# Self-supervised learning

V-JEPA 2 is a cutting-edge video understanding model developed by the FAIR team under Meta. It extends the pre-training objectives of VJEPA and has industry-leading video understanding capabilities.

Midnight-12k is a foundational pathology model trained with self-supervised learning on a small dataset, achieving performance comparable to leading models

Japanese wav2vec2.0 Large model pre-trained on large-scale Japanese TV broadcast audio data

Japanese speech feature extraction model pre-trained on 62,215 hours of Japanese TV broadcast audio data

RNA foundation model pre-trained on non-coding RNA data using masked language modeling (MLM) objective

voc2vec is a foundational model specifically designed for non-linguistic human data, built upon the wav2vec 2.0 framework.

VideoMAEv2-Base is a self-supervised video feature extraction model that employs a dual masking mechanism pre-trained on the UnlabeldHybrid-1M dataset.

RNABERT is a pre-trained model based on non-coding RNA (ncRNA), employing Masked Language Modeling (MLM) and Structural Alignment Learning (SAL) objectives.

I-JEPA is a self-supervised learning method that predicts representations of other parts of an image from partial representations, without relying on manual data transformations or filling in pixel-level details.

I-JEPA is a self-supervised learning method that predicts representations of other parts of an image from partial representations, without relying on predefined manual data transformations or pixel-level detail filling.

I-JEPA is a self-supervised learning method that predicts representations of other parts of an image from partial representations, without relying on predefined manual data transformations or pixel-level detail filling.

I-JEPA is a self-supervised learning method that predicts representations of other parts of an image from partial representations, without relying on manual data transformations or filling in pixel-level details.

DINOv2 large is a large-scale visual feature extraction model based on self-supervised learning, capable of generating robust image feature representations.

Vision Transformer model trained with self-supervised DINOv2, specifically designed for encoding chest X-ray images

Ahma-7B is a 7-billion-parameter decoder-only Transformer model based on Meta Llama(v1) architecture, fully pretrained from scratch using Finnish language.

An image classification model based on the Vision Transformer (ViT), trained on 33 million histological sections using the DINO self-supervised learning method, suitable for pathological image classification tasks.

Phikon is a self-supervised learning model for histopathology based on iBOT training, primarily used for extracting features from histology image patches.

Audio representation model based on HuBERT architecture, pre-trained on the complete AudioSet dataset, suitable for general audio tasks

TwinBooster is a DeBERTa V3 base model fine-tuned on the PubChem bioassay corpus, combining Barlow Twins self-supervised learning method and gradient boosting techniques to enhance molecular property prediction.

Japanese HuBERT base model trained by rinna Co., Ltd., based on approximately 19,000 hours of Japanese speech corpus ReazonSpeech v1.

Data2Vec-Vision is a self-supervised learning model based on the BEiT architecture, fine-tuned on the ImageNet-1k dataset, suitable for image classification tasks.

Data2Vec-Vision is a self-supervised learning vision model based on the BEiT architecture, fine-tuned on the ImageNet-1k dataset, suitable for image classification tasks.

Data2Vec-Vision is a self-supervised learning model based on the BEiT architecture, pre-trained on the ImageNet-1k dataset, suitable for image classification tasks.

Data2Vec-Vision is a self-supervised learning model based on the BEiT architecture, pretrained on the ImageNet-1k dataset, suitable for image classification tasks.

This model is a large-scale speech recognition model based on the Wav2Vec2 architecture, pre-trained and fine-tuned on 10 minutes of data from Libri-Light and Librispeech, using self-training objectives, suitable for 16kHz sampled speech audio.

Data2Vec is a general self-supervised learning framework applicable to speech, vision, and language tasks. This large audio model is pre-trained and fine-tuned on 960 hours of LibriSpeech data, specifically optimized for automatic speech recognition tasks.

Data2Vec-Audio-Large is a large model pre-trained on 16kHz sampled speech audio using a self-supervised learning framework, suitable for tasks such as speech recognition.

A general self-supervised learning framework pre-trained on English language using the data2vec objective, handling different modality tasks through a unified approach

A Transformer model pre-trained on English corpus using ELECTRA-like objective functions, learning intrinsic representations of English language through self-supervised methods.

A self-supervised Vision Transformer model trained using the DINO method, suitable for image feature extraction

Pre-trained speech recognition model based on Common Voice Spanish data, trained on TPU using the Flax framework

The first lightweight ALBERT model for Persian language, trained based on Google ALBERT BASE 2.0 version

Large-scale speech pre-training model trained on the Spanish subset of the VoxPopuli corpus, suitable for Spanish speech recognition tasks

Funnel Transformer is an English text pre-training model based on self-supervised learning, adopting objectives similar to ELECTRA, achieving efficient language processing by filtering sequence redundancy.

XLM-RoBERTa is an extended version of a cross-lingual sentence encoder, trained on 2.5T of data across 100 languages, achieving excellent performance in multiple cross-lingual benchmarks.

HuBERT-Large is a self-supervised speech representation learning model fine-tuned on 960 hours of LibriSpeech data for automatic speech recognition tasks.

XLM-RoBERTa is a large-scale cross-lingual sentence encoder, trained on 2.5TB of data across 100 languages, achieving excellent performance in multiple cross-lingual benchmarks.

The CORe model is based on BioBERT and trained on medical data through clinical outcome pre-training objectives for predicting ICD9 diagnosis codes from admission records.

Polish text generation model based on GPT2 architecture, filling the gap in Polish NLP field, trained on multilingual Oscar corpus

Transformer-based end-to-end speech translation model, specifically designed for English-to-German speech translation