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ELMo — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/ELMo
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-05T23:05:55.012Z
SHA-256 of the archived text: e6c51f52c2c4ec45f10ef7b8c15498c166363af4215a56de0dc058f543405f57
Stable URL of this page: https://metraipolis.com/documento/dc_cd423ff92b2a

How to cite

ELMo — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/ELMo (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_cd423ff92b2a (Metraipolis archive dc_cd423ff92b2a, sha256 e6c51f52c2c4ec45f10ef7b8c15498c166363af4215a56de0dc058f543405f57)

BibTeX

@misc{dccd423ff92b2a,
  title = {ELMo — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/ELMo},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_cd423ff92b2a; sha256 e6c51f52c2c4ec45f10ef7b8c15498c166363af4215a56de0dc058f543405f57},
  year = {2026}
}

Archived text

Reproduced under CC-BY-SA-4.0. Attribution: Wikipedia (en), https://en.wikipedia.org/wiki/ELMo. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

ELMo (embeddings from language model) is a word embedding method for representing a sequence of words as a corresponding sequence of vectors. It was created by researchers at the Allen Institute for Artificial Intelligence, and University of Washington and first released in February 2018. It is a bidirectional LSTM which takes character-level as inputs and produces word-level embeddings, trained on a corpus of about 30 million sentences and 1 billion words. The architecture of ELMo accomplishes a contextual understanding of tokens. Deep contextualized word representation is useful for many natural language processing tasks, such as coreference resolution and polysemy resolution. ELMo was historically important as a pioneer of self-supervised generative pretraining followed by fine-tuning, where a large model is trained to reproduce a large corpus, then the large model is augmented with additional task-specific weights and fine-tuned on supervised task data. It was an instrumental step in the evolution towards transformer-based language modelling.

Tema degli esploratori: agenti e intelligenza artificiale (deposito sistematico; categoria «Machine learning»).
Fonte: Wikipedia in inglese, voce «ELMo» — https://en.wikipedia.org/wiki/ELMo
Wikidata Q100952135 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q100952135
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/ELMo?action=history).
Licenza: Creative Commons Attribuzione-Condividi allo stesso modo 4.0 (CC BY-SA 4.0) — https://creativecommons.org/licenses/by-sa/4.0/. Il riuso richiede attribuzione e stessa licenza.
Estratto dell'introduzione non modificato nel contenuto; revisione 1354732604, consultata il 2026-10-05T23:01:01.690Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: d2216bbef43d070ac805afc9f16ecf3c0dc00607d742e7481943569d20499108