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limaatulya
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my_awesome_billsum_model_66

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 +164 -0
  2. generation_config.json +6 -0
  3. model.safetensors +1 -1
  4. runs/Jun22_09-19-07_0de1f947a67a/events.out.tfevents.1719047948.0de1f947a67a.1794.2 +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google-t5/t5-small
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - rouge
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+ model-index:
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+ - name: my_awesome_billsum_model_66
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # my_awesome_billsum_model_66
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+
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+ This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.0931
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+ - Rouge1: 0.9612
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+ - Rouge2: 0.844
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+ - Rougel: 0.9033
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+ - Rougelsum: 0.9017
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+ - Gen Len: 5.0833
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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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: 100
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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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 | 12 | 0.9723 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 2.0 | 24 | 0.9569 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 3.0 | 36 | 0.9556 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 4.0 | 48 | 0.9349 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 5.0 | 60 | 0.9414 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | No log | 6.0 | 72 | 0.9466 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | No log | 7.0 | 84 | 0.9614 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | No log | 8.0 | 96 | 0.9674 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | No log | 9.0 | 108 | 0.9679 | 0.958 | 0.8387 | 0.9045 | 0.9031 | 5.0625 |
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+ | No log | 10.0 | 120 | 0.9714 | 0.9603 | 0.8504 | 0.9142 | 0.9098 | 5.0417 |
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+ | No log | 11.0 | 132 | 0.9692 | 0.9641 | 0.8748 | 0.924 | 0.9224 | 5.1042 |
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+ | No log | 12.0 | 144 | 0.9700 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | No log | 13.0 | 156 | 0.9649 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 14.0 | 168 | 0.9539 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 15.0 | 180 | 0.9534 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | No log | 16.0 | 192 | 0.9646 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | No log | 17.0 | 204 | 0.9753 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | No log | 18.0 | 216 | 0.9846 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | No log | 19.0 | 228 | 0.9885 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 20.0 | 240 | 0.9898 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | No log | 21.0 | 252 | 0.9944 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 22.0 | 264 | 0.9961 | 0.9641 | 0.8509 | 0.9068 | 0.905 | 5.1042 |
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+ | No log | 23.0 | 276 | 1.0002 | 0.9641 | 0.8509 | 0.9068 | 0.905 | 5.1042 |
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+ | No log | 24.0 | 288 | 1.0003 | 0.9641 | 0.8509 | 0.9068 | 0.905 | 5.1042 |
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+ | No log | 25.0 | 300 | 1.0077 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 26.0 | 312 | 1.0249 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 27.0 | 324 | 1.0351 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 28.0 | 336 | 1.0177 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | No log | 29.0 | 348 | 1.0214 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 30.0 | 360 | 1.0268 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 31.0 | 372 | 1.0304 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 32.0 | 384 | 1.0350 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 33.0 | 396 | 1.0293 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 34.0 | 408 | 1.0266 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 35.0 | 420 | 1.0319 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 36.0 | 432 | 1.0462 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 37.0 | 444 | 1.0478 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 38.0 | 456 | 1.0539 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 39.0 | 468 | 1.0613 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 40.0 | 480 | 1.0575 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | No log | 41.0 | 492 | 1.0462 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 42.0 | 504 | 1.0411 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 43.0 | 516 | 1.0446 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 44.0 | 528 | 1.0429 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 45.0 | 540 | 1.0420 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 46.0 | 552 | 1.0439 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 47.0 | 564 | 1.0413 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 48.0 | 576 | 1.0420 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 49.0 | 588 | 1.0465 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 50.0 | 600 | 1.0519 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 51.0 | 612 | 1.0570 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 52.0 | 624 | 1.0635 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 53.0 | 636 | 1.0589 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | 0.0089 | 54.0 | 648 | 1.0580 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | 0.0089 | 55.0 | 660 | 1.0582 | 0.9641 | 0.864 | 0.9121 | 0.9107 | 5.1042 |
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+ | 0.0089 | 56.0 | 672 | 1.0539 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 57.0 | 684 | 1.0407 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 58.0 | 696 | 1.0421 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 59.0 | 708 | 1.0488 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 60.0 | 720 | 1.0579 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 61.0 | 732 | 1.0644 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 62.0 | 744 | 1.0750 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 63.0 | 756 | 1.0848 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 64.0 | 768 | 1.0877 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 65.0 | 780 | 1.0866 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 66.0 | 792 | 1.0889 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 67.0 | 804 | 1.0881 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 68.0 | 816 | 1.0824 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 69.0 | 828 | 1.0787 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 70.0 | 840 | 1.0779 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 71.0 | 852 | 1.0769 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 72.0 | 864 | 1.0769 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 73.0 | 876 | 1.0759 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 74.0 | 888 | 1.0766 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 75.0 | 900 | 1.0761 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 76.0 | 912 | 1.0849 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 77.0 | 924 | 1.0856 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 78.0 | 936 | 1.0896 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 79.0 | 948 | 1.0952 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 80.0 | 960 | 1.0984 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 81.0 | 972 | 1.0995 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 82.0 | 984 | 1.0983 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.0089 | 83.0 | 996 | 1.0968 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 84.0 | 1008 | 1.0962 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 85.0 | 1020 | 1.0980 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 86.0 | 1032 | 1.0997 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 87.0 | 1044 | 1.0994 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 88.0 | 1056 | 1.0997 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 89.0 | 1068 | 1.0990 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 90.0 | 1080 | 1.0984 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 91.0 | 1092 | 1.0975 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 92.0 | 1104 | 1.0966 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 93.0 | 1116 | 1.0938 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 94.0 | 1128 | 1.0937 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 95.0 | 1140 | 1.0943 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 96.0 | 1152 | 1.0933 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 97.0 | 1164 | 1.0928 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 98.0 | 1176 | 1.0927 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 99.0 | 1188 | 1.0929 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+ | 0.006 | 100.0 | 1200 | 1.0931 | 0.9612 | 0.844 | 0.9033 | 0.9017 | 5.0833 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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+ "eos_token_id": 1,
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+ "pad_token_id": 0,
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+ "transformers_version": "4.41.2"
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