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
Commit 路
e4e40fb
1
Parent(s): 91eb295
Add TF weights (#1)
Browse files- Add TF weights (c7e11940d82fe88cee320a959a3516005d68a745)
Co-authored-by: Joao Gante <joaogante@users.noreply.huggingface.co>
- tf_model.h5 +3 -0
tf_model.h5
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:69184a2ab8f59ee8de3cf5022000961f61d70028492caa5641d413f9f370f63c
|
| 3 |
+
size 1336926952
|