---
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