<pre style='margin:0'>
Ryan Schmidt (ryandesign) pushed a commit to branch master
in repository macports-ports.
</pre>
<p><a href="https://github.com/macports/macports-ports/commit/298cc6bcf5b17583367e2779535567f262dc5726">https://github.com/macports/macports-ports/commit/298cc6bcf5b17583367e2779535567f262dc5726</a></p>
<pre style="white-space: pre; background: #F8F8F8">The following commit(s) were added to refs/heads/master by this push:
<span style='display:block; white-space:pre;color:#404040;'> new 298cc6bcf5b py-sentence-transformers: Update to version 0.3.7
</span>298cc6bcf5b is described below
<span style='display:block; white-space:pre;color:#808000;'>commit 298cc6bcf5b17583367e2779535567f262dc5726
</span>Author: Steven Thomas Smith <s.t.smith@ieee.org>
AuthorDate: Thu Oct 1 15:22:03 2020 -0400
<span style='display:block; white-space:pre;color:#404040;'> py-sentence-transformers: Update to version 0.3.7
</span>---
python/py-sentence-transformers/Portfile | 46 ++++++++++++++++----------------
1 file changed, 23 insertions(+), 23 deletions(-)
<span style='display:block; white-space:pre;color:#808080;'>diff --git a/python/py-sentence-transformers/Portfile b/python/py-sentence-transformers/Portfile
</span><span style='display:block; white-space:pre;color:#808080;'>index 35ae549449f..fa247f5fffc 100644
</span><span style='display:block; white-space:pre;background:#e0e0ff;'>--- a/python/py-sentence-transformers/Portfile
</span><span style='display:block; white-space:pre;background:#e0e0ff;'>+++ b/python/py-sentence-transformers/Portfile
</span><span style='display:block; white-space:pre;background:#e0e0e0;'>@@ -4,7 +4,7 @@ PortSystem 1.0
</span> PortGroup python 1.0
name py-sentence-transformers
<span style='display:block; white-space:pre;background:#ffe0e0;'>-version 0.2.6.1
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+version 0.3.7
</span> revision 0
categories python textproc
<span style='display:block; white-space:pre;background:#e0e0e0;'>@@ -14,32 +14,32 @@ platforms darwin
</span>
description Sentence Embeddings with BERT & XLNet
<span style='display:block; white-space:pre;background:#ffe0e0;'>-long_description BERT/XLNet produces out-of-the-box rather bad\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- sentence embeddings. This repository fine-tunes\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- BERT/XLNet with a siamese or triplet network\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- structure to produce semantically meaningful\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- sentence embeddings that can be used in\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- unsupervised scenarios: Semantic textual\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- similarity via cosine-similarity, clustering,\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- semantic search. We provide an increasing number\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- of state-of-the-art pretrained models that can be\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- used to derive sentence embeddings. See Pretrained\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- Models. Details of the implemented approaches can\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- be found in our publication: Sentence-BERT:\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- Sentence Embeddings using Siamese BERT-Networks\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- (published at EMNLP 2019). You can use this code\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- to easily train your own sentence embeddings, that\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- are tuned for your specific task. We provide\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- various dataset readers and you can tune sentence\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- embeddings with different loss function, depending\
</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- on the structure of your dataset. For further\
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+long_description BERT/XLNet produces out-of-the-box rather bad \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ sentence embeddings. This repository fine-tunes \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ BERT/XLNet with a siamese or triplet network \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ structure to produce semantically meaningful \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ sentence embeddings that can be used in \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ unsupervised scenarios: Semantic textual \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ similarity via cosine-similarity, clustering, \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ semantic search. We provide an increasing number \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ of state-of-the-art pretrained models that can be \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ used to derive sentence embeddings. See Pretrained \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ Models. Details of the implemented approaches can \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ be found in our publication: Sentence-BERT: \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ Sentence Embeddings using Siamese BERT-Networks \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ (published at EMNLP 2019). You can use this code \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ to easily train your own sentence embeddings, that \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ are tuned for your specific task. We provide \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ various dataset readers and you can tune sentence \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ embeddings with different loss function, depending \
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+ on the structure of your dataset. For further \
</span> details, see Train your own Sentence Embeddings.
homepage https://github.com/UKPLab/sentence-transformers
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</span><span style='display:block; white-space:pre;background:#ffe0e0;'>- size 55609
</span><span style='display:block; white-space:pre;background:#e0ffe0;'>+checksums rmd160 9b370dfe44747f9a7f637ecd158ee12b13464320 \
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</span>
python.versions 37 38
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