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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pubmed-summarization
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pubmed-summarization
type: pubmed-summarization
config: section
split: validation
args: section
metrics:
- name: Rouge1
type: rouge
value: 14.1074
---
<!-- 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-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the pubmed-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3381
- Rouge1: 14.1074
- Rouge2: 5.3407
- Rougel: 11.9593
- Rougelsum: 12.9286
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.0498 | 1.0 | 2500 | 2.4883 | 12.7167 | 5.1639 | 10.969 | 11.902 |
| 2.8737 | 2.0 | 5000 | 2.4022 | 13.812 | 5.1042 | 11.7056 | 12.6907 |
| 2.7603 | 3.0 | 7500 | 2.3895 | 13.6588 | 5.1146 | 11.6214 | 12.5331 |
| 2.6946 | 4.0 | 10000 | 2.3523 | 13.7167 | 5.2024 | 11.669 | 12.5419 |
| 2.6527 | 5.0 | 12500 | 2.3383 | 14.082 | 5.2787 | 11.9031 | 12.875 |
| 2.6303 | 6.0 | 15000 | 2.3381 | 14.1074 | 5.3407 | 11.9593 | 12.9286 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|