Instructions to use svmarakin/bert-finetuned-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use svmarakin/bert-finetuned-squad2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="svmarakin/bert-finetuned-squad2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("svmarakin/bert-finetuned-squad2") model = AutoModelForQuestionAnswering.from_pretrained("svmarakin/bert-finetuned-squad2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert-finetuned-squad2
This model is a fine-tuned version of timpal0l/mdeberta-v3-base-squad2 on an unknown dataset.
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: 8
- eval_batch_size: 8
- seed: 42
- 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: 5
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
- Downloads last month
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Model tree for svmarakin/bert-finetuned-squad2
Base model
microsoft/mdeberta-v3-base Finetuned
timpal0l/mdeberta-v3-base-squad2