Instructions to use kxx-kkk/FYP_sq2_mrqa_overfitting_dontUse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kxx-kkk/FYP_sq2_mrqa_overfitting_dontUse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="kxx-kkk/FYP_sq2_mrqa_overfitting_dontUse")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("kxx-kkk/FYP_sq2_mrqa_overfitting_dontUse") model = AutoModelForQuestionAnswering.from_pretrained("kxx-kkk/FYP_sq2_mrqa_overfitting_dontUse") - Notebooks
- Google Colab
- Kaggle
FYP_sq2_mrqa
This model is a fine-tuned version of deepset/deberta-v3-base-squad2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2207
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.08 | 1.0 | 58676 | 1.1569 |
| 0.9346 | 2.0 | 117352 | 1.1576 |
| 0.8191 | 3.0 | 176028 | 1.2207 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 5
Model tree for kxx-kkk/FYP_sq2_mrqa_overfitting_dontUse
Base model
microsoft/deberta-v3-base Finetuned
deepset/deberta-v3-base-squad2