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t5-small-machine-articles-tag-generation

Summarization
Transformers
PyTorch
TensorBoard
English
t5
text2text-generation
Generated from Trainer
machine_learning
article_tag
tag_generation
ml_article_tag
blog_tag_generation
tagging
text-generation-inference
Inference Endpoints
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update model card README.md

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@@ -16,12 +16,12 @@ 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 None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.9833
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- - Rouge1: 35.3543
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- - Rouge2: 18.1226
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- - Rougel: 31.3958
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- - Rougelsum: 31.414
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- - Gen Len: 17.6596
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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- | 3.7917 | 1.0 | 47 | 3.0002 | 19.9138 | 6.9215 | 17.6969 | 17.7888 | 18.9787 |
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- | 3.0113 | 2.0 | 94 | 2.5823 | 22.9993 | 9.0341 | 20.8118 | 20.7657 | 18.7021 |
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- | 2.7086 | 3.0 | 141 | 2.3643 | 26.7716 | 12.2207 | 24.1983 | 24.2611 | 18.3298 |
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- | 2.5192 | 4.0 | 188 | 2.2361 | 28.5866 | 13.6305 | 26.1201 | 26.1367 | 17.9894 |
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- | 2.4089 | 5.0 | 235 | 2.1661 | 30.1919 | 13.8779 | 27.1523 | 27.1256 | 18.0638 |
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- | 2.3293 | 6.0 | 282 | 2.1185 | 31.1222 | 15.6736 | 27.3953 | 27.4457 | 17.8404 |
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- | 2.2635 | 7.0 | 329 | 2.0875 | 32.3166 | 16.3032 | 28.7062 | 28.732 | 17.9149 |
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- | 2.2349 | 8.0 | 376 | 2.0653 | 31.8387 | 15.616 | 28.3254 | 28.4288 | 17.7979 |
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- | 2.1945 | 9.0 | 423 | 2.0473 | 32.388 | 16.4027 | 28.5642 | 28.6096 | 17.6809 |
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- | 2.1658 | 10.0 | 470 | 2.0352 | 33.9489 | 16.999 | 29.8446 | 29.8251 | 17.5426 |
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- | 2.1414 | 11.0 | 517 | 2.0252 | 34.0804 | 17.6999 | 30.1921 | 30.2739 | 17.5106 |
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- | 2.1103 | 12.0 | 564 | 2.0155 | 34.3488 | 17.8273 | 30.2613 | 30.3358 | 17.5957 |
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- | 2.1052 | 13.0 | 611 | 2.0053 | 35.1038 | 18.3494 | 30.6999 | 30.7655 | 17.6064 |
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- | 2.0795 | 14.0 | 658 | 2.0004 | 35.366 | 18.8791 | 31.4931 | 31.5691 | 17.7872 |
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- | 2.0612 | 15.0 | 705 | 1.9951 | 36.1778 | 18.7911 | 31.5974 | 31.6309 | 17.6064 |
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- | 2.0792 | 16.0 | 752 | 1.9886 | 35.0387 | 18.2363 | 31.5279 | 31.5694 | 17.6702 |
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- | 2.0695 | 17.0 | 799 | 1.9868 | 36.1432 | 18.4902 | 31.8314 | 31.7955 | 17.617 |
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- | 2.0593 | 18.0 | 846 | 1.9844 | 35.7847 | 18.3497 | 31.745 | 31.7007 | 17.6809 |
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- | 2.0395 | 19.0 | 893 | 1.9842 | 36.0629 | 18.9649 | 32.098 | 32.0453 | 17.5745 |
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- | 2.0623 | 20.0 | 940 | 1.9833 | 35.3543 | 18.1226 | 31.3958 | 31.414 | 17.6596 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.8786
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+ - Rouge1: 35.5143
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+ - Rouge2: 18.6656
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+ - Rougel: 32.7292
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+ - Rougelsum: 32.6493
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+ - Gen Len: 17.5745
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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+ | 2.9902 | 1.0 | 47 | 2.5327 | 21.3135 | 8.6584 | 19.3983 | 19.6598 | 19.0 |
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+ | 2.7388 | 2.0 | 94 | 2.2970 | 22.3252 | 9.3302 | 21.6035 | 21.61 | 18.3298 |
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+ | 2.5481 | 3.0 | 141 | 2.1579 | 27.0804 | 13.3149 | 25.5412 | 25.5419 | 18.2553 |
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+ | 2.4268 | 4.0 | 188 | 2.0718 | 29.7762 | 14.9601 | 27.5516 | 27.4876 | 17.9149 |
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+ | 2.3651 | 5.0 | 235 | 2.0219 | 31.8162 | 16.0977 | 28.3376 | 28.3442 | 17.8298 |
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+ | 2.2935 | 6.0 | 282 | 1.9786 | 32.4803 | 16.6321 | 29.6387 | 29.5773 | 17.7553 |
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+ | 2.2474 | 7.0 | 329 | 1.9522 | 33.176 | 16.3891 | 29.708 | 29.7527 | 17.6915 |
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+ | 2.2121 | 8.0 | 376 | 1.9300 | 33.3863 | 16.8205 | 30.6451 | 30.5657 | 17.8085 |
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+ | 2.1792 | 9.0 | 423 | 1.9160 | 34.1843 | 17.523 | 30.6954 | 30.6197 | 17.766 |
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+ | 2.149 | 10.0 | 470 | 1.9013 | 35.068 | 17.8979 | 31.835 | 31.8103 | 17.7021 |
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+ | 2.1388 | 11.0 | 517 | 1.8886 | 35.6427 | 18.3297 | 32.2549 | 32.1712 | 17.6489 |
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+ | 2.1184 | 12.0 | 564 | 1.8786 | 35.5143 | 18.6656 | 32.7292 | 32.6493 | 17.5745 |
 
 
 
 
 
 
 
 
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  ### Framework versions