PromCSE VS inltk

Compare PromCSE vs inltk and see what are their differences.

PromCSE

Code for "Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning (EMNLP 2022)" (by YJiangcm)

inltk

Natural Language Toolkit for Indic Languages aims to provide out of the box support for various NLP tasks that an application developer might need (by goru001)
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PromCSE inltk
1 1
131 811
- -
5.9 0.0
6 months ago 4 months ago
Python Python
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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PromCSE

Posts with mentions or reviews of PromCSE. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-05-03.
  • State of the Art in Sentence Embeddings
    2 projects | /r/LanguageTechnology | 3 May 2022
    To answer your question about sentence embedding SOTA, it is not s-Bert and hasn't been for a while. SimCSE officially takes the crown since it's been presented at a conference, though according to paperswithcode's benchmark leaderboard there are other papers on arxiv that report higher performance on STS and similar tasks such as DCPCSE. Having tried both of these for my use case I found SimCSE to be better but YMMV.

inltk

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

What are some alternatives?

When comparing PromCSE and inltk you can also consider the following projects:

nlu - 1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.

allennlp - An open-source NLP research library, built on PyTorch.

DiffCSE - Code for the NAACL 2022 long paper "DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings"

SimCSE - [EMNLP 2021] SimCSE: Simple Contrastive Learning of Sentence Embeddings https://arxiv.org/abs/2104.08821

BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.

smaller-labse - Applying "Load What You Need: Smaller Versions of Multilingual BERT" to LaBSE

clip-as-service - 🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP

KitanaQA - KitanaQA: Adversarial training and data augmentation for neural question-answering models

ModelNet40-C - Repo for "Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions" https://arxiv.org/abs/2201.12296

flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)