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Lora_LED_sum_challenge

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  1. README.md +25 -25
  2. adapter_config.json +3 -3
  3. adapter_model.safetensors +1 -1
  4. runs/Jul24_11-53-52_tardis/events.out.tfevents.1753350834.tardis.447846.0 +3 -0
  5. runs/Jul24_12-04-52_tardis/events.out.tfevents.1753351494.tardis.450378.0 +3 -0
  6. runs/Jul24_12-05-23_tardis/events.out.tfevents.1753351525.tardis.450578.0 +3 -0
  7. runs/Jul24_12-11-08_tardis/events.out.tfevents.1753351870.tardis.451748.0 +3 -0
  8. training_args.bin +1 -1
README.md CHANGED
@@ -22,21 +22,21 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 4.1136
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- - Rouge1: 0.2528
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- - Rouge2: 0.0697
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- - Rougel: 0.2065
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- - Rougelsum: 0.2056
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- - Gen Len: 21.0
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- - Bleu: 0.0273
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- - Precisions: 0.097
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- - Brevity Penalty: 0.6118
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- - Length Ratio: 0.6705
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- - Translation Length: 810.0
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  - Reference Length: 1208.0
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- - Precision: 0.8734
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- - Recall: 0.8657
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- - F1: 0.8695
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  - Hashcode: roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1)
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  ## Model description
@@ -57,7 +57,7 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 0.001
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- - train_batch_size: 16
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
@@ -69,16 +69,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Precision | Recall | F1 | Hashcode |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:----------:|:---------------:|:------------:|:------------------:|:----------------:|:---------:|:------:|:------:|:---------------------------------------------------------:|
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- | 9.3557 | 1.0 | 7 | 8.2857 | 0.1835 | 0.037 | 0.1475 | 0.1474 | 21.0 | 0.0093 | 0.0545 | 0.6661 | 0.7111 | 859.0 | 1208.0 | 0.8595 | 0.8516 | 0.8554 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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- | 7.5243 | 2.0 | 14 | 6.2064 | 0.2082 | 0.0492 | 0.1646 | 0.1643 | 19.54 | 0.0117 | 0.073 | 0.58 | 0.6474 | 782.0 | 1208.0 | 0.8725 | 0.8561 | 0.8641 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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- | 5.8052 | 3.0 | 21 | 5.2106 | 0.2352 | 0.0577 | 0.1872 | 0.186 | 20.1 | 0.0174 | 0.0764 | 0.6275 | 0.6821 | 824.0 | 1208.0 | 0.875 | 0.86 | 0.8674 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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- | 5.2043 | 4.0 | 28 | 4.6310 | 0.2571 | 0.0795 | 0.2055 | 0.2052 | 20.38 | 0.0199 | 0.0913 | 0.5914 | 0.6556 | 792.0 | 1208.0 | 0.8797 | 0.8658 | 0.8726 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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- | 4.55 | 5.0 | 35 | 4.3489 | 0.2527 | 0.0755 | 0.2017 | 0.201 | 20.3 | 0.0279 | 0.0973 | 0.5754 | 0.644 | 778.0 | 1208.0 | 0.8804 | 0.8671 | 0.8736 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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- | 4.3215 | 6.0 | 42 | 4.2134 | 0.2517 | 0.0742 | 0.2041 | 0.203 | 20.9 | 0.0238 | 0.0888 | 0.6107 | 0.6697 | 809.0 | 1208.0 | 0.8749 | 0.8656 | 0.8702 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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- | 4.168 | 7.0 | 49 | 4.1657 | 0.2409 | 0.0632 | 0.1934 | 0.194 | 20.82 | 0.0244 | 0.0901 | 0.5857 | 0.6515 | 787.0 | 1208.0 | 0.8728 | 0.8645 | 0.8686 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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- | 4.0873 | 8.0 | 56 | 4.1293 | 0.2382 | 0.0584 | 0.1928 | 0.1924 | 20.96 | 0.0215 | 0.0849 | 0.5971 | 0.6598 | 797.0 | 1208.0 | 0.8732 | 0.8644 | 0.8687 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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- | 3.9975 | 9.0 | 63 | 4.1130 | 0.2466 | 0.0657 | 0.1984 | 0.1977 | 20.98 | 0.0253 | 0.091 | 0.6084 | 0.668 | 807.0 | 1208.0 | 0.8719 | 0.8644 | 0.8681 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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- | 3.9602 | 10.0 | 70 | 4.1136 | 0.2528 | 0.0697 | 0.2065 | 0.2056 | 21.0 | 0.0273 | 0.097 | 0.6118 | 0.6705 | 810.0 | 1208.0 | 0.8734 | 0.8657 | 0.8695 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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  ### Framework versions
 
