 | 2.0437 | 0.1991 | 0.1016 | 0.1693 | 0.1694 | 19.0 |
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+ | 2.4198 | 6.0 | 744 | 2.0256 | 0.1983 | 0.1016 | 0.1694 | 0.1695 | 19.0 |
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+ | 2.4198 | 7.0 | 868 | 2.0109 | 0.2003 | 0.1044 | 0.1702 | 0.1705 | 19.0 |
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+ | 2.4198 | 8.0 | 992 | 1.9969 | 0.1981 | 0.1025 | 0.1692 | 0.1694 | 19.0 |
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+ | 2.2056 | 9.0 | 1116 | 1.9849 | 0.1984 | 0.103 | 0.1696 | 0.1699 | 19.0 |
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+ | 2.2056 | 10.0 | 1240 | 1.9738 | 0.1985 | 0.1032 | 0.1702 | 0.1704 | 19.0 |
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+ | 2.2056 | 11.0 | 1364 | 1.9661 | 0.1976 | 0.1029 | 0.1694 | 0.1697 | 19.0 |
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+ | 2.2056 | 12.0 | 1488 | 1.9591 | 0.1986 | 0.1038 | 0.1704 | 0.1706 | 19.0 |
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+ | 2.1209 | 13.0 | 1612 | 1.9535 | 0.1994 | 0.1045 | 0.1708 | 0.1709 | 19.0 |
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+ | 2.1209 | 14.0 | 1736 | 1.9486 | 0.1986 | 0.1047 | 0.1706 | 0.1708 | 19.0 |
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+ | 2.1209 | 15.0 | 1860 | 1.9440 | 0.1988 | 0.1053 | 0.1709 | 0.1711 | 19.0 |
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+ | 2.1209 | 16.0 | 1984 | 1.9406 | 0.1983 | 0.1057 | 0.1708 | 0.1709 | 19.0 |
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+ | 2.0754 | 17.0 | 2108 | 1.9378 | 0.199 | 0.1062 | 0.1712 | 0.1712 | 19.0 |
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+ | 2.0754 | 18.0 | 2232 | 1.9361 | 0.1986 | 0.1057 | 0.1713 | 0.1714 | 19.0 |
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+ | 2.0754 | 19.0 | 2356 | 1.9348 | 0.1986 | 0.1056 | 0.1712 | 0.1713 | 19.0 |
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+ | 2.0754 | 20.0 | 2480 | 1.9346 | 0.1982 | 0.1052 | 0.1709 | 0.1711 | 19.0 |
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  ### Framework versions