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---
license: apache-2.0
base_model: google/t5-efficient-tiny
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: denoice-finetuned-xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# denoice-finetuned-xsum
This model is a fine-tuned version of [google/t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0221
- Rouge1: 94.8315
- Rouge2: 72.6592
- Rougel: 94.8315
- Rougelsum: 94.8876
- Gen Len: 5.1348
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 500
- eval_batch_size: 500
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 70
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 36 | 0.0285 | 91.8165 | 69.4757 | 91.8727 | 92.0412 | 5.0843 |
| No log | 2.0 | 72 | 0.0284 | 91.6854 | 69.2884 | 91.7603 | 91.9476 | 5.0787 |
| No log | 3.0 | 108 | 0.0280 | 92.3783 | 70.0375 | 92.4625 | 92.5281 | 5.0899 |
| No log | 4.0 | 144 | 0.0277 | 92.3783 | 70.0375 | 92.4625 | 92.5281 | 5.0899 |
| No log | 5.0 | 180 | 0.0276 | 92.9307 | 70.412 | 93.0337 | 93.1086 | 5.0955 |
| No log | 6.0 | 216 | 0.0275 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.1236 |
| No log | 7.0 | 252 | 0.0271 | 92.9963 | 70.412 | 92.9588 | 93.0337 | 5.1067 |
| No log | 8.0 | 288 | 0.0268 | 93.3708 | 70.9738 | 93.427 | 93.3708 | 5.118 |
| No log | 9.0 | 324 | 0.0267 | 93.3708 | 70.9738 | 93.427 | 93.3708 | 5.118 |
| No log | 10.0 | 360 | 0.0264 | 93.3708 | 70.9738 | 93.427 | 93.3708 | 5.118 |
| No log | 11.0 | 396 | 0.0264 | 93.5581 | 70.9738 | 93.5393 | 93.6517 | 5.1124 |
| No log | 12.0 | 432 | 0.0262 | 93.5581 | 70.9738 | 93.5393 | 93.6517 | 5.1124 |
| No log | 13.0 | 468 | 0.0260 | 93.5581 | 70.9738 | 93.5393 | 93.6517 | 5.1124 |
| 0.049 | 14.0 | 504 | 0.0259 | 93.4644 | 70.8801 | 93.4457 | 93.5768 | 5.1236 |
| 0.049 | 15.0 | 540 | 0.0257 | 93.5581 | 70.9738 | 93.5393 | 93.6517 | 5.1124 |
| 0.049 | 16.0 | 576 | 0.0256 | 93.4644 | 70.8801 | 93.4457 | 93.5768 | 5.1236 |
| 0.049 | 17.0 | 612 | 0.0256 | 93.5581 | 70.9738 | 93.5393 | 93.6517 | 5.1124 |
| 0.049 | 18.0 | 648 | 0.0255 | 93.4644 | 70.8801 | 93.4457 | 93.5768 | 5.118 |
| 0.049 | 19.0 | 684 | 0.0252 | 93.5581 | 70.9738 | 93.5393 | 93.6517 | 5.1067 |
| 0.049 | 20.0 | 720 | 0.0250 | 93.5581 | 70.9738 | 93.5393 | 93.6517 | 5.1124 |
| 0.049 | 21.0 | 756 | 0.0248 | 93.5581 | 70.9738 | 93.5393 | 93.6517 | 5.1124 |
| 0.049 | 22.0 | 792 | 0.0245 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.049 | 23.0 | 828 | 0.0246 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.049 | 24.0 | 864 | 0.0246 | 93.5581 | 70.9738 | 93.5393 | 93.6517 | 5.1067 |
| 0.049 | 25.0 | 900 | 0.0245 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.049 | 26.0 | 936 | 0.0243 | 94.1573 | 72.2846 | 94.2697 | 94.2697 | 5.1236 |
| 0.049 | 27.0 | 972 | 0.0243 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.0433 | 28.0 | 1008 | 0.0242 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.1236 |
| 0.0433 | 29.0 | 1044 | 0.0239 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.0433 | 30.0 | 1080 | 0.0237 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.0433 | 31.0 | 1116 | 0.0236 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.1236 |
| 0.0433 | 32.0 | 1152 | 0.0235 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.0433 | 33.0 | 1188 | 0.0234 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.0433 | 34.0 | 1224 | 0.0234 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.0433 | 35.0 | 1260 | 0.0232 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.0433 | 36.0 | 1296 | 0.0232 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.0433 | 37.0 | 1332 | 0.0232 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.0433 | 38.0 | 1368 | 0.0232 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.118 |
| 0.0433 | 39.0 | 1404 | 0.0230 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.1236 |
| 0.0433 | 40.0 | 1440 | 0.0228 | 93.8764 | 71.5356 | 93.9607 | 94.0449 | 5.1236 |
| 0.0433 | 41.0 | 1476 | 0.0228 | 94.4944 | 72.0974 | 94.4944 | 94.6067 | 5.1348 |
| 0.0399 | 42.0 | 1512 | 0.0228 | 94.4944 | 72.0974 | 94.4944 | 94.6067 | 5.1348 |
| 0.0399 | 43.0 | 1548 | 0.0228 | 94.4944 | 72.0974 | 94.4944 | 94.6067 | 5.1348 |
| 0.0399 | 44.0 | 1584 | 0.0228 | 94.4944 | 72.0974 | 94.4944 | 94.6067 | 5.1348 |
| 0.0399 | 45.0 | 1620 | 0.0228 | 94.4944 | 72.0974 | 94.4944 | 94.6067 | 5.1292 |
| 0.0399 | 46.0 | 1656 | 0.0228 | 94.4944 | 72.0974 | 94.4944 | 94.6067 | 5.1292 |
| 0.0399 | 47.0 | 1692 | 0.0228 | 94.4944 | 72.0974 | 94.4944 | 94.6067 | 5.1292 |
| 0.0399 | 48.0 | 1728 | 0.0227 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0399 | 49.0 | 1764 | 0.0226 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0399 | 50.0 | 1800 | 0.0225 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0399 | 51.0 | 1836 | 0.0224 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0399 | 52.0 | 1872 | 0.0225 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0399 | 53.0 | 1908 | 0.0224 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0399 | 54.0 | 1944 | 0.0224 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0399 | 55.0 | 1980 | 0.0224 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 56.0 | 2016 | 0.0223 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 57.0 | 2052 | 0.0222 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 58.0 | 2088 | 0.0222 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 59.0 | 2124 | 0.0222 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 60.0 | 2160 | 0.0221 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 61.0 | 2196 | 0.0221 | 94.4944 | 72.0974 | 94.4944 | 94.6067 | 5.1292 |
| 0.0379 | 62.0 | 2232 | 0.0221 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 63.0 | 2268 | 0.0221 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 64.0 | 2304 | 0.0221 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 65.0 | 2340 | 0.0221 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 66.0 | 2376 | 0.0221 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 67.0 | 2412 | 0.0221 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 68.0 | 2448 | 0.0221 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0379 | 69.0 | 2484 | 0.0221 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
| 0.0369 | 70.0 | 2520 | 0.0221 | 94.8315 | 72.6592 | 94.8315 | 94.8876 | 5.1348 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.13.1
- Datasets 2.16.1
- Tokenizers 0.15.0
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