An easy-to-use Discord bot written in Python

AniSearch AniSearch is an easy-to-use Discord bot written in Python that allows you to search for anime, manga, characters, staff, studios and much more directly in Discord and displays the results as paginated embeds! You can also: Search for a random anime or manga of a specific genre. View the opening and ending themes of an anime. View another user’s profile with anime and manga stats from AniList, MyAnimeList or Kitsu. Search for the anime or the source of an […]

Read more

A repository of PyBullet utility functions for robotic motion planning

pybullet-planning (ss-pybullet) A repository of PyBullet utility functions for robotic motion planning, manipulation planning, and task and motion planning (TAMP). This repository was originally developed for the PDDLStream (previously named STRIPStream) approach to TAMP. With the help of Yijiang Huang, a stable and documented fork of ss-pybullet named pybullet_planning is available through PyPI. However, new features will continue to be introduced first through ss-pybullet. Installation Install PyBullet on OS X or Linux using: $ pip install numpy pybullet $ git […]

Read more

A Guide to Feature Engineering in NLP

Overview Feature engineering in NLP is understanding the context of the text. In this blog, we will look at some of the common feature engineering in NLP. We will compare the results of a classification task with and without doing feature engineering   Table of Content Introduction NLP task overview List of features with code Implementation Results comparison with and without doing feature engineering Conclusion Introduction   “If 80 percent of our work is data preparation, then ensuring data quality […]

Read more

Issue #126 – Learning Feature Weights for Denoising Parallel Corpora

15 Apr21 Issue #126 – Learning Feature Weights for Denoising Parallel Corpora Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction Large web-crawled parallel corpora constitute a very useful source of data to improve neural machine translation (NMT) engines. However, their effectiveness is reduced by the large amount of noise they usually contain. As early as in issue #2 of this series, we pointed out that NMT is particularly sensitive to noise in the training data. In issue […]

Read more

Pixel Art Reverse Image Search for OpenGameArt

PixelArtSearch Pixel art search engine for opengameart. ## What data do you need on each image to create a reverse image search? I used [VGG16 feature extraction](https://towardsdatascience.com/extract-features-visualize-filters-and-feature-maps-in-vgg16-and-vgg19-cnn-models-d2da6333edd0) in [my script for this](https://github.com/emnh/PixelArtSearch/blob/master/scripts/featureVectors.py). See the article for more information, but in essence it’s 4096 32-bit floating point numbers for each image, which describe various features of the image, say for instance in a very simplified way how many stripes or squares it has or how green it is. But these features […]

Read more

Transfer learning for NLP models by annotating your textual data

Label Studio for Transformers Transfer learning for NLP models by annotating your textual data without any additional coding. This package provides a ready-to-use container that links together: Quick Usage Install Label Studio and other dependencies pip install -r requirements.txt Create ML backend with BERT classifier label-studio-ml init my-ml-backend –script models/bert_classifier.py cp models/utils.py my-ml-backend/utils.py Create ML backend with BERT named entity recognizer label-studio-ml init my-ml-backend –script models/ner.py cp models/utils.py my-ml-backend/utils.py Start ML backend at http://localhost:9090 label-studio-ml start my-ml-backend Start Label Studio […]

Read more

OpenAI DALLE model and generating images from given texts

DALLE-reproduction This repository is for sharing pre-trained OpenAI DALLE model and generating images from given texts. All models are trained by lucidrains/DALLE-pytorch + VQGAN (Taming transformer) with different training code and BPE model. The notebook includes Text to image generation Pre-trained CLIP reranking 3. Generate rest of image based on the given cropped image Usage Install requirements $ pip install -r requirements Install DeepSpeed Follow the instruction here and install DeepSpeed Models Download models below and save them in pretrained […]

Read more

A Legate library that aims to provide a distributed and accelerated drop-in replacement

Legate NumPy Legate NumPy is a Legate library that aims to provide a distributed and accelerated drop-in replacement for the NumPy API on top of the Legion runtime. Using Legate NumPy you do things like run the final example of the Python CFD course completely unmodified on 2048 A100 GPUs in a DGX SuperPOD and achieve good weak scaling. Legate NumPy works best for programs that have very large arrays of data that cannot fit in the memory of a […]

Read more

A subfield of machine learning focused on developing representations of images

Curator Self-supervised learning is a subfield of machine learning focused on developing representations of images without any labels, which is useful for reverse image searching, categorization and filtering of images, especially so when it would be infeasible to have a human manually inspect each individual image. It also has downstream benefits for classification tasks. For instance, training SSL on 100% of your data and finetuning the encoder on the 5% of data that has been labeled significantly outperforms training a […]

Read more

Implementation of different architectures for emotion recognition in conversations

Emotion Recognition in Conversations Unlike other emotion detection models, these techniques consider the party-states and inter-party dependencies for modeling conversational context relevant to emotion recognition. The primary purpose of all these techniques are to pretrain an emotion detection model for empathetic dialogue generation. Interaction among different controlling variables during a dyadic conversation between persons X and Y. Grey and white circles represent hidden and observed variables, respectively. P represents personality, U represents utterance, S represents interlocutor state, I represents interlocutor […]

Read more
1 653 654 655 656 657 912