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
- Xet hash:
- 1f1e81f79481a78b1345a39c1de6d91e0d8d7d5dfceb99a7d24282ab3fe2214f
- Size of remote file:
- 1.25 GB
- SHA256:
- 905fc7ee48cae3973d7c95c6631950b4161bff2c8c5859cdcd7981f7f1049e7f
路
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