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t5-summarization-one-shot-better-prompt

Text2Text Generation
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t5
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text-generation-inference
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End of training

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  1. README.md +25 -25
  2. model.safetensors +1 -1
  3. runs/Jan13_20-45-16_3eda5e562e43/events.out.tfevents.1705178717.3eda5e562e43.2889.0 +2 -2
  4. runs/Jan13_20-45-16_3eda5e562e43/events.out.tfevents.1705181178.3eda5e562e43.2889.1 +3 -0
README.md CHANGED
@@ -17,11 +17,11 @@ 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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.2414
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- - Rouge: {'rouge1': 38.3588, 'rouge2': 17.983, 'rougeL': 20.1917, 'rougeLsum': 20.1917}
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- - Bert Score: 0.8806
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- - Bleurt 20: -0.7794
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- - Gen Len: 13.44
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  ## Model description
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@@ -52,26 +52,26 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Rouge | Bert Score | Bleurt 20 | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------:|:----------:|:---------:|:-------:|
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- | 2.7663 | 1.0 | 186 | 2.4069 | {'rouge1': 43.4548, 'rouge2': 17.3297, 'rougeL': 18.9728, 'rougeLsum': 18.9728} | 0.874 | -0.8387 | 14.275 |
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- | 2.4668 | 2.0 | 372 | 2.3255 | {'rouge1': 42.9892, 'rouge2': 18.518, 'rougeL': 19.7631, 'rougeLsum': 19.7631} | 0.8763 | -0.8091 | 13.965 |
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- | 2.2692 | 3.0 | 558 | 2.2633 | {'rouge1': 36.8257, 'rouge2': 16.1751, 'rougeL': 17.9916, 'rougeLsum': 17.9916} | 0.8744 | -0.8312 | 12.955 |
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- | 2.2018 | 4.0 | 744 | 2.2481 | {'rouge1': 40.4112, 'rouge2': 18.1938, 'rougeL': 20.0606, 'rougeLsum': 20.0606} | 0.877 | -0.7846 | 14.04 |
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- | 2.1736 | 5.0 | 930 | 2.2243 | {'rouge1': 39.2656, 'rouge2': 18.4718, 'rougeL': 19.5926, 'rougeLsum': 19.5926} | 0.8786 | -0.7865 | 13.31 |
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- | 2.0189 | 6.0 | 1116 | 2.2220 | {'rouge1': 38.1992, 'rouge2': 18.0936, 'rougeL': 18.6278, 'rougeLsum': 18.6278} | 0.877 | -0.8295 | 13.3 |
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- | 1.9425 | 7.0 | 1302 | 2.2103 | {'rouge1': 38.9165, 'rouge2': 18.0013, 'rougeL': 19.2571, 'rougeLsum': 19.2571} | 0.8779 | -0.7923 | 13.445 |
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- | 1.9192 | 8.0 | 1488 | 2.2060 | {'rouge1': 37.6615, 'rouge2': 18.1423, 'rougeL': 19.3882, 'rougeLsum': 19.3882} | 0.8773 | -0.814 | 13.135 |
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- | 1.8502 | 9.0 | 1674 | 2.1948 | {'rouge1': 37.595, 'rouge2': 17.5944, 'rougeL': 19.4897, 'rougeLsum': 19.4897} | 0.8809 | -0.7914 | 13.15 |
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- | 1.8201 | 10.0 | 1860 | 2.1995 | {'rouge1': 38.7935, 'rouge2': 19.2667, 'rougeL': 20.5059, 'rougeLsum': 20.5059} | 0.8809 | -0.7765 | 13.36 |
