remove excess line breaks in DeepPavlov model cards

This commit is contained in:
Aleksei Lymar authored and Julien Chaumond committed 2020-03-06 17:19:35 -05:00
1 parent ed37f9fa4f
commit 73a0c25376
6 files changed
+19 -50

No files matched your search

@@ -5,17 +5,11 @@ language:
# rubert-base-cased-sentence
Sentence RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\)
is a representation-based sentence encoder for Russian. It is initialized with RuBERT and fine-tuned on SNLI\[1\]
google-translated to russian and on russian part of XNLI dev set\[2\]. Sentence representations are mean pooled
token embeddings in the same manner as in Sentence-BERT\[3\].
Sentence RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) is a representation‑based sentence encoder for Russian. It is initialized with RuBERT and fine‑tuned on SNLI\[1\] google-translated to russian and on russian part of XNLI dev set\[2\]. Sentence representations are mean pooled token embeddings in the same manner as in Sentence‑BERT\[3\].
\[1\]: S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. \(2015\) A large annotated corpus for learning
natural language inference. arXiv preprint [arXiv:1508.05326](https://arxiv.org/abs/1508.05326)
\[1\]: S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. \(2015\) A large annotated corpus for learning natural language inference. arXiv preprint [arXiv:1508.05326](https://arxiv.org/abs/1508.05326)
\[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations.
arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053)
\[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053)
\[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.
arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)
\[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)