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embetter-0.3.8
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مشاهده بیشتر
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مشاهده بیشتر
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشترتوضیحات
Just a bunch of useful embeddings to get started quickly.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | embetter-0.3.8 |
| نام | embetter |
| نسخه کتابخانه | 0.3.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Vincent D. Warmerdam |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://koaning.github.io/embetter/ |
| آدرس اینترنتی | https://pypi.org/project/embetter/ |
| مجوز | - |
<img src="https://raw.githubusercontent.com/koaning/embetter/main/docs/images/icon.png" width="125" height="125" align="right" />
# embetter
> "Just a bunch of useful embeddings to get started quickly."
<br>
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 a also meant to play nice with [bulk](https://github.com/koaning/bulk) and [scikit-partial](https://github.com/koaning/scikit-partial).
## 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[sentence-tfm]"
python -m pip install "embetter[spacy]"
python -m pip install "embetter[sense2vec]"
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, Sense2VecEncoder, BytePairEncoder, spaCyEncoder
# Representations from multi-modal models
from embetter.multi import ClipEncoder
# Finetuning components
from embetter.finetune import ForwardFinetuner
# 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, TimmEncoder
# 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 MobileNetv2 via TorchImageModels (timm).
image_emb_pipeline = make_pipeline(
ColumnGrabber("img_path"),
ImageLoader(convert="RGB"),
TimmEncoder("mobilenetv2_120d")
)
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.
نیازمندی
| مقدار | نام |
|---|---|
| >=1.0.0 | scikit-learn |
| >=1.0.0 | pandas |
| >=1.0.0 | scikit-learn |
| >=1.0.0 | pandas |
| >=2.2.2 | sentence-transformers |
| ==2.0.0 | sense2vec |
| >=0.3.3 | bpemb |
| >=0.6.7 | timm |
| >=0.25.0 | openai |
| >=0.3.3 | bpemb |
| >=1.0.0 | scikit-learn |
| >=1.0.0 | pandas |
| >=1.0.0 | scikit-learn |
| >=1.0.0 | pandas |
| >=2.2.2 | sentence-transformers |
| ==2.0.0 | sense2vec |
| >=0.3.3 | bpemb |
| >=0.6.7 | timm |
| >=0.25.0 | openai |
| ==1.1 | mkdocs |
| ==4.6.3 | mkdocs-material |
| ==0.8.0 | mkdocstrings |
| ==0.1.2 | mktestdocs |
| >=1.5.0 | interrogate |
| >=3.6.0 | flake8 |
| >=4.0.2 | pytest |
| >=19.3b0 | black |
| >=2.2.0 | pre-commit |
| ==2.8.0 | datasets |
| ==3.4.3 | matplotlib |
| >=0.25.0 | openai |
| >=1.0.0 | scikit-learn |
| >=1.0.0 | pandas |
| >=1.12.0 | torch |
| >=1.0.0 | scikit-learn |
| >=1.0.0 | pandas |
| ==2.0.0 | sense2vec |
| >=1.0.0 | scikit-learn |
| >=1.0.0 | pandas |
| >=2.2.2 | sentence-transformers |
| >=1.0.0 | scikit-learn |
| >=1.0.0 | pandas |
| >=3.5.0 | spacy |
| >=1.0.0 | scikit-learn |
| >=1.0.0 | pandas |
| >=2.2.2 | sentence-transformers |
| ==2.0.0 | sense2vec |
| >=0.3.3 | bpemb |
| >=1.0.0 | scikit-learn |
| >=1.0.0 | pandas |
| >=0.6.7 | timm |
| >=1.0.0 | scikit-learn |
| >=1.0.0 | pandas |
نحوه نصب
نصب پکیج whl embetter-0.3.8:
pip install embetter-0.3.8.whl
نصب پکیج tar.gz embetter-0.3.8:
pip install embetter-0.3.8.tar.gz