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`flair-lms`

This repository is part of the NLP research with flair, a state-of-the-art NLP framework from Zalando Research.

This repository will include various language models (forward and backward) that can be used with flair. It will be updated frequently. So please star or watch this repository 😅

Changelog

January 2020: Move repository to the new FlairNLP group on GitHub.

September 2019: New Multilingual Flair Embeddings trained on JW300 corpus are released.

September 2019: All Flair Embeddings that are now officially available in Flair >= 0.4.3 are listed.

Parameters

All Flair Embeddings are trained with a hidden_size of 2048 and nlayers of 1.

Flair Embeddings

Language model	# Tokens	Forward ppl.	Backward ppl.	Flair Embeddings alias
Arabic	736M	3.39	3.45	`ar-forward` and `ar-backward`
Bulgarian (fast)	66M	2.48	2.51	`bg-forward-fast` and `bg-backward-fast`
Bulgarian	111M	2.46	2.47	`bg-forward` and `bg-backward`
Czech (v0)	778M	3.44	3.48	`cs-v0-forward` and `cs-v0-backward`
Czech	442M	2.89	2.90	`cs-forward` and `cs-backward`
Danish	325M	2.62	2.68	`da-forward` and `da-backward`
Basque (v0)	37M	2.56	2.58	`eu-v0-forward` and `eu-v0-backward`
Basque (v1)	37M	2.64	2.31	`eu-v1-forward` and `eu-v1-backward`
Basque	57M	2.90	2.83	`eu-forward` and `eu-backward`
Persian	146M	3.68	3.66	`fa-forward` and `fa-backward`
Finnish	427M	2.63	2.65	`fi-forward` and `fi-backward`
Hebrew	502M	3.84	3.87	`he-forward` and `he-backward`
Hindi	28M	2.87	2.86	`hi-forward` and `hi-backward`
Croatian	625M	3.13	3.20	`hr-forward` and `hr-backward`
Indonesian	174M	2.80	2.74	`id-forward` and `id-backward`
Italian	1,5B	2.62	2.63	`it-forward` and `it-backward`
Dutch (v0)	897M	2.78	2.77	`nl-v0-forward` and `nl-v0-backward`
Dutch	1,2B	2.43	2.55	`nl-forward` and `nl-backward`
Norwegian	156M	3.01	3.01	`no-forward` and `no-backward`
Polish	1,4B	2.95	2.84	`pl-opus-forward` and `pl-opus-backward`
Slovenian (v0)	314M	3.28	3.34	`sl-v0-forward` and `sl-v0-backward`
Slovenian	419M	2.88	2.91	`sl-forward` and `sl-backward`
Swedish (v0)	545M	2.29	2.27	`sv-v0-forward` and `sv-v0-backward`
Swedish	671M	6.82 (?)	2.25	`sv-forward` and `sv-backward`
Tamil	18M	2.23	4509 (!)	`ta-forward` and `ta-backward`

Multilingual Flair Embeddings

Multilingual Flair Embeddings were trained on the recently released JW300 corpus. Thanks to half precision support in Flair, both forward and backward Embeddings were trained for 5 epochs for over 10 days. The training corpus has 2,025,826,977 token.

Language model	# Tokens	Forward ppl.	Backward ppl.	Flair Embeddings alias
JW300	2B	3.25	3.37	`multi-forward` and `multi-backward`

It can be loaded with:

from flair.embeddings import FlairEmbeddings

jw_forward = FlairEmbeddings("multi-forward")
jw_backward = FlairEmbeddings("multi-backward")

A detailed evaluation on various PoS tagging tasks can be found in this repository.

We would like to thank Željko Agić for providing us access to the corpus (before it was officially released)!