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- | 1.7472 | 11.0 | 2046 | 2.2036 | {'rouge1': 37.4728, 'rouge2': 17.5974, 'rougeL': 19.5534, 'rougeLsum': 19.5534} | 0.8797 | -0.7943 | 13.245 |
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- | 1.772 | 12.0 | 2232 | 2.2050 | {'rouge1': 37.6136, 'rouge2': 17.442, 'rougeL': 20.122, 'rougeLsum': 20.122} | 0.881 | -0.7765 | 13.35 |
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- | 1.7273 | 13.0 | 2418 | 2.2153 | {'rouge1': 37.2238, 'rouge2': 16.6237, 'rougeL': 19.4117, 'rougeLsum': 19.4117} | 0.8789 | -0.7929 | 13.325 |
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- | 1.6854 | 14.0 | 2604 | 2.2243 | {'rouge1': 38.1249, 'rouge2': 18.0241, 'rougeL': 20.485, 'rougeLsum': 20.485} | 0.8822 | -0.778 | 13.315 |
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- | 1.6598 | 15.0 | 2790 | 2.2299 | {'rouge1': 37.3743, 'rouge2': 17.3192, 'rougeL': 19.9239, 'rougeLsum': 19.9239} | 0.8795 | -0.7805 | 13.275 |
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- | 1.63 | 16.0 | 2976 | 2.2286 | {'rouge1': 38.6731, 'rouge2': 18.2088, 'rougeL': 20.2535, 'rougeLsum': 20.2535} | 0.8801 | -0.7882 | 13.415 |
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- | 1.6654 | 17.0 | 3162 | 2.2355 | {'rouge1': 38.0295, 'rouge2': 17.6256, 'rougeL': 19.9215, 'rougeLsum': 19.9215} | 0.8799 | -0.7894 | 13.34 |
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- | 1.6443 | 18.0 | 3348 | 2.2404 | {'rouge1': 38.3122, 'rouge2': 17.5836, 'rougeL': 19.8706, 'rougeLsum': 19.8706} | 0.8801 | -0.7799 | 13.45 |
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- | 1.6083 | 19.0 | 3534 | 2.2399 | {'rouge1': 38.1749, 'rouge2': 17.4993, 'rougeL': 20.0054, 'rougeLsum': 20.0054} | 0.8801 | -0.7772 | 13.435 |
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- | 1.5953 | 20.0 | 3720 | 2.2414 | {'rouge1': 38.3588, 'rouge2': 17.983, 'rougeL': 20.1917, 'rougeLsum': 20.1917} | 0.8806 | -0.7794 | 13.44 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.2431
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+ - Rouge: {'rouge1': 39.1164, 'rouge2': 19.0784, 'rougeL': 20.2856, 'rougeLsum': 20.2856}
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+ - Bert Score: 0.8802
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+ - Bleurt 20: -0.7688
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+ - Gen Len: 13.545
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Rouge | Bert Score | Bleurt 20 | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------:|:----------:|:---------:|:-------:|
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+ | 2.6921 | 1.0 | 172 | 2.4379 | {'rouge1': 43.7984, 'rouge2': 17.2952, 'rougeL': 18.5604, 'rougeLsum': 18.5604} | 0.869 | -0.8739 | 14.84 |
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+ | 2.5119 | 2.0 | 344 | 2.3282 | {'rouge1': 41.5219, 'rouge2': 17.4612, 'rougeL': 19.5103, 'rougeLsum': 19.5103} | 0.8749 | -0.8329 | 13.7 |
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+ | 2.3033 | 3.0 | 516 | 2.2821 | {'rouge1': 41.0636, 'rouge2': 18.2347, 'rougeL': 19.8704, 'rougeLsum': 19.8704} | 0.878 | -0.8268 | 13.75 |
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+ | 2.2139 | 4.0 | 688 | 2.2404 | {'rouge1': 39.9679, 'rouge2': 18.8795, 'rougeL': 19.7032, 'rougeLsum': 19.7032} | 0.8796 | -0.8035 | 13.305 |
