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
File size: 889 Bytes
8cc04d6 0fdcbd3 8cc04d6 9f0b167 8cc04d6 0fdcbd3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | # ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ------------------------------------------------------------------------------------------
from typing import Any
from transformers import BertConfig, BertTokenizer
class CXRBertConfig(BertConfig):
"""
Config class for CXR-BERT model.
:param projection_size: Dimensionality of the joint latent space.
"""
model_type = "cxr-bert"
def __init__(self, projection_size: int = 128, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.projection_size = projection_size
class CXRBertTokenizer(BertTokenizer):
def __init__(self, **kwargs: Any) -> None:
super().__init__(**kwargs)
|