---
library_name: transformers
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: mt5-base-qaqg-finetuned-SQuAD-id-sentence
  results: []
---

<!-- 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-qaqg-finetuned-SQuAD-id-sentence

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4176
- Rouge: {'rouge1': 0.431906438503008, 'rouge2': 0.25499026452104945, 'rougeL': 0.39204274842839615, 'rougeLsum': 0.39456014504144676}
- Rouge1: 0.4319
- Rouge2: 0.2550
- Rougel: 0.3920
- Rougelsum: 0.3946
- Bleu: 0.2255

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- 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 | Rouge                                                                                                                          | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu   |
|:-------------:|:-----:|:-----:|:---------------:|:------------------------------------------------------------------------------------------------------------------------------:|:------:|:------:|:------:|:---------:|:------:|
| 1.8092        | 1.0   | 2000  | 1.5726          | {'rouge1': 0.38857764947308354, 'rouge2': 0.2144816356866815, 'rougeL': 0.34718290508264066, 'rougeLsum': 0.3491704618702567}  | 0.3886 | 0.2145 | 0.3472 | 0.3492    | 0.2027 |
| 1.448         | 2.0   | 4000  | 1.4578          | {'rouge1': 0.4201425066563658, 'rouge2': 0.24245589461633016, 'rougeL': 0.3801327577125928, 'rougeLsum': 0.38306624507182285}  | 0.4201 | 0.2425 | 0.3801 | 0.3831    | 0.2179 |
| 1.2703        | 3.0   | 6000  | 1.4241          | {'rouge1': 0.42984982575843933, 'rouge2': 0.2542816232928319, 'rougeL': 0.38888435744081745, 'rougeLsum': 0.39130709798526525} | 0.4298 | 0.2543 | 0.3889 | 0.3913    | 0.2251 |
| 1.2228        | 4.0   | 8000  | 1.4314          | {'rouge1': 0.4293519247279466, 'rouge2': 0.2525711759038574, 'rougeL': 0.3885634471147471, 'rougeLsum': 0.3910623048069688}    | 0.4294 | 0.2526 | 0.3886 | 0.3911    | 0.2240 |
| 1.1391        | 5.0   | 10000 | 1.4176          | {'rouge1': 0.431906438503008, 'rouge2': 0.25499026452104945, 'rougeL': 0.39204274842839615, 'rougeLsum': 0.39456014504144676}  | 0.4319 | 0.2550 | 0.3920 | 0.3946    | 0.2255 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0a0+f70bd71a48.nv24.06
- Datasets 2.21.0
- Tokenizers 0.19.1