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---
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small_adafactor
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 32.8631
---
<!-- 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_adafactor
This model is a fine-tuned version of [oMateos2020/t5-small_adafactor](https://huggingface.co/oMateos2020/t5-small_adafactor) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1167
- Rouge1: 32.8631
- Rouge2: 11.658
- Rougel: 26.6192
- Rougelsum: 26.6224
- Gen Len: 18.7663
## 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: 0.0005
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.1315 | 0.02 | 200 | 2.1865 | 31.9486 | 10.9605 | 25.7418 | 25.7408 | 18.8466 |
| 2.1297 | 0.05 | 400 | 2.1965 | 31.9598 | 10.9463 | 25.784 | 25.7867 | 18.8525 |
| 2.1284 | 0.07 | 600 | 2.1981 | 32.231 | 11.1003 | 26.0155 | 26.0226 | 18.8466 |
| 2.1315 | 0.09 | 800 | 2.1873 | 31.9161 | 10.8642 | 25.7166 | 25.7273 | 18.8227 |
| 2.1212 | 0.12 | 1000 | 2.1892 | 32.4646 | 11.1852 | 26.2451 | 26.2439 | 18.8259 |
| 2.1028 | 0.14 | 1200 | 2.1978 | 32.2886 | 11.1346 | 26.0795 | 26.0827 | 18.7685 |
| 2.1221 | 0.16 | 1400 | 2.1936 | 32.2901 | 11.0821 | 25.9983 | 26.0024 | 18.7798 |
| 2.1168 | 0.19 | 1600 | 2.1922 | 32.1655 | 11.1451 | 25.986 | 25.9893 | 18.8232 |
| 2.1166 | 0.21 | 1800 | 2.1836 | 32.2611 | 11.174 | 26.0594 | 26.0688 | 18.7633 |
| 2.1053 | 0.24 | 2000 | 2.1929 | 32.3321 | 11.213 | 26.1859 | 26.1903 | 18.7758 |
| 2.1126 | 0.26 | 2200 | 2.1811 | 32.2078 | 11.1792 | 26.0776 | 26.0817 | 18.8197 |
| 2.1038 | 0.28 | 2400 | 2.1836 | 32.2799 | 11.2511 | 26.1191 | 26.1251 | 18.7884 |
| 2.1181 | 0.31 | 2600 | 2.1805 | 32.1197 | 11.1586 | 26.0441 | 26.0441 | 18.8045 |
| 2.1217 | 0.33 | 2800 | 2.1806 | 32.3051 | 11.2638 | 26.1319 | 26.1386 | 18.7886 |
| 2.116 | 0.35 | 3000 | 2.1741 | 32.2799 | 11.1887 | 26.1224 | 26.1363 | 18.7769 |
| 2.1118 | 0.38 | 3200 | 2.1767 | 32.387 | 11.2053 | 26.077 | 26.0845 | 18.8407 |
| 2.1164 | 0.4 | 3400 | 2.1743 | 32.5008 | 11.4021 | 26.3291 | 26.3297 | 18.7731 |
| 2.1068 | 0.42 | 3600 | 2.1673 | 32.2347 | 11.1676 | 26.0657 | 26.0662 | 18.817 |
| 2.1276 | 0.45 | 3800 | 2.1664 | 32.2434 | 11.2862 | 26.094 | 26.0994 | 18.7713 |
| 2.1313 | 0.47 | 4000 | 2.1636 | 32.694 | 11.3724 | 26.4071 | 26.4008 | 18.7709 |
| 2.1229 | 0.49 | 4200 | 2.1633 | 32.456 | 11.4057 | 26.2733 | 26.2689 | 18.7586 |
| 2.129 | 0.52 | 4400 | 2.1641 | 32.309 | 11.2133 | 26.1062 | 26.1121 | 18.7729 |
| 2.1425 | 0.54 | 4600 | 2.1577 | 32.5879 | 11.4001 | 26.3045 | 26.3078 | 18.8104 |
| 2.1536 | 0.56 | 4800 | 2.1507 | 32.5152 | 11.4035 | 26.3054 | 26.3116 | 18.7941 |
| 2.148 | 0.59 | 5000 | 2.1503 | 32.8088 | 11.5641 | 26.5346 | 26.5311 | 18.7602 |
| 2.1541 | 0.61 | 5200 | 2.1491 | 32.8185 | 11.5816 | 26.5261 | 26.527 | 18.7654 |
| 2.155 | 0.64 | 5400 | 2.1466 | 32.7229 | 11.5339 | 26.4363 | 26.442 | 18.8404 |
| 2.1579 | 0.66 | 5600 | 2.1435 | 32.884 | 11.6042 | 26.5862 | 26.5891 | 18.7713 |
| 2.1601 | 0.68 | 5800 | 2.1393 | 32.8027 | 11.5328 | 26.4521 | 26.4567 | 18.7904 |
| 2.1765 | 0.71 | 6000 | 2.1393 | 32.8059 | 11.5751 | 26.5499 | 26.5551 | 18.7768 |
| 2.2176 | 0.73 | 6200 | 2.1345 | 33.0734 | 11.8056 | 26.7546 | 26.7607 | 18.7756 |
| 2.2126 | 0.75 | 6400 | 2.1328 | 32.7478 | 11.5925 | 26.5333 | 26.5359 | 18.7819 |
| 2.1916 | 0.78 | 6600 | 2.1298 | 32.658 | 11.491 | 26.379 | 26.3869 | 18.8101 |
| 2.2162 | 0.8 | 6800 | 2.1297 | 32.7843 | 11.5629 | 26.4736 | 26.4728 | 18.8187 |
| 2.2358 | 0.82 | 7000 | 2.1287 | 32.9181 | 11.6378 | 26.5966 | 26.5987 | 18.8039 |
| 2.2371 | 0.85 | 7200 | 2.1265 | 32.8413 | 11.674 | 26.5905 | 26.5831 | 18.7962 |
| 2.256 | 0.87 | 7400 | 2.1245 | 32.7412 | 11.5627 | 26.4976 | 26.503 | 18.7728 |
| 2.2566 | 0.89 | 7600 | 2.1220 | 32.8165 | 11.6069 | 26.5301 | 26.5295 | 18.7871 |
| 2.2954 | 0.92 | 7800 | 2.1197 | 32.7399 | 11.5417 | 26.4914 | 26.4938 | 18.7752 |
| 2.2766 | 0.94 | 8000 | 2.1187 | 32.853 | 11.6411 | 26.5909 | 26.5938 | 18.7852 |
| 2.3273 | 0.96 | 8200 | 2.1169 | 32.9376 | 11.709 | 26.6665 | 26.6672 | 18.7734 |
| 2.3182 | 0.99 | 8400 | 2.1167 | 32.8631 | 11.658 | 26.6192 | 26.6224 | 18.7663 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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