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
Joao Gante commited on
Commit 路
c7e1194
1
Parent(s): 8ebfa4a
Add TF weights
Browse filesModel converted by the `transformers`' `pt_to_tf` CLI -- all converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=2.146e-05; Maximum converted output difference=2.146e-05.
- tf_model.h5 +3 -0
tf_model.h5
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