Instructions to use AmitDantal/T5-fine_tuned_ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AmitDantal/T5-fine_tuned_ with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") model = PeftModel.from_pretrained(base_model, "AmitDantal/T5-fine_tuned_") - Transformers
How to use AmitDantal/T5-fine_tuned_ with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AmitDantal/T5-fine_tuned_", dtype="auto") - Notebooks
- Google Colab
- Kaggle
T5-fine_tuned_
This model is a fine-tuned version of t5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.9283
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.8189 | 0.2 | 1000 | 2.7719 |
| 6.2095 | 0.4 | 2000 | 2.8892 |
| 6.2846 | 0.6 | 3000 | 2.9284 |
| 6.1986 | 0.8 | 4000 | 2.9283 |
| 6.2482 | 1.0 | 5000 | 2.9283 |
Framework versions
- PEFT 0.18.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for AmitDantal/T5-fine_tuned_
Base model
google-t5/t5-base