---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: t5-v1_1-small-finetuned-cnn_dailymail
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: cnn_dailymail
      type: cnn_dailymail
      config: 3.0.0
      split: train
      args: 3.0.0
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.3362804924711526
---

<!-- 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-v1_1-small-finetuned-cnn_dailymail

This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7290
- Rouge1: 0.3363
- Rouge2: 0.1736
- Rougel: 0.2951
- Rougelsum: 0.3151

## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.7338        | 1.0   | 35890  | 1.8390          | 0.3278 | 0.1658 | 0.2876 | 0.3064    |
| 2.3233        | 2.0   | 71780  | 1.7779          | 0.3335 | 0.1713 | 0.2924 | 0.3124    |
| 2.2253        | 3.0   | 107670 | 1.7428          | 0.3348 | 0.1728 | 0.2941 | 0.3138    |
| 2.1797        | 4.0   | 143560 | 1.7290          | 0.3363 | 0.1736 | 0.2951 | 0.3151    |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2