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23
  This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on an unknown dataset.
24
  It achieves the following results on the evaluation set:
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+ - Loss: 4.1201
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+ - Rouge1: 0.2826
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+ - Rouge2: 0.1016
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+ - Rougel: 0.2235
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+ - Rougelsum: 0.2227
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+ - Gen Len: 27.48
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+ - Bleu: 0.0515
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+ - Precisions: 0.1044
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+ - Brevity Penalty: 0.8659
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+ - Length Ratio: 0.8742
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+ - Translation Length: 1056.0
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  - Reference Length: 1208.0
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+ - Precision: 0.8808
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+ - Recall: 0.8739
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+ - F1: 0.8773
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  - Hashcode: roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1)
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  ## Model description
 
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  The following hyperparameters were used during training:
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  - learning_rate: 0.001
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+ - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Precision | Recall | F1 | Hashcode |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:----------:|:---------------:|:------------:|:------------------:|:----------------:|:---------:|:------:|:------:|:---------------------------------------------------------:|
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+ | 8.708 | 1.0 | 13 | 6.7992 | 0.2058 | 0.0456 | 0.1594 | 0.159 | 31.68 | 0.0216 | 0.0515 | 1.0 | 1.0737 | 1297.0 | 1208.0 | 0.8535 | 0.8564 | 0.8549 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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+ | 5.8473 | 2.0 | 26 | 4.8979 | 0.2553 | 0.0817 | 0.1969 | 0.1972 | 27.54 | 0.035 | 0.0853 | 0.8901 | 0.8957 | 1082.0 | 1208.0 | 0.8761 | 0.8691 | 0.8725 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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+ | 4.6072 | 3.0 | 39 | 4.2460 | 0.269 | 0.0781 | 0.2078 | 0.2084 | 28.32 | 0.0414 | 0.0898 | 0.865 | 0.8733 | 1055.0 | 1208.0 | 0.8742 | 0.8722 | 0.8731 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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+ | 4.2016 | 4.0 | 52 | 4.1384 | 0.2709 | 0.0894 | 0.2139 | 0.2134 | 27.4 | 0.0495 | 0.0998 | 0.8753 | 0.8825 | 1066.0 | 1208.0 | 0.8792 | 0.8721 | 0.8756 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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+ | 4.0062 | 5.0 | 65 | 4.0907 | 0.2755 | 0.0825 | 0.2128 | 0.2125 | 28.64 | 0.0437 | 0.0921 | 0.901 | 0.9056 | 1094.0 | 1208.0 | 0.8733 | 0.8725 | 0.8729 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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+ | 3.892 | 6.0 | 78 | 4.0992 | 0.2806 | 0.0934 | 0.2199 | 0.2191 | 28.22 | 0.0388 | 0.0952 | 0.891 | 0.8965 | 1083.0 | 1208.0 | 0.8797 | 0.8754 | 0.8775 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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+ | 3.8119 | 7.0 | 91 | 4.0950 | 0.2985 | 0.0916 | 0.2268 | 0.2264 | 28.16 | 0.0284 | 0.0947 | 0.891 | 0.8965 | 1083.0 | 1208.0 | 0.8812 | 0.8763 | 0.8787 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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+ | 3.7427 | 8.0 | 104 | 4.1031 | 0.2942 | 0.1025 | 0.2356 | 0.2344 | 27.2 | 0.0526 | 0.1111 | 0.8394 | 0.851 | 1028.0 | 1208.0 | 0.8819 | 0.8758 | 0.8788 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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+ | 3.6902 | 9.0 | 117 | 4.1120 | 0.2981 | 0.1028 | 0.2323 | 0.232 | 28.08 | 0.0487 | 0.1036 | 0.8836 | 0.8899 | 1075.0 | 1208.0 | 0.8782 | 0.8755 | 0.8768 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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+ | 3.6548 | 10.0 | 130 | 4.1201 | 0.2826 | 0.1016 | 0.2235 | 0.2227 | 27.48 | 0.0515 | 0.1044 | 0.8659 | 0.8742 | 1056.0 | 1208.0 | 0.8808 | 0.8739 | 0.8773 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.53.1) |
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  ### Framework versions
adapter_config.json CHANGED
@@ -24,10 +24,10 @@
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