Question Answering
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
bert
Eval Results (legacy)
Instructions to use deepset/bert-large-uncased-whole-word-masking-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/bert-large-uncased-whole-word-masking-squad2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="deepset/bert-large-uncased-whole-word-masking-squad2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/bert-large-uncased-whole-word-masking-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/bert-large-uncased-whole-word-masking-squad2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering.
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## Overview
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**Language model:** bert-large
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**Language:** English
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**Downstream-task:** Extractive QA
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This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering.
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## Overview
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**Language model:** bert-large
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**Language:** English
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**Downstream-task:** Extractive QA
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