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ParastooC
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t5_small_SA_abbr_replaced

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t5
text2text-generation
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text-generation-inference
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update model card README.md

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@@ -16,15 +16,15 @@ 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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5644
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- - Rouge1: 0.232
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- - Rouge2: 0.1064
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- - Rougel: 0.2202
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- - Rougelsum: 0.2199
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- - Gen Len: 12.9115
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- - Bert Score F1: 0.8426
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- - Bert Score Precision: 0.8571
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- - Bert Score Recall: 0.8301
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  ## Model description
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@@ -55,18 +55,18 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bert Score F1 | Bert Score Precision | Bert Score Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:-------------:|:--------------------:|:-----------------:|
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- | 0.614 | 0.95 | 500 | 0.6288 | 0.1538 | 0.0705 | 0.1474 | 0.1478 | 11.292 | 0.6771 | 0.6893 | 0.6662 |
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- | 0.6239 | 1.9 | 1000 | 0.6071 | 0.21 | 0.0908 | 0.1999 | 0.1999 | 14.4071 | 0.8204 | 0.8323 | 0.8102 |
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- | 0.6028 | 2.85 | 1500 | 0.5950 | 0.2178 | 0.0956 | 0.2058 | 0.2055 | 13.3363 | 0.8376 | 0.8518 | 0.8251 |
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- | 0.557 | 3.8 | 2000 | 0.5869 | 0.2206 | 0.095 | 0.215 | 0.2144 | 13.3717 | 0.84 | 0.8544 | 0.8273 |
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- | 0.566 | 4.74 | 2500 | 0.5842 | 0.2167 | 0.1018 | 0.207 | 0.2071 | 12.6018 | 0.8393 | 0.8556 | 0.825 |
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- | 0.5492 | 5.69 | 3000 | 0.5778 | 0.2181 | 0.1039 | 0.2097 | 0.2096 | 13.0 | 0.8407 | 0.8557 | 0.8275 |
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- | 0.5192 | 6.64 | 3500 | 0.5728 | 0.214 | 0.0983 | 0.2078 | 0.2076 | 12.7788 | 0.8416 | 0.8564 | 0.8285 |
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- | 0.5165 | 7.59 | 4000 | 0.5706 | 0.2252 | 0.1031 | 0.2167 | 0.217 | 11.8496 | 0.8427 | 0.8595 | 0.8279 |
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- | 0.5408 | 8.54 | 4500 | 0.5672 | 0.2337 | 0.1098 | 0.222 | 0.2226 | 13.0708 | 0.8442 | 0.8593 | 0.8308 |
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- | 0.5128 | 9.49 | 5000 | 0.5650 | 0.2326 | 0.1066 | 0.2233 | 0.2231 | 12.6637 | 0.8443 | 0.8593 | 0.8311 |
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- | 0.5202 | 10.44 | 5500 | 0.5649 | 0.2344 | 0.1051 | 0.222 | 0.2216 | 12.8761 | 0.8423 | 0.8571 | 0.8294 |
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- | 0.4839 | 11.39 | 6000 | 0.5644 | 0.232 | 0.1064 | 0.2202 | 0.2199 | 12.9115 | 0.8426 | 0.8571 | 0.8301 |
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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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5633
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+ - Rouge1: 0.2325
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+ - Rouge2: 0.1112
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+ - Rougel: 0.2228
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+ - Rougelsum: 0.2228
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+ - Gen Len: 13.115
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+ - Bert Score F1: 0.8407
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+ - Bert Score Precision: 0.8551
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+ - Bert Score Recall: 0.8282
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bert Score F1 | Bert Score Precision | Bert Score Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:-------------:|:--------------------:|:-----------------:|
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+ | 0.6839 | 0.95 | 500 | 0.6228 | 0.1495 | 0.0654 | 0.1433 | 0.1421 | 10.3894 | 0.6689 | 0.6835 | 0.656 |
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+ | 0.5918 | 1.9 | 1000 | 0.6084 | 0.1965 | 0.0775 | 0.1893 | 0.1904 | 13.6991 | 0.8138 | 0.827 | 0.8024 |
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+ | 0.5917 | 2.85 | 1500 | 0.5943 | 0.2075 | 0.0804 | 0.1975 | 0.1982 | 12.5841 | 0.8293 | 0.8452 | 0.8153 |
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+ | 0.5524 | 3.8 | 2000 | 0.5859 | 0.2104 | 0.0856 | 0.1994 | 0.2 | 13.4602 | 0.8373 | 0.8523 | 0.8242 |
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+ | 0.5635 | 4.74 | 2500 | 0.5830 | 0.2069 | 0.0905 | 0.1974 | 0.1984 | 12.1327 | 0.8379 | 0.8549 | 0.8231 |
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+ | 0.5455 | 5.69 | 3000 | 0.5756 | 0.2113 | 0.0963 | 0.2038 | 0.2032 | 12.6903 | 0.8398 | 0.8545 | 0.8269 |
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+ | 0.5156 | 6.64 | 3500 | 0.5710 | 0.2147 | 0.0993 | 0.2093 | 0.2096 | 13.115 | 0.8393 | 0.8527 | 0.8278 |
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+ | 0.5134 | 7.59 | 4000 | 0.5683 | 0.2252 | 0.105 | 0.216 | 0.2168 | 12.4513 | 0.8418 | 0.8572 | 0.8285 |
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+ | 0.5381 | 8.54 | 4500 | 0.5661 | 0.2228 | 0.1051 | 0.215 | 0.2151 | 13.4602 | 0.8401 | 0.8534 | 0.8287 |
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+ | 0.5092 | 9.49 | 5000 | 0.5643 | 0.2186 | 0.1027 | 0.2135 | 0.2132 | 13.1593 | 0.8385 | 0.8524 | 0.8265 |
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+ | 0.5181 | 10.44 | 5500 | 0.5643 | 0.2299 | 0.1087 | 0.2196 | 0.2202 | 13.0 | 0.8398 | 0.8543 | 0.8273 |
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+ | 0.4824 | 11.39 | 6000 | 0.5633 | 0.2325 | 0.1112 | 0.2228 | 0.2228 | 13.115 | 0.8407 | 0.8551 | 0.8282 |
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  ### Framework versions