Instructions to use microsoft/BiomedVLP-CXR-BERT-specialized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/BiomedVLP-CXR-BERT-specialized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="microsoft/BiomedVLP-CXR-BERT-specialized", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/BiomedVLP-CXR-BERT-specialized", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Ozan Oktay commited on
Commit ·
b990504
1
Parent(s): 32bc7c9
add model
Browse files- modeling_cxrbert.py +1 -1
modeling_cxrbert.py
CHANGED
|
@@ -12,7 +12,7 @@ from torch import Tensor as T
|
|
| 12 |
from transformers import BertForMaskedLM
|
| 13 |
from transformers.modeling_outputs import ModelOutput
|
| 14 |
|
| 15 |
-
from .configuration_cxrbert import CXRBertConfig
|
| 16 |
|
| 17 |
BERTTupleOutput = Tuple[T, T, T, T, T]
|
| 18 |
|
|
|
|
| 12 |
from transformers import BertForMaskedLM
|
| 13 |
from transformers.modeling_outputs import ModelOutput
|
| 14 |
|
| 15 |
+
from .configuration_cxrbert import CXRBertConfig
|
| 16 |
|
| 17 |
BERTTupleOutput = Tuple[T, T, T, T, T]
|
| 18 |
|