embetter 0.8.0

Just a bunch of useful embeddings to get started quickly.

embetter

"Just a bunch of useful embeddings for scikit-learn pipelines, to get started quickly."

Embetter implements scikit-learn compatible embeddings for computer vision and text. It should make it very easy to quickly build proof of concepts using scikit-learn pipelines and, in particular, should help with bulk labelling. It's also meant to play nice with bulk and scikit-partial but it can also be used together with your favorite ANN solution like lancedb.

Install

You can install via pip.

python -m pip install embetter

Many of the embeddings are optional depending on your use-case, so if you want to nit-pick to download only the tools that you need:

python -m pip install "embetter[text]"
python -m pip install "embetter[spacy]"
python -m pip install "embetter[sense2vec]"
python -m pip install "embetter[vision]"
python -m pip install "embetter[all]"

API Design

This is what's being implemented now.

# Helpers to grab text or image from pandas column.
from embetter.grab import ColumnGrabber

# Representations/Helpers for computer vision
from embetter.vision import ImageLoader, TimmEncoder, ColorHistogramEncoder

# Representations for text
from embetter.text import SentenceEncoder, MatryoshkaEncoder, Sense2VecEncoder, spaCyEncoder, TextEncoder

# Representations from multi-modal models
from embetter.multi import ClipEncoder

# Finetuning components 
from embetter.finetune import FeedForwardTuner, ContrastiveTuner, ContrastiveLearner, SbertLearner

# External embedding providers, typically needs an API key
from embetter.external import CohereEncoder, OpenAIEncoder

All of these components are scikit-learn compatible, which means that you can apply them as you would normally in a scikit-learn pipeline. Just be aware that these components are stateless. They won't require training as these are all pretrained tools.

Text Example

To run this example, make sure that you pip install 'embetter[sbert]'.

import pandas as pd
from sklearn.pipeline import make_pipeline 
from sklearn.linear_model import LogisticRegression

from embetter.grab import ColumnGrabber
from embetter.text import SentenceEncoder

# This pipeline grabs the `text` column from a dataframe
# which then get fed into Sentence-Transformers' all-MiniLM-L6-v2.
text_emb_pipeline = make_pipeline(
  ColumnGrabber("text"),
  SentenceEncoder('all-MiniLM-L6-v2')
)

# This pipeline can also be trained to make predictions, using
# the embedded features. 
text_clf_pipeline = make_pipeline(
  text_emb_pipeline,
  LogisticRegression()
)

dataf = pd.DataFrame({
  "text": ["positive sentiment", "super negative"],
  "label_col": ["pos", "neg"]
})
X = text_emb_pipeline.fit_transform(dataf, dataf['label_col'])
text_clf_pipeline.fit(dataf, dataf['label_col']).predict(dataf)

Image Example

The goal of the API is to allow pipelines like this:

import pandas as pd
from sklearn.pipeline import make_pipeline 
from sklearn.linear_model import LogisticRegression

from embetter.grab import ColumnGrabber
from embetter.vision import ImageLoader
from embetter.multi import ClipEncoder

# This pipeline grabs the `img_path` column from a dataframe
# then it grabs the image paths and turns them into `PIL.Image` objects
# which then get fed into CLIP which can also handle images.
image_emb_pipeline = make_pipeline(
  ColumnGrabber("img_path"),
  ImageLoader(convert="RGB"),
  ClipEncoder()
)

dataf = pd.DataFrame({
  "img_path": ["tests/data/thiscatdoesnotexist.jpeg"]
})
image_emb_pipeline.fit_transform(dataf)

Batched Learning

All of the encoding tools you've seen here are also compatible with the partial_fit mechanic in scikit-learn. That means you can leverage scikit-partial to build pipelines that can handle out-of-core datasets.