Instructions to use Solutionsmedia/t5-math-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Solutionsmedia/t5-math-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Solutionsmedia/t5-math-model") model = AutoModelForSeq2SeqLM.from_pretrained("Solutionsmedia/t5-math-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
t5-math-model
This model is a fine-tuned version of Solutionsmedia/t5-math-model on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5010
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5678 | 0.4 | 50 | 0.5178 |
| 0.5539 | 0.8 | 100 | 0.5132 |
| 0.5412 | 1.2 | 150 | 0.5098 |
| 0.5391 | 1.6 | 200 | 0.5097 |
| 0.4775 | 2.0 | 250 | 0.5084 |
| 0.5456 | 2.4 | 300 | 0.5045 |
| 0.5331 | 2.8 | 350 | 0.5033 |
| 0.4802 | 3.2 | 400 | 0.5026 |
| 0.5832 | 3.6 | 450 | 0.5026 |
| 0.5087 | 4.0 | 500 | 0.5014 |
| 0.5032 | 4.4 | 550 | 0.5011 |
| 0.5697 | 4.8 | 600 | 0.5010 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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