# jwieting/iclr2016

<https://repo.masters-masters.com/en/repos/jwieting/iclr2016>

[Home](https://repo.masters-masters.com/en) › [jwieting](https://repo.masters-masters.com/en/owners/jwieting) › [iclr2016](https://repo.masters-masters.com/en/repos/jwieting/iclr2016)

jwieting/iclr2016 has 193 stars on GitHub. Its RepoMasters card: Prototype tier. Python code for training all models in the ICLR paper, "Towards Universal…

| Field | Value |
| --- | --- |
| Owner | [jwieting](https://repo.masters-masters.com/en/owners/jwieting) |
| Stars | 193 |
| Forks | 51 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | None |
| Created | 2016 |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [jwieting/iclr2016](https://github.com/jwieting/iclr2016) |

## Repository

Python code for training all models in the ICLR paper, "Towards Universal Paraphrastic Sentence Embeddings". These models achieve strong performance on semantic similarity tasks without any training or tuning on the training data for those tasks. They also can produce features that are at least as…

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.