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srijan2024
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my_awesome_billsum_model

Text2Text Generation
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Safetensors
t5
Generated from Trainer
text-generation-inference
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End of training

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  1. README.md +27 -17
  2. model.safetensors +1 -1
  3. runs/Dec03_14-29-37_a485489377d6/events.out.tfevents.1701613783.a485489377d6.265.0 +2 -2
README.md CHANGED
@@ -17,12 +17,12 @@ 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: 0.0580
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- - Rouge1: 0.7481
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- - Rouge2: 0.6473
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- - Rougel: 0.7481
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- - Rougelsum: 0.7481
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- - Gen Len: 11.6741
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  ## Model description
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@@ -47,23 +47,33 @@ The following hyperparameters were used during training:
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  - seed: 42
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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: 10
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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 | 56 | 4.8661 | 0.0 | 0.0 | 0.0 | 0.0 | 16.3036 |
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- | No log | 2.0 | 112 | 2.3787 | 0.0 | 0.0 | 0.0 | 0.0 | 13.4375 |
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- | No log | 3.0 | 168 | 0.6260 | 0.0 | 0.0 | 0.0 | 0.0 | 11.7589 |
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- | No log | 4.0 | 224 | 0.2127 | 0.452 | 0.3564 | 0.4496 | 0.4523 | 11.1295 |
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- | No log | 5.0 | 280 | 0.0894 | 0.7226 | 0.6116 | 0.7226 | 0.7226 | 11.6116 |
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- | No log | 6.0 | 336 | 0.0667 | 0.7274 | 0.6161 | 0.7258 | 0.7258 | 11.6518 |
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- | No log | 7.0 | 392 | 0.0616 | 0.7321 | 0.6205 | 0.729 | 0.729 | 11.6652 |
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- | No log | 8.0 | 448 | 0.0591 | 0.7449 | 0.6429 | 0.7449 | 0.7449 | 11.6696 |
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- | 1.7476 | 9.0 | 504 | 0.0583 | 0.7481 | 0.6473 | 0.7481 | 0.7481 | 11.6741 |
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- | 1.7476 | 10.0 | 560 | 0.0580 | 0.7481 | 0.6473 | 0.7481 | 0.7481 | 11.6741 |
 
 
 
 
 
 
 
 
 
 
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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: 0.0467
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+ - Rouge1: 0.7832
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+ - Rouge2: 0.692
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+ - Rougel: 0.781
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+ - Rougelsum: 0.7805
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+ - Gen Len: 11.6071
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  ## Model description
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  - seed: 42
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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 | 56 | 4.9294 | 0.0 | 0.0 | 0.0 | 0.0 | 16.558 |
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+ | No log | 2.0 | 112 | 2.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 13.5357 |
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+ | No log | 3.0 | 168 | 0.4763 | 0.0045 | 0.0045 | 0.0045 | 0.0045 | 10.6518 |
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+ | No log | 4.0 | 224 | 0.1138 | 0.7232 | 0.6205 | 0.7245 | 0.7236 | 11.5893 |
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+ | No log | 5.0 | 280 | 0.0654 | 0.7417 | 0.6339 | 0.7417 | 0.7402 | 11.6607 |
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+ | No log | 6.0 | 336 | 0.0587 | 0.7321 | 0.6205 | 0.7321 | 0.7309 | 11.5938 |
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+ | No log | 7.0 | 392 | 0.0552 | 0.7496 | 0.6473 | 0.7491 | 0.7491 | 11.625 |
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+ | No log | 8.0 | 448 | 0.0533 | 0.7714 | 0.6786 | 0.7714 | 0.7709 | 11.6562 |
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+ | 1.6431 | 9.0 | 504 | 0.0518 | 0.781 | 0.692 | 0.7832 | 0.7805 | 11.6161 |
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+ | 1.6431 | 10.0 | 560 | 0.0505 | 0.764 | 0.6652 | 0.7632 | 0.7614 | 11.6607 |
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+ | 1.6431 | 11.0 | 616 | 0.0494 | 0.7778 | 0.6875 | 0.78 | 0.7773 | 11.6116 |
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+ | 1.6431 | 12.0 | 672 | 0.0488 | 0.7778 | 0.6875 | 0.78 | 0.7773 | 11.6116 |
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+ | 1.6431 | 13.0 | 728 | 0.0483 | 0.781 | 0.692 | 0.7815 | 0.7805 | 11.6161 |
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+ | 1.6431 | 14.0 | 784 | 0.0479 | 0.781 | 0.692 | 0.7815 | 0.7805 | 11.6071 |
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+ | 1.6431 | 15.0 | 840 | 0.0475 | 0.7852 | 0.6964 | 0.7839 | 0.7842 | 11.6205 |
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+ | 1.6431 | 16.0 | 896 | 0.0471 | 0.7812 | 0.692 | 0.781 | 0.7805 | 11.5982 |
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+ | 1.6431 | 17.0 | 952 | 0.0469 | 0.7884 | 0.7009 | 0.7879 | 0.7869 | 11.625 |
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+ | 0.062 | 18.0 | 1008 | 0.0468 | 0.7832 | 0.692 | 0.781 | 0.7805 | 11.6071 |
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+ | 0.062 | 19.0 | 1064 | 0.0467 | 0.7864 | 0.6964 | 0.7839 | 0.7837 | 11.6027 |
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+ | 0.062 | 20.0 | 1120 | 0.0467 | 0.7832 | 0.692 | 0.781 | 0.7805 | 11.6071 |
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  ### Framework versions
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