Instructions to use Gayathri142214002/Question_Generation_ComQ_17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gayathri142214002/Question_Generation_ComQ_17 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Gayathri142214002/Question_Generation_ComQ_17") model = AutoModelForSeq2SeqLM.from_pretrained("Gayathri142214002/Question_Generation_ComQ_17", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Question_Generation_ComQ_17
This model is a fine-tuned version of Gayathri142214002/Question_Generation_ComQ_16 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1992
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 |
|---|---|---|---|
| 0.1611 | 0.77 | 100 | 0.1509 |
| 0.148 | 1.54 | 200 | 0.1671 |
| 0.149 | 2.31 | 300 | 0.1783 |
| 0.1448 | 3.08 | 400 | 0.1796 |
| 0.1391 | 3.85 | 500 | 0.1877 |
| 0.1334 | 4.62 | 600 | 0.1931 |
| 0.1372 | 5.38 | 700 | 0.1964 |
| 0.1253 | 6.15 | 800 | 0.1981 |
| 0.1251 | 6.92 | 900 | 0.1992 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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