transformers pytorch elastic_t5 text2text-generation generated_from_trainer en ro dataset:wmt16 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
language:
- en
- ro
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: t5
  results:
  - task:
      name: Translation
      type: translation
    dataset:
      name: wmt16 ro-en
      type: wmt16
      args: ro-en
    metrics:
    - name: Bleu
      type: bleu
      value: 27.1318
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# t5

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3574
- Bleu: 27.1318
- Gen Len: 42.5798
- Loss Smallest Subnet: 1.3574
- Bleu Smallest Subnet: 27.1318
- Gen Len Smallest Subnet: 42.5798
- Loss Random Subnet: 1.3574
- Loss Sum: 4.0723
- Bleu Random Subnet: 27.1318
- Bleu Sum: 81.3954
- Gen Len Random Subnet: 42.5798
- Gen Len Sum: 127.7394

## 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: 5e-05
- train_batch_size: 12
- eval_batch_size: 24
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 48
- total_eval_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len | Loss Smallest Subnet | Bleu Smallest Subnet | Gen Len Smallest Subnet | Loss Random Subnet | Loss Sum | Bleu Random Subnet | Bleu Sum | Gen Len Random Subnet | Gen Len Sum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:--------------------:|:--------------------:|:-----------------------:|:------------------:|:--------:|:------------------:|:--------:|:---------------------:|:-----------:|
| 0.5967        | 1.0   | 12715 | 1.3820          | 26.593  | 42.4422 | 1.3820               | 26.593               | 42.4422                 | 1.3820             | 4.1461   | 26.593             | 79.779   | 42.4422               | 127.3266    |
| 0.5768        | 2.0   | 25430 | 1.3728          | 26.6191 | 42.6738 | 1.3728               | 26.6191              | 42.6738                 | 1.3728             | 4.1184   | 26.6191            | 79.8573  | 42.6738               | 128.0214    |
| 0.5663        | 3.0   | 38145 | 1.3616          | 26.9203 | 42.5298 | 1.3616               | 26.9203              | 42.5298                 | 1.3616             | 4.0849   | 26.9203            | 80.7609  | 42.5298               | 127.5894    |
| 0.5523        | 4.0   | 50860 | 1.3570          | 27.0195 | 42.5203 | 1.3570               | 27.0195              | 42.5203                 | 1.3570             | 4.0709   | 27.0195            | 81.0585  | 42.5203               | 127.5609    |
| 0.5436        | 5.0   | 63575 | 1.3574          | 27.1318 | 42.5798 | 1.3574               | 27.1318              | 42.5798                 | 1.3574             | 4.0723   | 27.1318            | 81.3954  | 42.5798               | 127.7394    |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.8.0
- Datasets 2.4.0
- Tokenizers 0.12.1
