Instructions to use Gayathri142214002/Question_Generation_ComQ_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gayathri142214002/Question_Generation_ComQ_10 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Gayathri142214002/Question_Generation_ComQ_10") model = AutoModelForSeq2SeqLM.from_pretrained("Gayathri142214002/Question_Generation_ComQ_10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Question_Generation_ComQ_10
This model is a fine-tuned version of Gayathri142214002/Question_Generation_ComQ_9 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2292
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 |
|---|---|---|---|
| 0.196 | 0.84 | 100 | 0.1793 |
| 0.1809 | 1.68 | 200 | 0.2003 |
| 0.1783 | 2.53 | 300 | 0.2110 |
| 0.1686 | 3.37 | 400 | 0.2178 |
| 0.1622 | 4.21 | 500 | 0.2223 |
| 0.1601 | 5.05 | 600 | 0.2233 |
| 0.1501 | 5.89 | 700 | 0.2292 |
| 0.1453 | 6.74 | 800 | 0.2292 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Base model
Gayathri142214002/Question_Generation_ComQ_8