jrmccluskey commented on code in PR #31536:
URL: https://github.com/apache/beam/pull/31536#discussion_r1631247201
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sdks/python/apache_beam/ml/transforms/embeddings/huggingface.py:
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@@ -153,6 +154,45 @@ def get_ptransform_for_processing(self, **kwargs) ->
beam.PTransform:
))
+class SentenceTransformerImageEmbeddings(EmbeddingsManager):
+ def __init__(self, model_name: str, columns: List[str], **kwargs):
+ """
+ Embedding config for sentence-transformers. This config can be used with
+ MLTransform to embed image data. Models are loaded using the RunInference
+ PTransform with the help of ModelHandler.
+
+ Args:
+ model_name: Name of the model to use. The model should be hosted on
+ HuggingFace Hub or compatible with sentence_transformers. See
+
https://www.sbert.net/docs/sentence_transformer/pretrained_models.html#image-text-models
# pylint: disable=line-too-long
+ for a list of sentence_transformers models.
+ columns: List of columns to be embedded.
+ min_batch_size: The minimum batch size to be used for inference.
+ max_batch_size: The maximum batch size to be used for inference.
+ large_model: Whether to share the model across processes.
+ """
+ super().__init__(columns, **kwargs)
+ self.model_name = model_name
+
+ def get_model_handler(self):
+ return _SentenceTransformerModelHandler(
+ model_class=SentenceTransformer,
+ model_name=self.model_name,
+ load_model_args=self.load_model_args,
+ min_batch_size=self.min_batch_size,
+ max_batch_size=self.max_batch_size,
+ large_model=self.large_model)
+
+ def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
+ # wrap the model handler in a _TextEmbeddingHandler since
Review Comment:
Swapping to a bool assignment may be cleaner (and is how we probably need to
handle the Inference API version as well,) let me take a run at writing that
real quick
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