Multimodal Art Projection

Model · Audio classification

MERT-v1-330M

Multimodal Art Projection

The development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M, trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with MLM paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with BYOL paradigm. Here is a table for quick model pick-up: The m-a-p models…

Model · Audio classification

MERT-v1-95M

Multimodal Art Projection

The development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M, trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with MLM paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with BYOL paradigm. Here is a table for quick model pick-up: The m-a-p models…

Dataset · Text classification

FineFineWeb

Multimodal Art Projection

The data construction workflow can be summarized as follows: 1. Deduplicate: The FineWeb dataset is deduplicated using exact deduplication and MinHash techniques to remove redundant data. 2. URL Labeling: Root URLs from FineWeb are counted, and the top 1 million URLs are labeled using GPT-4. This step generates DoI (Domain-of-Interest) Coarse-Grained URLs and DoNI (Domain-of-Non-Interest) Coarse-Grained URLs as seed data sources. 3. Coarse Recall: a. Based on the labeled root URLs, data is sampled for each domain. b. The sampled data is labeled using Qwen2-7B-Instruct, producing 500K DoI Positive Data and 500K DoI Negative Data (note that for N>1 iterations, each 500K samples are composed…

Dataset

PIN-200M

Multimodal Art Projection

A mini version of "PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents" This dataset contains around 200M samples in PIN format, with around 312 TB storage. News [ 2025.09.22 ]!NEW! We have completed the final version of the PIN-200M dataset and conducted some simple statistics on it. [ 2024.12.06 ]!NEW! We have updated the quality signals, enabling a swift assessment of whether a sample meets the required specifications based on our quality indicators. Further detailed descriptions will be provided in the forthcoming formal publication. (Aside from the Chinese-Markdown subset, there are unresolved issues that are currently being addressed.) This dataset…