---
license: bsd-3-clause
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: long-t5-tglobal-base-mediasum
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: multi_news
      type: multi_news
      config: default
      split: train[:20000]
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.3246
---

<!-- 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. -->

# long-t5-tglobal-base-mediasum

This model is a fine-tuned version of [pszemraj/long-t5-tglobal-base-16384-book-summary](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0387
- Rouge1: 0.3246
- Rouge2: 0.0867
- Rougel: 0.1663
- Rougelsum: 0.1662
- Gen Len: 106.985

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.4191        | 1.0   | 4500  | 2.0952          | 0.3389 | 0.0882 | 0.1706 | 0.1706    | 118.285 |
| 2.3462        | 2.0   | 9000  | 2.0484          | 0.3339 | 0.0887 | 0.1683 | 0.1683    | 111.936 |
| 2.3458        | 3.0   | 13500 | 2.0387          | 0.3246 | 0.0867 | 0.1663 | 0.1662    | 106.985 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3