|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- mlsum |
|
metrics: |
|
- rouge |
|
base_model: google/mt5-base |
|
model-index: |
|
- name: mt5-base-finetuned-xsum-mlsum___topic_text_google_mt5_base |
|
results: |
|
- task: |
|
type: text2text-generation |
|
name: Sequence-to-sequence Language Modeling |
|
dataset: |
|
name: mlsum |
|
type: mlsum |
|
args: es |
|
metrics: |
|
- type: rouge |
|
value: 0.1582 |
|
name: Rouge1 |
|
--- |
|
|
|
<!-- 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-base-finetuned-xsum-mlsum___topic_text_google_mt5_base |
|
|
|
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mlsum dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: nan |
|
- Rouge1: 0.1582 |
|
- Rouge2: 0.0133 |
|
- Rougel: 0.1585 |
|
- Rougelsum: 0.1586 |
|
- Gen Len: 10.2326 |
|
|
|
## 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 |
|
- num_epochs: 1 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
|
| 0.0 | 1.0 | 66592 | nan | 0.1582 | 0.0133 | 0.1585 | 0.1586 | 10.2326 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.19.2 |
|
- Pytorch 1.11.0+cu113 |
|
- Datasets 2.2.2 |
|
- Tokenizers 0.12.1 |
|
|