LASER VS electra

Compare LASER vs electra and see what are their differences.

LASER

Language-Agnostic SEntence Representations (by facebookresearch)

electra

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators (by google-research)
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LASER electra
5 3
3,520 2,295
0.3% 0.6%
5.7 0.0
7 days ago about 1 month ago
Jupyter Notebook Python
GNU General Public License v3.0 or later 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.

LASER

Posts with mentions or reviews of LASER. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-04-07.

electra

Posts with mentions or reviews of electra. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-27.

What are some alternatives?

When comparing LASER and electra you can also consider the following projects:

MUSE - A library for Multilingual Unsupervised or Supervised word Embeddings

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

Arraymancer - A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends

stanford-tensorflow-tutorials - This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

flores - Facebook Low Resource (FLoRes) MT Benchmark

datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

iSarcasmEval - Datasets used for iSarcasmEval shared-task (Task 6 at SemEval 2022)