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limaatulya
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my_awesome_billsum_model_16

Text2Text Generation
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t5
Generated from Trainer
text-generation-inference
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  1. README.md +164 -0
  2. generation_config.json +6 -0
  3. model.safetensors +1 -1
  4. runs/Jun19_10-55-35_1bb842139753/events.out.tfevents.1718794536.1bb842139753.1759.0 +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_16
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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_16
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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: 0.4760
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+ - Rouge1: 0.9701
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+ - Rouge2: 0.8569
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+ - Rougel: 0.9116
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+ - Rougelsum: 0.9111
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+ - Gen Len: 5.125
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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 | 2.2330 | 0.4088 | 0.2664 | 0.3762 | 0.3787 | 16.9792 |
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+ | No log | 2.0 | 24 | 1.6503 | 0.4535 | 0.3049 | 0.4144 | 0.4162 | 15.625 |
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+ | No log | 3.0 | 36 | 1.0795 | 0.6368 | 0.4863 | 0.5795 | 0.5845 | 11.75 |
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+ | No log | 4.0 | 48 | 0.8013 | 0.9291 | 0.767 | 0.8384 | 0.8384 | 5.3542 |
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+ | No log | 5.0 | 60 | 0.7414 | 0.9521 | 0.8032 | 0.8652 | 0.8677 | 4.875 |
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+ | No log | 6.0 | 72 | 0.6977 | 0.9521 | 0.8032 | 0.8652 | 0.8677 | 4.875 |
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+ | No log | 7.0 | 84 | 0.6469 | 0.9573 | 0.8118 | 0.864 | 0.8652 | 4.9583 |
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+ | No log | 8.0 | 96 | 0.6180 | 0.9611 | 0.8243 | 0.873 | 0.8736 | 4.9792 |
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+ | No log | 9.0 | 108 | 0.5926 | 0.9611 | 0.8243 | 0.873 | 0.8736 | 4.9792 |
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+ | No log | 10.0 | 120 | 0.5686 | 0.9673 | 0.8507 | 0.8925 | 0.892 | 5.0208 |
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+ | No log | 11.0 | 132 | 0.5447 | 0.9625 | 0.8389 | 0.8897 | 0.8888 | 5.0625 |
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+ | No log | 12.0 | 144 | 0.5307 | 0.9673 | 0.8507 | 0.8925 | 0.892 | 5.0208 |
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+ | No log | 13.0 | 156 | 0.5118 | 0.9625 | 0.8389 | 0.8897 | 0.8888 | 5.0625 |
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+ | No log | 14.0 | 168 | 0.5064 | 0.9625 | 0.8389 | 0.8897 | 0.8888 | 5.0625 |
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+ | No log | 15.0 | 180 | 0.4990 | 0.9625 | 0.8389 | 0.8897 | 0.8888 | 5.0625 |
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+ | No log | 16.0 | 192 | 0.4914 | 0.9625 | 0.8389 | 0.8897 | 0.8888 | 5.0625 |
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+ | No log | 17.0 | 204 | 0.4881 | 0.9625 | 0.8389 | 0.8897 | 0.8888 | 5.0625 |
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+ | No log | 18.0 | 216 | 0.4832 | 0.9647 | 0.8398 | 0.8861 | 0.8845 | 5.0417 |
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+ | No log | 19.0 | 228 | 0.4751 | 0.9647 | 0.8398 | 0.8861 | 0.8845 | 5.0417 |
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+ | No log | 20.0 | 240 | 0.4656 | 0.9675 | 0.8398 | 0.8929 | 0.8925 | 5.0625 |
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+ | No log | 21.0 | 252 | 0.4584 | 0.9652 | 0.8389 | 0.8965 | 0.8965 | 5.0833 |
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+ | No log | 22.0 | 264 | 0.4586 | 0.9652 | 0.8389 | 0.8965 | 0.8965 | 5.0833 |
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+ | No log | 23.0 | 276 | 0.4565 | 0.9676 | 0.8493 | 0.903 | 0.9039 | 5.0625 |
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+ | No log | 24.0 | 288 | 0.4433 | 0.9634 | 0.8285 | 0.8913 | 0.8914 | 5.0833 |
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+ | No log | 25.0 | 300 | 0.4346 | 0.9634 | 0.8285 | 0.8913 | 0.8914 | 5.0833 |
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+ | No log | 26.0 | 312 | 0.4364 | 0.9634 | 0.8285 | 0.8913 | 0.8914 | 5.0833 |
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+ | No log | 27.0 | 324 | 0.4372 | 0.9634 | 0.8285 | 0.8913 | 0.8914 | 5.0833 |
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+ | No log | 28.0 | 336 | 0.4328 | 0.9634 | 0.8201 | 0.8913 | 0.8914 | 5.0833 |
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+ | No log | 29.0 | 348 | 0.4329 | 0.9615 | 0.8098 | 0.8842 | 0.8837 | 5.1042 |
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+ | No log | 30.0 | 360 | 0.4285 | 0.959 | 0.8098 | 0.8827 | 0.8827 | 5.125 |
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+ | No log | 31.0 | 372 | 0.4260 | 0.959 | 0.8098 | 0.8827 | 0.8827 | 5.125 |
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+ | No log | 32.0 | 384 | 0.4217 | 0.959 | 0.8098 | 0.8827 | 0.8827 | 5.125 |
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+ | No log | 33.0 | 396 | 0.4189 | 0.9625 | 0.8085 | 0.8812 | 0.881 | 5.1458 |
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+ | No log | 34.0 | 408 | 0.4205 | 0.9653 | 0.8157 | 0.8855 | 0.8852 | 5.1667 |
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+ | No log | 35.0 | 420 | 0.4234 | 0.9679 | 0.8243 | 0.8929 | 0.8924 | 5.1458 |
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+ | No log | 36.0 | 432 | 0.4259 | 0.9644 | 0.8257 | 0.8944 | 0.8938 | 5.125 |
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+ | No log | 37.0 | 444 | 0.4253 | 0.9644 | 0.8257 | 0.8944 | 0.8938 | 5.125 |
