Instructions to use Gayathri142214002/Question_Generation_ComQ_11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gayathri142214002/Question_Generation_ComQ_11 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Gayathri142214002/Question_Generation_ComQ_11") model = AutoModelForSeq2SeqLM.from_pretrained("Gayathri142214002/Question_Generation_ComQ_11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Question_Generation_ComQ_11
This model is a fine-tuned version of Gayathri142214002/Question_Generation_ComQ_10 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2960
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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 |
|---|---|---|---|
| 0.2405 | 1.71 | 1000 | 0.2576 |
| 0.2043 | 3.42 | 2000 | 0.2805 |
| 0.1814 | 5.13 | 3000 | 0.2842 |
| 0.1591 | 6.83 | 4000 | 0.2960 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Downloads last month
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Base model
Gayathri142214002/Question_Generation_ComQ_8