Instructions to use elpeeee/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elpeeee/results with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("elpeeee/results") model = AutoModelForSeq2SeqLM.from_pretrained("elpeeee/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
results
This model is a fine-tuned version of elpeeee/results on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0215
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0654 | 1.0 | 105 | 0.0266 |
| 0.0626 | 2.0 | 210 | 0.0254 |
| 0.0635 | 3.0 | 315 | 0.0242 |
| 0.0573 | 4.0 | 420 | 0.0234 |
| 0.058 | 5.0 | 525 | 0.0227 |
| 0.0564 | 6.0 | 630 | 0.0222 |
| 0.0572 | 7.0 | 735 | 0.0219 |
| 0.0536 | 8.0 | 840 | 0.0217 |
| 0.0543 | 9.0 | 945 | 0.0215 |
| 0.0547 | 10.0 | 1050 | 0.0215 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Tokenizers 0.21.0
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