---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_bn
metrics:
- sacrebleu
model-index:
- name: squad-bn-qgen-mt5-all-metric
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: squad_bn
      type: squad_bn
      args: squad_bn
    metrics:
    - name: Sacrebleu
      type: sacrebleu
      value: 6.4143
---

<!-- 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. -->

# squad-bn-qgen-mt5-all-metric

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the squad_bn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7273
- Rouge1 Precision: 35.8589
- Rouge1 Recall: 29.7041
- Rouge1 Fmeasure: 31.6373
- Rouge2 Precision: 15.4203
- Rouge2 Recall: 12.5155
- Rouge2 Fmeasure: 13.3978
- Rougel Precision: 34.4684
- Rougel Recall: 28.5887
- Rougel Fmeasure: 30.4627
- Rougelsum Precision: 34.4252
- Rougelsum Recall: 28.5362
- Rougelsum Fmeasure: 30.4053
- Sacrebleu: 6.4143
- Meteor: 0.1416
- Gen Len: 16.7199

## 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: 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: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure | Sacrebleu | Meteor | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|:---------:|:------:|:-------:|
| 0.8449        | 1.0   | 16396 | 0.7340          | 31.6476          | 26.8901       | 28.2871         | 13.621           | 11.3545       | 11.958          | 30.3276          | 25.7754       | 27.1048         | 30.3426             | 25.7489          | 27.0991            | 5.9655    | 0.1336 | 16.8685 |
| 0.7607        | 2.0   | 32792 | 0.7182          | 33.7173          | 28.6115       | 30.1049         | 14.8227          | 12.2059       | 12.9453         | 32.149           | 27.2036       | 28.6617         | 32.2479             | 27.2261          | 28.7272            | 6.6093    | 0.138  | 16.8522 |
| 0.7422        | 3.0   | 49188 | 0.7083          | 34.6128          | 29.0223       | 30.7248         | 14.9888          | 12.3092       | 13.1021         | 33.2507          | 27.8154       | 29.4599         | 33.2848             | 27.812           | 29.5064            | 6.2407    | 0.1416 | 16.5806 |
| 0.705         | 4.0   | 65584 | 0.7035          | 34.156           | 29.0012       | 30.546          | 14.72            | 12.0251       | 12.8161         | 32.7527          | 27.6511       | 29.1955         | 32.7692             | 27.6627          | 29.231             | 6.1784    | 0.1393 | 16.7793 |
| 0.6859        | 5.0   | 81980 | 0.7038          | 35.1405          | 29.6033       | 31.2614         | 15.5108          | 12.6414       | 13.5059         | 33.8335          | 28.4264       | 30.0745         | 33.8782             | 28.4349          | 30.0901            | 6.5896    | 0.144  | 16.6651 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1