apache_beam.ml.transforms.embeddings.huggingface module¶
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class
apache_beam.ml.transforms.embeddings.huggingface.SentenceTransformerEmbeddings(model_name: str, columns: List[str], max_seq_length: Optional[int] = None, image_model: bool = False, **kwargs)[source]¶ Bases:
apache_beam.ml.transforms.base.EmbeddingsManagerEmbedding config for sentence-transformers. This config can be used with MLTransform to embed text data. Models are loaded using the RunInference PTransform with the help of ModelHandler.
Parameters: - model_name – Name of the model to use. The model should be hosted on HuggingFace Hub or compatible with sentence_transformers. For image embedding models, see https://www.sbert.net/docs/sentence_transformer/pretrained_models.html#image-text-models # pylint: disable=line-too-long for a list of available sentence_transformers models.
- columns – List of columns to be embedded.
- max_seq_length – Max sequence length to use for the model if applicable.
- image_model – Whether the model is generating image embeddings.
- 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.
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class
apache_beam.ml.transforms.embeddings.huggingface.InferenceAPIEmbeddings(hf_token: Optional[str], columns: List[str], model_name: Optional[str] = None, api_url: Optional[str] = None, **kwargs)[source]¶ Bases:
apache_beam.ml.transforms.base.EmbeddingsManagerFeature extraction using HuggingFace’s Inference API. Intended to be used for feature-extraction. For other tasks, please refer to https://huggingface.co/inference-api.
Parameters: - hf_token – HuggingFace token.
- columns – List of columns to be embedded.
- model_name – Model name used for feature extraction.
- api_url – API url for feature extraction. If specified, model_name will be ignored. If none, the default url for feature extraction will be used.
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api_url¶