Instructions to use merthacioglu/bert-finetuned-squad_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use merthacioglu/bert-finetuned-squad_v2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="merthacioglu/bert-finetuned-squad_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("merthacioglu/bert-finetuned-squad_v2") model = AutoModelForQuestionAnswering.from_pretrained("merthacioglu/bert-finetuned-squad_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bert-finetuned-squad_v2
This model is a fine-tuned version of merthacioglu/bert-finetuned-squad on the None 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.12.0
- Tokenizers 0.13.2
- Downloads last month
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