Instructions to use Sahil4818/finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Sahil4818/finetuned with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-v3-base") model = PeftModel.from_pretrained(base_model, "Sahil4818/finetuned") - Transformers
How to use Sahil4818/finetuned with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sahil4818/finetuned", dtype="auto") - Notebooks
- Google Colab
- Kaggle
finetuned
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.1953
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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 17.6049 | 1.0 | 203 | 2.1953 |
Framework versions
- PEFT 0.18.1
- Transformers 5.2.0
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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Base model
microsoft/deberta-v3-base