Instructions to use spsdevil/deberta-v3-large-squad2-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spsdevil/deberta-v3-large-squad2-finetuned-squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="spsdevil/deberta-v3-large-squad2-finetuned-squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("spsdevil/deberta-v3-large-squad2-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("spsdevil/deberta-v3-large-squad2-finetuned-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
deberta-v3-large-squad2-finetuned-squad
This model is a fine-tuned version of deepset/deberta-v3-large-squad2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.4651
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: 7e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.0478 | 1.0 | 1567 | 2.0644 |
| 1.7901 | 2.0 | 3134 | 2.1144 |
| 1.4437 | 3.0 | 4701 | 2.4651 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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