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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-cnn-dailymail
results:
- task:
name: Sequence-to-sequence Language Modeling
type: summarization
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: train
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 32.8352
---
<!-- 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. -->
# mt5-small-finetuned-cnn-dailymail
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7294
- Rouge1: 32.8352
- Rouge2: 17.0633
- Rougel: 29.0888
- Rougelsum: 30.8226
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| No log | 1.0 | 8973 | 1.9272 | 31.6634 | 16.1653 | 28.1624 | 29.7819 |
| No log | 2.0 | 17946 | 1.8282 | 32.1032 | 16.4388 | 28.4914 | 30.1856 |
| No log | 3.0 | 26919 | 1.7967 | 32.5721 | 16.8392 | 28.8483 | 30.5764 |
| 2.1615 | 4.0 | 35892 | 1.7640 | 32.6788 | 16.94 | 28.994 | 30.6883 |
| 2.1615 | 5.0 | 44865 | 1.7450 | 32.8129 | 17.048 | 29.0788 | 30.8106 |
| 2.1615 | 6.0 | 53838 | 1.7379 | 32.7074 | 16.9641 | 28.9745 | 30.7043 |
| 2.1615 | 7.0 | 62811 | 1.7317 | 32.7692 | 17.0116 | 29.0395 | 30.7685 |
| 2.0886 | 8.0 | 71784 | 1.7294 | 32.8352 | 17.0633 | 29.0888 | 30.8226 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu102
- Datasets 2.7.1
- Tokenizers 0.13.2
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