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These are 2 million 768-dimensional and 300-dimensional CBOW embeddings trained on the English colossal, cleaned common crawl (C4) corpus. They were trained with the corrected CBOW code from kōan: https://github.com/bloomberg/koan with intrinsic evaluation reported in: Ozan İrsoy, Adrian Benton, Karl Stratos. “Corrected CBOW Performs as well as Skip-gram”. The 2nd Workshop on Insights from Negative Results in NLP. 2021.
word embeddings, cbow, common crawl, nlp
word embeddings, cbow, common crawl, nlp
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 8 | |
| downloads | 1 |

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Downloads provided by UsageCounts