Instructions to use Gayathri142214002/t5_Question_Generation_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gayathri142214002/t5_Question_Generation_4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Gayathri142214002/t5_Question_Generation_4") model = AutoModelForSeq2SeqLM.from_pretrained("Gayathri142214002/t5_Question_Generation_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
t5_Question_Generation_4
This model is a fine-tuned version of Gayathri142214002/t5_Question_Generation_3 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3653
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 |
|---|---|---|---|
| 0.4286 | 1.57 | 5000 | 0.3839 |
| 0.3469 | 3.14 | 10000 | 0.3729 |
| 0.2993 | 4.71 | 15000 | 0.3642 |
| 0.2704 | 6.28 | 20000 | 0.3653 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
- Downloads last month
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