Instructions to use ntworld6/flan_t5_mobile_descriptions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ntworld6/flan_t5_mobile_descriptions with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ntworld6/flan_t5_mobile_descriptions") model = AutoModelForSeq2SeqLM.from_pretrained("ntworld6/flan_t5_mobile_descriptions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
flan_t5_mobile_descriptions
This model is a fine-tuned version of google/flan-t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0237
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: 0.0003
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.3193 | 1.0 | 3 | 0.6287 |
| 1.2454 | 2.0 | 6 | 0.2379 |
| 0.8150 | 3.0 | 9 | 0.0913 |
| 0.4069 | 4.0 | 12 | 0.0461 |
| 0.2608 | 5.0 | 15 | 0.0273 |
| 0.2279 | 6.0 | 18 | 0.0249 |
| 0.1086 | 7.0 | 21 | 0.0240 |
| 0.1781 | 8.0 | 24 | 0.0237 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for ntworld6/flan_t5_mobile_descriptions
Base model
google/flan-t5-small