Instructions to use Gayathri142214002/Question_Generation_ComQ_13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gayathri142214002/Question_Generation_ComQ_13 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Gayathri142214002/Question_Generation_ComQ_13") model = AutoModelForSeq2SeqLM.from_pretrained("Gayathri142214002/Question_Generation_ComQ_13", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Question_Generation_ComQ_13
This model is a fine-tuned version of Gayathri142214002/Question_Generation_ComQ_12 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2058
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 |
|---|---|---|---|
| 0.178 | 0.8 | 100 | 0.1611 |
| 0.1684 | 1.61 | 200 | 0.1737 |
| 0.158 | 2.41 | 300 | 0.1884 |
| 0.1556 | 3.22 | 400 | 0.1929 |
| 0.1521 | 4.02 | 500 | 0.1964 |
| 0.1434 | 4.83 | 600 | 0.2016 |
| 0.1406 | 5.63 | 700 | 0.2040 |
| 0.1394 | 6.44 | 800 | 0.2058 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 12
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Gayathri142214002/Question_Generation_ComQ_8