---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-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. -->

# t5-small-finetuned-xsum

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0734
- Rouge1: 99.9038
- Rouge2: 99.838
- Rougel: 99.9145
- Rougelsum: 99.9038
- Gen Len: 93.9181

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log        | 1.0   | 180  | 1.7815          | 9.7268  | 2.7047  | 8.7069  | 8.7035    | 155.8472 |
| No log        | 2.0   | 360  | 0.6270          | 28.7135 | 19.99   | 27.1646 | 27.1386   | 265.2903 |
| 2.122         | 3.0   | 540  | 0.3572          | 21.4211 | 17.5143 | 21.0387 | 20.9118   | 142.7333 |
| 2.122         | 4.0   | 720  | 0.2757          | 92.8223 | 90.5077 | 92.0061 | 92.0015   | 87.0847  |
| 2.122         | 5.0   | 900  | 0.2493          | 95.6972 | 94.5082 | 95.5057 | 95.522    | 91.8556  |
| 0.4002        | 6.0   | 1080 | 0.2348          | 96.8942 | 96.2704 | 96.7552 | 96.7736   | 96.0764  |
| 0.4002        | 7.0   | 1260 | 0.2227          | 97.7669 | 97.4255 | 97.6867 | 97.6913   | 93.9097  |
| 0.4002        | 8.0   | 1440 | 0.2111          | 98.7823 | 98.5538 | 98.7622 | 98.7722   | 94.2875  |
| 0.2717        | 9.0   | 1620 | 0.1979          | 99.7455 | 99.6524 | 99.7428 | 99.7449   | 93.8569  |
| 0.2717        | 10.0  | 1800 | 0.1843          | 99.8967 | 99.8175 | 99.8953 | 99.8939   | 93.875   |
| 0.2717        | 11.0  | 1980 | 0.1716          | 99.9078 | 99.8578 | 99.9114 | 99.9095   | 93.8556  |
| 0.2244        | 12.0  | 2160 | 0.1606          | 99.9371 | 99.8807 | 99.9373 | 99.9373   | 93.9236  |
| 0.2244        | 13.0  | 2340 | 0.1512          | 99.9112 | 99.8535 | 99.9141 | 99.9103   | 93.8542  |
| 0.19          | 14.0  | 2520 | 0.1424          | 99.9573 | 99.919  | 99.9573 | 99.9573   | 93.9236  |
| 0.19          | 15.0  | 2700 | 0.1353          | 99.9679 | 99.9421 | 99.9679 | 99.9679   | 93.925   |
| 0.19          | 16.0  | 2880 | 0.1290          | 99.9234 | 99.8727 | 99.9323 | 99.9234   | 93.8736  |
| 0.1652        | 17.0  | 3060 | 0.1235          | 99.9252 | 99.8727 | 99.9359 | 99.9252   | 93.9222  |
| 0.1652        | 18.0  | 3240 | 0.1184          | 99.9038 | 99.8373 | 99.911  | 99.9021   | 93.8722  |
| 0.1652        | 19.0  | 3420 | 0.1137          | 99.9466 | 99.9074 | 99.9573 | 99.9466   | 93.9236  |
| 0.1471        | 20.0  | 3600 | 0.1092          | 99.9252 | 99.8727 | 99.9359 | 99.9252   | 93.9222  |
| 0.1471        | 21.0  | 3780 | 0.1053          | 99.9252 | 99.8727 | 99.9359 | 99.9252   | 93.9222  |
| 0.1471        | 22.0  | 3960 | 0.1014          | 99.9252 | 99.8727 | 99.9359 | 99.9252   | 93.9222  |
| 0.1331        | 23.0  | 4140 | 0.0982          | 99.9252 | 99.8727 | 99.9359 | 99.9252   | 93.9222  |
| 0.1331        | 24.0  | 4320 | 0.0949          | 99.9252 | 99.8727 | 99.9359 | 99.9252   | 93.9208  |
| 0.1226        | 25.0  | 4500 | 0.0918          | 99.9252 | 99.8727 | 99.9359 | 99.9252   | 93.9208  |
| 0.1226        | 26.0  | 4680 | 0.0892          | 99.9252 | 99.8727 | 99.9359 | 99.9252   | 93.9208  |
| 0.1226        | 27.0  | 4860 | 0.0867          | 99.9252 | 99.8727 | 99.9359 | 99.9252   | 93.9208  |
| 0.114         | 28.0  | 5040 | 0.0848          | 99.9145 | 99.8495 | 99.9252 | 99.9145   | 93.9194  |
| 0.114         | 29.0  | 5220 | 0.0828          | 99.9038 | 99.838  | 99.9145 | 99.9038   | 93.9181  |
| 0.114         | 30.0  | 5400 | 0.0811          | 99.9145 | 99.8495 | 99.9252 | 99.9145   | 93.9194  |
| 0.1074        | 31.0  | 5580 | 0.0794          | 99.9038 | 99.838  | 99.9145 | 99.9038   | 93.9181  |
| 0.1074        | 32.0  | 5760 | 0.0781          | 99.9038 | 99.838  | 99.9145 | 99.9038   | 93.9181  |
| 0.1074        | 33.0  | 5940 | 0.0769          | 99.9252 | 99.8669 | 99.9252 | 99.9252   | 93.9194  |
| 0.1027        | 34.0  | 6120 | 0.0757          | 99.9038 | 99.838  | 99.9145 | 99.9038   | 93.9181  |
| 0.1027        | 35.0  | 6300 | 0.0751          | 99.9038 | 99.838  | 99.9145 | 99.9038   | 93.9181  |
| 0.1027        | 36.0  | 6480 | 0.0745          | 99.9038 | 99.838  | 99.9145 | 99.9038   | 93.9181  |
| 0.0994        | 37.0  | 6660 | 0.0740          | 99.9038 | 99.838  | 99.9145 | 99.9038   | 93.9181  |
| 0.0994        | 38.0  | 6840 | 0.0737          | 99.9038 | 99.838  | 99.9145 | 99.9038   | 93.9181  |
| 0.0975        | 39.0  | 7020 | 0.0735          | 99.9038 | 99.838  | 99.9145 | 99.9038   | 93.9181  |
| 0.0975        | 40.0  | 7200 | 0.0734          | 99.9038 | 99.838  | 99.9145 | 99.9038   | 93.9181  |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2