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https://github.com/koaning/embetter
just a bunch of useful embeddings
https://github.com/koaning/embetter
Last synced: about 1 month ago
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just a bunch of useful embeddings
- Host: GitHub
- URL: https://github.com/koaning/embetter
- Owner: koaning
- License: mit
- Created: 2021-10-31T16:05:10.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-26T19:32:31.000Z (5 months ago)
- Last Synced: 2024-04-26T20:42:42.599Z (5 months ago)
- Language: Python
- Homepage: https://koaning.github.io/embetter/
- Size: 6.69 MB
- Stars: 426
- Watchers: 7
- Forks: 14
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# embetter
> "Just a bunch of useful embeddings 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](https://www.youtube.com/watch?v=gDk7_f3ovIk). It's also meant to play nice with [bulk](https://github.com/koaning/bulk) and [scikit-partial](https://github.com/koaning/scikit-partial) but it can also be used together with your favorite ANN solution like [lancedb](https://lancedb.github.io/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[gensim]"
python -m pip install "embetter[bpemb]"
python -m pip install "embetter[vision]"
python -m pip install "embetter[all]"
```
## API Design
This is what's being implemented now.
```python
# 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, BytePairEncoder, spaCyEncoder, GensimEncoder
# 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
```python
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:
```python
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](https://scikit-learn.org/0.15/modules/scaling_strategies.html#incremental-learning)
in scikit-learn. That means
you can leverage [scikit-partial](https://github.com/koaning/scikit-partial)
to build pipelines that can handle out-of-core datasets.