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+ | No log | 38.0 | 456 | 0.4275 | 0.9708 | 0.8542 | 0.9077 | 0.9083 | 5.0833 |
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+ | No log | 39.0 | 468 | 0.4312 | 0.9701 | 0.8337 | 0.8942 | 0.8941 | 5.125 |
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+ | No log | 40.0 | 480 | 0.4350 | 0.9701 | 0.8337 | 0.8942 | 0.8941 | 5.125 |
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+ | No log | 41.0 | 492 | 0.4351 | 0.9701 | 0.8337 | 0.8942 | 0.8941 | 5.125 |
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+ | 0.4347 | 42.0 | 504 | 0.4366 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 43.0 | 516 | 0.4396 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 44.0 | 528 | 0.4433 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 45.0 | 540 | 0.4468 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 46.0 | 552 | 0.4406 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 47.0 | 564 | 0.4340 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 48.0 | 576 | 0.4351 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 49.0 | 588 | 0.4380 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 50.0 | 600 | 0.4394 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 51.0 | 612 | 0.4415 | 0.9679 | 0.8382 | 0.9023 | 0.9028 | 5.1458 |
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+ | 0.4347 | 52.0 | 624 | 0.4438 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 53.0 | 636 | 0.4425 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 54.0 | 648 | 0.4445 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 55.0 | 660 | 0.4462 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 56.0 | 672 | 0.4509 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 57.0 | 684 | 0.4515 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 58.0 | 696 | 0.4517 | 0.9679 | 0.8382 | 0.8971 | 0.8976 | 5.1458 |
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+ | 0.4347 | 59.0 | 708 | 0.4539 | 0.9679 | 0.8382 | 0.8971 | 0.8976 | 5.1458 |
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+ | 0.4347 | 60.0 | 720 | 0.4524 | 0.9679 | 0.8382 | 0.8971 | 0.8976 | 5.1458 |
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+ | 0.4347 | 61.0 | 732 | 0.4519 | 0.9679 | 0.8382 | 0.8971 | 0.8976 | 5.1458 |
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+ | 0.4347 | 62.0 | 744 | 0.4535 | 0.9679 | 0.8382 | 0.8971 | 0.8976 | 5.1458 |
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+ | 0.4347 | 63.0 | 756 | 0.4523 | 0.9679 | 0.8382 | 0.8971 | 0.8976 | 5.1458 |
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+ | 0.4347 | 64.0 | 768 | 0.4533 | 0.9679 | 0.8382 | 0.8971 | 0.8976 | 5.1458 |
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+ | 0.4347 | 65.0 | 780 | 0.4579 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 66.0 | 792 | 0.4567 | 0.9679 | 0.8479 | 0.9039 | 0.9047 | 5.1458 |
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+ | 0.4347 | 67.0 | 804 | 0.4576 | 0.9679 | 0.8479 | 0.9039 | 0.9047 | 5.1458 |
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+ | 0.4347 | 68.0 | 816 | 0.4575 | 0.9679 | 0.8479 | 0.9039 | 0.9047 | 5.1458 |
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+ | 0.4347 | 69.0 | 828 | 0.4542 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 70.0 | 840 | 0.4526 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 71.0 | 852 | 0.4550 | 0.9701 | 0.8465 | 0.9037 | 0.9049 | 5.125 |
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+ | 0.4347 | 72.0 | 864 | 0.4612 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 73.0 | 876 | 0.4647 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 74.0 | 888 | 0.4664 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 75.0 | 900 | 0.4677 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 76.0 | 912 | 0.4688 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 77.0 | 924 | 0.4673 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 78.0 | 936 | 0.4667 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 79.0 | 948 | 0.4683 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 80.0 | 960 | 0.4693 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 81.0 | 972 | 0.4700 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 82.0 | 984 | 0.4701 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.4347 | 83.0 | 996 | 0.4723 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 84.0 | 1008 | 0.4751 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 85.0 | 1020 | 0.4775 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 86.0 | 1032 | 0.4799 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 87.0 | 1044 | 0.4802 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 88.0 | 1056 | 0.4792 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 89.0 | 1068 | 0.4783 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 90.0 | 1080 | 0.4765 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 91.0 | 1092 | 0.4753 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 92.0 | 1104 | 0.4754 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 93.0 | 1116 | 0.4757 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 94.0 | 1128 | 0.4761 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 95.0 | 1140 | 0.4765 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 96.0 | 1152 | 0.4763 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 97.0 | 1164 | 0.4761 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 98.0 | 1176 | 0.4761 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 99.0 | 1188 | 0.4762 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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+ | 0.0835 | 100.0 | 1200 | 0.4760 | 0.9701 | 0.8569 | 0.9116 | 0.9111 | 5.125 |
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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
generation_config.json ADDED
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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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