SelSum
nlp-recipes
| SelSum | nlp-recipes | |
|---|---|---|
| 1 | 5 | |
| 44 | 6,020 | |
| - | - | |
| 0.0 | 0.0 | |
| over 2 years ago | over 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
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.
SelSum
-
[D] How to best extract product benefits/problems from customer reviews using NLP?
https://github.com/abrazinskas/SelSum - for abstractive summarization into vertdict/pros/cons
nlp-recipes
-
Show HN: I turned my microeconomics textbook into a chatbot with GPT-3
https://github.com/topics/automatic-summarization
Microsoft/nlp-recipes lists current NLP tasks that would be helpful for a docs bot: https://github.com/microsoft/nlp-recipes#content
-
Show HN: DocsGPT, open-source documentation assistant, fully aware of libraries
https://github.com/topics/automatic-summarization
Though now archived,
> Microsoft/nlp-recipes lists current NLP tasks that would be helpful for a docs bot: https://github.com/microsoft/nlp-recipes#content
NLP Tasks: Text Classification, Named Entity Recognition, Text Summarization, Entailment, Question Answering, Sentence Similarity, Embeddings, Sentiment Analysis, Model Explainability, and Auto-Annotatiom
- ✨ 5 Free Resources for Learning Natural Language Processing with Python 🚀
-
Is there any utility software/bot that produces descriptor tags for a Reddit image post using the comments?
I found this (https://github.com/microsoft/nlp-recipes) resource and it has a list of pre-built or easily customizable NLP models that I'm going to try out.
-
Building a Aspect based sentiment classification
There is an NLP recipe from Microsoft on ABSA. Have you seen this? https://github.com/microsoft/nlp-recipes/blob/master/examples/sentiment_analysis/absa/absa.ipynb
What are some alternatives?
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models
PyABSA - Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;
OpenPrompt - An Open-Source Framework for Prompt-Learning.
rasa - 💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
deepsegment - A sentence segmenter that actually works!
pymarl2 - Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)
Parrot_Paraphraser - A practical and feature-rich paraphrasing framework to augment human intents in text form to build robust NLU models for conversational engines. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.
ccg2lambda - Provide Semantic Parsing solutions and Natural Language Inferences for multiple languages following the idea of the syntax-semantics interface.
link-grammar - The CMU Link Grammar natural language parser
forte - Forte is a flexible and powerful ML workflow builder. This is part of the CASL project: http://casl-project.ai/
autonlp - 🤗 AutoNLP: train state-of-the-art natural language processing models and deploy them in a scalable environment automatically