keytotext
predict-subreddit
keytotext | predict-subreddit | |
---|---|---|
5 | 8 | |
436 | 31 | |
- | - | |
3.1 | 0.9 | |
9 months ago | about 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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.
keytotext
- [Machine Learning] [P] Implémentation de la génération de texte à partir de mots clés Python Module
-
[P] Implementation of text generation from keywords Python Module
I am trying to find a module with a key to text generator using NLP models, I have been using "keytotext" (https://github.com/gagan3012/keytotext) which has been working really well up until now but today it seems like the models have been taken down from https://huggingface.co/models.
-
Library that takes a pool of words and spits out sentences with only those words?
This library can generate sentences based on the given keywords using T5. I feel like this is probably close to what you are looking for.
- Keytotext Convert Keywords to Large Texts
-
Keytotext
Hello, Presenting Keytotext: Keytotext is an NLP model that can convert keywords to sentences and larger texts. It is built using the T5 model. Keytotext has a PyPI installation and on-demand inference API too. It also features a UI built using streamlit and a GPU-enabled colab notebook for easy usage! Please do check it out on GitHub: https://github.com/gagan3012/keytotext Please to star 📷 if you liked the work!
predict-subreddit
-
Moderating posts that don't belong to a subreddit using ML model
You can try the demo here; there is also an API endpoint for it. The code for the project is open-source on the GitHub repo.
-
Interesting AI projects in hugging face
predict-subreddit
-
[P] Made an NLP model that predicts subreddit based on the title of a post (link in comments)
here you go https://github.com/daspartho/predict-subreddit
-
Made an NLP model that predicts subreddit based on the title of a post
You could look at the model notebook in the github repo, it has steps to create the model using the HuggingFace library.
-
Made an NLP model that predicts subreddit based on the title of a post (link in comments)
I've put the details for the project in the GitHub Readme. The repo contains notebooks with steps to create the dataset and the model.
What are some alternatives?
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
is-it-huggable - An image classifier to classify things as huggable or not.
adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.
BERT-for-Mobile - Compares the DistilBERT and MobileBERT architectures for mobile deployments.
mt5-M2M-comparison - Comparing M2M and mT5 on a rare language pairs, blog post: https://medium.com/@abdessalemboukil/comparing-facebooks-m2m-to-mt5-in-low-resources-translation-english-yoruba-ef56624d2b75
multi-label-sentiment-classifier - How to build a multi-label sentiment classifiers with Tez and PyTorch
aws-lambda-docker-serverless-inference - Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.
Machine-Learning-Cyrillic-Classifier - This is a web app where you can draw a letter in the russian alphabet and the ML algorithm will predict the letter that you drew.
anime-or-not - NLP model that determines whether a plot is anime enough
mfp-wrapped - Data app to provide analytics for myfitnesspal users: a calorie counter and food journal
SagemakerHuggingfaceDashboard - This is a solution that demonstrates how to train and deploy a pre-trained Huggingface model on AWS SageMaker and publish an AWS QuickSight Dashboard that visualizes the model performance over the validation dataset and Exploratory Data Analysis for the pre-processed training dataset.