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PyYel

PyYel is a personnal library that aims at helping the deployement of strong data science tools, from data handling to deep learning.

Quick start

  1. Install the library.
your_path> pip install PyYel
  1. Import the library into you code.
import pyl
  1. Import the relevant features.
from pyl.models.LLM import LLMDecodingPhi, LLMEncodingBARTLargeMNLI
from pyl.models.CNN import CNNClassificationResNet

Content

Data

A collection of features to manipulate the data. Can be used to implement pipelines, preprocessing, data augmentation...

  • Augmentations: a compilation of classes featuring methods to augment a datapoint of various type.

    • ImageAugmentation : features a handfull of functions that can augment any type of data, as well as its labels.
    • TODO
  • Reduction: acompilation of classes featuring methods to reduce datapoint of various type.

    • TODO/TO-REWORK
  • Utils: a collection of powerful tools that permit an easy manipulation of the datapoints.

    • TODO/TO-REWORK

Models

The neural networks implementations. These are grouped by types and tasks.

  • CNN (Convolutional Neural Networks)
Source model PyYel model Task Status
ResNet CNNCLassificationResNet Classification Implemented
FasterRCNN CNNDetectionFasterRCNN Detection Implemented
SSD CNNDetectionSSD Detection Implemented
RetinaNet CNNDetectionRetinaNet Detection TODO
/ CNNKeypoint Keypoint detection TODO
FCN CNNSegmentationFCN Segmentation Implemented/TODO
DeeplabV3 CNNSegmentationDeeplabV3 Segmentation Implemented/TODO

Note: Traditionnal computer vision networks. Features a model builder to design custom small-sized networks.

  • FCN (Fully Connected Networks)
Source model PyYel model Task Status
/ FCNBuilder / TODO
Note: Dense models. Features a model builder to design custom small-sized networks.
  • LLM (Large Language Models)
Source model PyYel model Task Status
Mistral7B v0.1 LLMDecodingMistral7B Decoding: text-to-text generation Implemented
OPT 125M LLMDecodingOPT125m Decoding: text-to-text generation Implemented
Phi 3.5 Mini Instruct LLMDecodingPhi Decoding: text-to-text generation Implemented
Phi 3.5 MoE LLMDecodingPhiMoE Decoding: text-to-text generation Implemented/TODO
BART Large LLMEncodingBARTLargeMNLI Encoding: zero-shoot classification Implemented
DeBERTaV3 Base LLMEncodingDeBERTaV3Base Encoding: zero-shoot classification Implemented
DeBERTaV3 Base LLMEncodingDeBERTaV3BaseMNLI Encoding: zero-shoot classification Implemented
DeBERTaV3 Large LLMEncodingDeBERTaV3Large Encoding: zero-shoot classification Implemented

Note: NLP transformers.

  • LVM (Large Vision Models)
Source model PyYel model Task Status
ViT LVMVisionTransformerClassification Classification TODO

Note: Computer vision transformers.

Utils

A collection of higher-level tools, that simplifies the manipulation of the library

TODO/TO-REWORK

Notes

TODO

Release files for PyYel 0.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for PyYel 0.1.0
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pyyel-0.1.0.tar.gz 57.5 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for PyYel 0.1.0
File Interpreter ABI Platform
PyYel-0.1.0-py3-none-any.whl Python 3 none any Details

Total release size: 154.1 kB

Release files / pyyel-0.1.0.tar.gz

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Release files / PyYel-0.1.0-py3-none-any.whl

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