word2vec
Ranked 8 of 8 embedding models by recommenders
Recommended as a beautiful piece of science that dominated word embeddings for about five years, though one critic says the criticism of it is valid.
Recommended as a beautiful piece of science that dominated word embeddings for about five years, though one critic says the criticism of it is valid.
7 people gave an opinion
- recommend it
- 5
- criticize it
- 2
- approval share
- 71%
Recommended in 3 of 13 months
- November 2025: recommended
- December 2025: not recommended
- January 2026: recommended
- February 2026: not recommended
- March 2026: not recommended
- April 2026: not recommended
- May 2026: not recommended
- June 2026: not recommended
- July 2026: not recommended
- August 2026: not recommended
- September 2026: not recommended
- October 2026: recommended
- November 2026: not recommended (month in progress)
Why it doesn’t carry the badge
- 5 people recommend itNeeds 10 or more, not met.
- 2.5 recommenders for every criticNeeds 2 to 1 or better, met.
- Recommended in 3 of 13 monthsNeeds 4 or more, not met.
The same three rules apply to every product, and the badge can’t be bought. How the badge works
What people say about it
The reasons people give, with the number of comments that give each one. Open a reason to read what they said.
Why people recommend it
Each mark is one comment
Dominated the field for years
“was the thing that was needed for compute at the time and completely dominated the field for five years or so”
Comment from 29 Oct 2026 in “Tomáš Mikolov awarded the Medal of Merit by the President of the Czech Republic” Learns word meanings without being told
“this completely misses how crazy word2vec is. The model doesn't get told anything about word meanings”
Comment from 20 Jan 2026 in “King – man and woman is queen; but why?” Beautiful piece of science
“word2vec is a beautiful peice of science”
Comment from 29 Oct 2026 in “Tomáš Mikolov awarded the Medal of Merit by the President of the Czech Republic”
Other embedding models people recommend
The four most recommended other embedding models, with the same counts for each, so you can compare like for like. The tick on each bar marks the 2 to 1 line the badge needs.
| Product | Recommend | Criticize | Approval | Months | Owned | Badge |
|---|---|---|---|---|---|---|
| EmbeddingGemma | 27 | 4 | 87% | 3 | — | — |
| Ternlight | 17 | 3 | 85% | 1 | — | — |
| CLIP by OpenAI | 10 | 5 | 67% | 6 | — | Approved |
| Gemini Embedding 2 | 9 | 3 | 75% | 3 | — | — |
| word2vecThis page | 5 | 2 | 71% | 3 | — | — |
What people said
6 of the 7 comments that mention it, newest first. Each links to the original comment.
“was the thing that was needed for compute at the time and completely dominated the field for five years or so”
“word2vec is a beautiful peice of science”
“I spent way too much time trying to automate moderation”
“this completely misses how crazy word2vec is. The model doesn't get told anything about word meanings”
“while the criticism is valid for word2vec”
“That never exactly worked for word2vec either.”
Counts come from 1,795,640 Hacker News comments, November 2025 to November 2026. How we count. Report a mistake.