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+ | 2.0835 | 5.0 | 860 | 2.2446 | {'rouge1': 41.8958, 'rouge2': 18.439, 'rougeL': 19.2982, 'rougeLsum': 19.2982} | 0.877 | -0.7963 | 14.34 |
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+ | 2.0379 | 6.0 | 1032 | 2.2233 | {'rouge1': 40.9703, 'rouge2': 19.7574, 'rougeL': 19.9387, 'rougeLsum': 19.9387} | 0.8793 | -0.7805 | 13.625 |
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+ | 1.959 | 7.0 | 1204 | 2.2073 | {'rouge1': 39.2194, 'rouge2': 18.9553, 'rougeL': 19.7847, 'rougeLsum': 19.7847} | 0.8787 | -0.8045 | 13.365 |
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+ | 1.9177 | 8.0 | 1376 | 2.2146 | {'rouge1': 40.8391, 'rouge2': 19.5219, 'rougeL': 20.2602, 'rougeLsum': 20.2602} | 0.8781 | -0.7974 | 13.865 |
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+ | 1.8749 | 9.0 | 1548 | 2.2071 | {'rouge1': 40.9497, 'rouge2': 19.9867, 'rougeL': 20.5682, 'rougeLsum': 20.5682} | 0.8808 | -0.7812 | 13.68 |
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+ | 1.8112 | 10.0 | 1720 | 2.2045 | {'rouge1': 36.465, 'rouge2': 16.4287, 'rougeL': 19.1978, 'rougeLsum': 19.1978} | 0.8772 | -0.8384 | 13.295 |
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+ | 1.7475 | 11.0 | 1892 | 2.2210 | {'rouge1': 39.4889, 'rouge2': 19.1309, 'rougeL': 19.879, 'rougeLsum': 19.879} | 0.8785 | -0.8074 | 13.585 |
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+ | 1.7384 | 12.0 | 2064 | 2.2269 | {'rouge1': 38.2904, 'rouge2': 18.2873, 'rougeL': 19.4418, 'rougeLsum': 19.4418} | 0.8789 | -0.7984 | 13.42 |
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+ | 1.6849 | 13.0 | 2236 | 2.2261 | {'rouge1': 37.6283, 'rouge2': 17.6979, 'rougeL': 19.584, 'rougeLsum': 19.584} | 0.878 | -0.7885 | 13.445 |
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+ | 1.6531 | 14.0 | 2408 | 2.2186 | {'rouge1': 38.7975, 'rouge2': 19.0939, 'rougeL': 20.7873, 'rougeLsum': 20.7873} | 0.8806 | -0.783 | 13.445 |
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+ | 1.663 | 15.0 | 2580 | 2.2245 | {'rouge1': 38.9159, 'rouge2': 19.153, 'rougeL': 20.5232, 'rougeLsum': 20.5232} | 0.8811 | -0.7514 | 13.59 |
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+ | 1.6036 | 16.0 | 2752 | 2.2430 | {'rouge1': 37.6184, 'rouge2': 17.6773, 'rougeL': 19.2693, 'rougeLsum': 19.2693} | 0.8771 | -0.7992 | 13.6 |
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+ | 1.6333 | 17.0 | 2924 | 2.2418 | {'rouge1': 38.1301, 'rouge2': 18.4061, 'rougeL': 20.1355, 'rougeLsum': 20.1355} | 0.879 | -0.7845 | 13.49 |
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+ | 1.6322 | 18.0 | 3096 | 2.2421 | {'rouge1': 38.0746, 'rouge2': 18.2039, 'rougeL': 19.7404, 'rougeLsum': 19.7404} | 0.8789 | -0.7892 | 13.41 |
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+ | 1.5982 | 19.0 | 3268 | 2.2411 | {'rouge1': 39.1375, 'rouge2': 19.1696, 'rougeL': 20.2695, 'rougeLsum': 20.2695} | 0.8802 | -0.7713 | 13.465 |
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+ | 1.593 | 20.0 | 3440 | 2.2431 | {'rouge1': 39.1164, 'rouge2': 19.0784, 'rougeL': 20.2856, 'rougeLsum': 20.2856} | 0.8802 | -0.7688 | 13.545 |
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  ### Framework versions
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