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Tags: Embeddings

1.jina-ai / clip-as-service

🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP

bert
sentence-encoding
deep-learning
clip-model
clip-as-service
bert-as-service
+9 more

Python Other license13K total2.1K forks

+3 stars

from 12,831 · score 1.39

2.flairNLP / flair

A very simple framework for state-of-the-art Natural Language Processing (NLP)

pytorch
nlp
named-entity-recognition
sequence-labeling
semantic-role-labeling
word-embeddings
+2 more

Python Other license14K total2.1K forks

+1 stars

from 14,396 · score 0.69

Order: Attention Score

Most net stars gained in the window first. The score only puts this gain on a log scale, so the order is the order of the gain.

How the ranking works

Ranked by stars gained in the window. The score is the natural log of one plus that gain, so a repository's rank is exactly its star gain's rank.

score = log1p(net stars gained in the trailing T complete UTC days)

A repository is ranked only when its Star History covers every day of the window and its latest Observation is not missing.

The longest window is 180 days. Star History from the Repository Host goes back at most 180 days before the first daily record that AwesomeRepos made for the repository. A longer window needs more of these daily records first. A repository younger than the window does not rank in it.

The filters select from the same eligible repositories. A repository matches the topics when it has any of them. Total stars is the count now. Last push and creation on the Repository Host count back from the start of today (UTC). The counts beside the languages, licenses, and topics include all ranked repositories in the window. A Tag holds the repositories that have it. See Tags.

A new repository enters the index when it gains stars faster than the published admission threshold, whatever its size. A repository stays in the index after it enters. Thus an archived repository can still rank.

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