Instructions to use Gachomba/multichoice-question-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gachomba/multichoice-question-generator with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Gachomba/multichoice-question-generator") model = AutoModelForSeq2SeqLM.from_pretrained("Gachomba/multichoice-question-generator") - Notebooks
- Google Colab
- Kaggle
multichoice-question-generator
This model is a fine-tuned version of facebook/bart-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1787
Model description
More information needed
Intended uses & limitations
This is an early version of a model meant to generate multichoice questions from text
Link to sample usage guide
https://github.com/Gach-omba/Multichoice-question-generation/blob/main/sample_usage.ipynb
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: 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2218 | 1.0 | 1000 | 0.1910 |
| 0.1913 | 2.0 | 2000 | 0.1811 |
| 0.1727 | 3.0 | 3000 | 0.1787 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Tokenizers 0.19.1
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Model tree for Gachomba/multichoice-question-generator
Base model
facebook/bart-large