Instructions to use Maungvee/hoshiBmaTchi_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maungvee/hoshiBmaTchi_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Maungvee/hoshiBmaTchi_model") model = AutoModelForSeq2SeqLM.from_pretrained("Maungvee/hoshiBmaTchi_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hoshiBmaTchi_model
This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4224
- Rouge1: 0.4716
- Rouge2: 0.2327
- Rougel: 0.3969
- Rougelsum: 0.3973
- Gen Len: 17.379
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.758 | 1.0 | 921 | 1.4669 | 0.4631 | 0.2228 | 0.3874 | 0.3873 | 17.2237 |
| 1.5697 | 2.0 | 1842 | 1.4307 | 0.4703 | 0.2317 | 0.3951 | 0.3955 | 17.3435 |
| 1.5241 | 3.0 | 2763 | 1.4224 | 0.4716 | 0.2327 | 0.3969 | 0.3973 | 17.379 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.4.1
- Tokenizers 0.21.2
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Model tree for Maungvee/hoshiBmaTchi_model
Base model
google-t5/t5-base