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title: README
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<h1 align="center">Musixmatch</h1>
<p align="center">
Musixmatch is the World’s Leading Music Data Company. <br/ >
Our mission is to provide data, tools and services that allows the experience of music to be enriched across the whole world.
</p>
<p align="center">
<a href="https://www.musixmatch.com">Website</a> ·
<a href="https://about.musixmatch.com">About</a> ·
<a href="https://developer.musixmatch.com">Developer API</a> ·
<a href="https://github.com/musixmatchresearch">GitHub</a> ·
<a href="https://about.musixmatch.com/careers">Careers</a>
</p>
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## AI research
Musixmatch best-known open contribution is **UmBERTo**, a RoBERTa-based Italian language
model trained with SentencePiece tokenization and Whole Word Masking on 70GB of
deduplicated Italian text from the OSCAR corpus (~11B words). It set a new bar
for Italian NLP and remains a widely used baseline.
| Model | Task | Score |
|---|---|---|
| UmBERTo (CommonCrawl, cased) | NER — WikiNER-ITA | **92.5 F1** |
| UmBERTo (CommonCrawl, cased) | NER — ICAB-EvalITA07 | **87.6 F1** |
| UmBERTo (CommonCrawl, cased) | POS — UD Italian-ISDT | **98.9 acc** |
→ [`Musixmatch/umberto-commoncrawl-cased-v1`](https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1)
· [`Musixmatch/umberto-wikipedia-uncased-v1`](https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1)
· [source on GitHub](https://github.com/musixmatchresearch/umberto)
We also run our training and inference on AWS — read the
[Musixmatch × Hugging Face × AWS case study](https://huggingface.co/case-studies/aws/musixmatch).
<p align="center">
<sub>© Musixmatch · Bologna, Italy</sub>
</p> |