Skip to main content

Documentation Index

Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt

Use this file to discover all available pages before exploring further.

View Source on Github
minimum required for Auto Embeddings
Install extra dependencies in the SDK: 
%pip install -q "arize[AutoEmbeddings]<8.0.0"

​
The EmbeddingGenerator Class

Arize class to generate embeddings data. Import and initialize EmbeddingGenerator from arize.pandas.embeddings: 
from arize.pandas.embeddings import EmbeddingGenerator

​
Methods

from_use_case View Source Pass in use_case and more options depending on the use case.
ArgumentDescription
use_caseUseCases.NLP.SEQUENCE_CLASSIFICATION orUseCases.NLP.SUMMARIZATION orUseCases.CV.IMAGE_CLASSIFICATION
model_nameRefer to Supported Models
list_pretrained_models View Source Returns updated table listing of supported models. 
EmbeddingGenerator.list_pretrained_models()

​
Code Example

from arize.pandas.embeddings import EmbeddingGenerator, UseCases

# example CV
generator = EmbeddingGenerator.from_use_case(
    use_case=UseCases.CV.IMAGE_CLASSIFICATION,
    model_name="google/vit-base-patch16-224-in21k",
    batch_size=100
)
df["image_vector"] = generator.generate_embeddings(
    local_image_path_col=df["local_path"]
)

# example NLP
generator = EmbeddingGenerator.from_use_case(
    use_case=UseCases.NLP.SEQUENCE_CLASSIFICATION,
    model_name="distilbert-base-uncased",
    tokenizer_max_length=512,
    batch_size=100
)
df["text_vector"] = generator.generate_embeddings(text_col=df["text"])