File size: 2,811 Bytes
de18fb0 fc30114 9325e67 de18fb0 f425c1c de18fb0 f425c1c de18fb0 f425c1c de18fb0 f425c1c de18fb0 f425c1c de18fb0 f425c1c de18fb0 f425c1c de18fb0 f425c1c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
---
license: apache-2.0
tags:
- generated_from_trainer
pipeline_tag: text2text-generation
inference:
parameters:
max_length: 256
num_beams: 4
length_penalty: 1.5
no_repeat_ngram_size: 3
early_stopping: true
base_model: yhavinga/t5-base-dutch
model-index:
- name: t5-end2end-questions-generation-dutch
results: []
---
# t5-end2end-questions-generation-dutch
This model is a fine-tuned version of [yhavinga/t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch) on a
Google translated version of SQUAD 1.1 found here: https://www.kaggle.com/datasets/michelvanheijningen/squad1-dutch.
The code used to finetune the model is largely based on the work by Thomas Simonini. You can find his English model
[here](https://huggingface.co/ThomasSimonini/t5-end2end-question-generation) and his Google colab tutorial [here](https://colab.research.google.com/drive/1z-Zl2hftMrFXabYfmz8o9YZpgYx6sGeW?usp=sharing)
It achieves the following results on the evaluation set:
- Loss: 1.6546
## Model description
This is my first model ever and still a work in progress ;)
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6528 | 0.34 | 100 | 1.9249 |
| 1.964 | 0.68 | 200 | 1.7897 |
| 1.8695 | 1.02 | 300 | 1.7554 |
| 1.7922 | 1.35 | 400 | 1.7270 |
| 1.7747 | 1.69 | 500 | 1.7054 |
| 1.7473 | 2.03 | 600 | 1.7019 |
| 1.697 | 2.37 | 700 | 1.6868 |
| 1.6848 | 2.71 | 800 | 1.6810 |
| 1.6756 | 3.05 | 900 | 1.6779 |
| 1.6282 | 3.39 | 1000 | 1.6712 |
| 1.6285 | 3.73 | 1100 | 1.6626 |
| 1.6161 | 4.06 | 1200 | 1.6616 |
| 1.5887 | 4.4 | 1300 | 1.6588 |
| 1.5877 | 4.74 | 1400 | 1.6583 |
| 1.5723 | 5.08 | 1500 | 1.6560 |
| 1.5545 | 5.42 | 1600 | 1.6550 |
| 1.5415 | 5.76 | 1700 | 1.6540 |
| 1.5509 | 6.1 | 1800 | 1.6541 |
| 1.5326 | 6.44 | 1900 | 1.6539 |
| 1.5268 | 6.77 | 2000 | 1.6546 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2 |