Instructions to use Gayathri142214002/Question_Generation_ComQ_14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gayathri142214002/Question_Generation_ComQ_14 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Gayathri142214002/Question_Generation_ComQ_14") model = AutoModelForSeq2SeqLM.from_pretrained("Gayathri142214002/Question_Generation_ComQ_14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Question_Generation_ComQ_14
This model is a fine-tuned version of Gayathri142214002/Question_Generation_ComQ_13 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2129
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 |
|---|---|---|---|
| 0.1754 | 0.79 | 100 | 0.1618 |
| 0.1585 | 1.58 | 200 | 0.1822 |
| 0.1601 | 2.38 | 300 | 0.1877 |
| 0.1494 | 3.17 | 400 | 0.1956 |
| 0.1469 | 3.96 | 500 | 0.2006 |
| 0.1399 | 4.75 | 600 | 0.2082 |
| 0.1352 | 5.54 | 700 | 0.2119 |
| 0.134 | 6.34 | 800 | 0.2129 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 15
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for Gayathri142214002/Question_Generation_ComQ_14
Base model
Gayathri142214002/Question_Generation_ComQ_8