Note: This model has been moved to linhd-postdata/alberti-bert-base-multilingual-cased

ALBERTI

ALBERTI is a set of two BERT-based multilingual model for poetry. One for verses and another one for stanzas. This model has been further trained with the PULPO corpus for verses using Flax , including training scripts.

This is part of the Flax/Jax Community Week , organised by HuggingFace and TPU usage sponsored by Google.

PULPO

PULPO, the Prodigious Unannotated Literary Poetry Corpus, is a set of multilingual corpora of verses and stanzas with over 95M words.

The following corpora has been downloaded using the Averell tool, developed by the POSTDATA team:

Spanish

• Disco v3
• Corpus of Spanish Golden-Age Sonnets
• Corpus general de poesía lírica castellana del Siglo de Oro
• Gongocorpus - source

English

• Eighteenth-Century Poetry Archive (ECPA)
• For better for verse

French

• Métrique en Ligne - source

Italian

• Biblioteca italiana - source

Czech

• Corpus of Czech Verse

Portuguese

• Stichotheque

Also, we obtained the following corpora from these sources:

Spanish

• Poesi.as - source

English

• A Gutenberg Poetry Corpus

Arabic

• Arabic Poetry dataset

Chinese

• THU Chinese Classical Poetry Corpus

Finnish

• SKVR

German

• TextGrid Poetry Corpus - source
• German Rhyme Corpus

Hungarian

• verskorpusz

Portuguese

• Poems in Portuguese

Russian

• 19 000 Russian poems

Team members

• Álvaro Pérez ( alvp )
• Javier de la Rosa ( versae )
• Aitor Díaz ( aitordiaz )
• Elena González-Blanco
• Salvador Ros ( salva )

Useful links

• Community Week timeline
• Community Week README
• Community Week thread
• Community Week channel
• Masked Language Modelling example scripts
• Model Repository

Acknowledgments

This project would not have been possible without the infrastructure and resources provided by HuggingFace and Google Cloud. Moreover, we want to thank POSTDATA Project (ERC-StG-679528) and the Computational Literary Studies Infrastructure (CLS INFRA No. 101004984) of the European Union's Horizon 2020 research and innovation programme for their support and time allowance.