huggingpics VS converse

Compare huggingpics vs converse and see what are their differences.

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huggingpics converse
1 6
249 176
- 0.0%
0.0 0.0
over 1 year ago 11 months ago
Jupyter Notebook Jupyter Notebook
- Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

huggingpics

Posts with mentions or reviews of huggingpics. We have used some of these posts to build our list of alternatives and similar projects.

converse

Posts with mentions or reviews of converse. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing huggingpics and converse you can also consider the following projects:

ganbert-pytorch - Enhancing the BERT training with Semi-supervised Generative Adversarial Networks in Pytorch/HuggingFace

FinBERT - A Pretrained BERT Model for Financial Communications. https://arxiv.org/abs/2006.08097

multi-label-sentiment-classifier - How to build a multi-label sentiment classifiers with Tez and PyTorch

soxan - Wav2Vec for speech recognition, classification, and audio classification

Speech-Emotion-Classification-by-utilizing-a-Convolutional-Neural-Network - Enhancing Speech Emotion Classification with CNNs: This project seeks to overcome the limitations of traditional approaches and improve the accuracy of emotion recognition. CNNs automatically extract features from speech signals, capturing complex patterns and nuances, leading to enhanced performance compared to traditional methods.

TopMost - A Topic Modeling System Toolkit

PLOD-AbbreviationDetection - This repository contains the PLOD Dataset for Abbreviation Detection released with our LREC 2022 publication

browser-ml-inference - Edge Inference in Browser with Transformer NLP model

nlphose - Enables creation of complex NLP pipelines in seconds, for processing static files or streaming text, using a set of simple command line tools. Perform multiple operation on text like NER, Sentiment Analysis, Chunking, Language Identification, Q&A, 0-shot Classification and more by executing a single command in the terminal. Can be used as a low code or no code Natural Language Processing solution. Also works with Kubernetes and PySpark !