From 661f83a1d1fbdb2502dcf3db3096ec63e6e5f5c5 Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Sat, 14 Nov 2020 15:35:44 +0000 Subject: [PATCH] ci: Automated build push --- .../bi_encoders.text_text.dpr2vec.doctree | Bin 35101 -> 35134 bytes ...i_encoders.text_text.lareqa_qa2vec.doctree | Bin 40499 -> 40529 bytes .../bi_encoders.text_text.use_qa2vec.doctree | Bin 35400 -> 35430 bytes ...ncoders.audio.speech_embedding2vec.doctree | Bin 31470 -> 31500 bytes .../encoders.audio.trill2vec.doctree | Bin 29757 -> 29787 bytes .../encoders.audio.vggish2vec.doctree | Bin 28444 -> 28474 bytes .../encoders.audio.yamnet2vec.doctree | Bin 31317 -> 31347 bytes docs/.doctrees/encoders.image.bit2vec.doctree | Bin 33204 -> 33234 bytes ...ncoders.image.inception_resnet2vec.doctree | Bin 33627 -> 33657 bytes .../encoders.image.mobilenet2vec.doctree | Bin 33276 -> 33306 bytes .../encoders.image.resnet2vec.doctree | Bin 31719 -> 31749 bytes .../.doctrees/encoders.text.labse2vec.doctree | Bin 33887 -> 33917 bytes .../encoders.text.legalbert2vec.doctree | Bin 37112 -> 37142 bytes docs/.doctrees/environment.pickle | Bin 113098 -> 113098 bytes docs/bi_encoders.text_text.dpr2vec.html | 2 +- docs/bi_encoders.text_text.lareqa_qa2vec.html | 2 +- docs/bi_encoders.text_text.use_qa2vec.html | 2 +- docs/encoders.audio.speech_embedding2vec.html | 2 +- docs/encoders.audio.trill2vec.html | 2 +- docs/encoders.audio.vggish2vec.html | 2 +- docs/encoders.audio.yamnet2vec.html | 2 +- docs/encoders.image.bit2vec.html | 2 +- docs/encoders.image.inception_resnet2vec.html | 2 +- docs/encoders.image.mobilenet2vec.html | 2 +- docs/encoders.image.resnet2vec.html | 2 +- docs/encoders.text.labse2vec.html | 2 +- docs/encoders.text.legalbert2vec.html | 2 +- docs/searchindex.js | 2 +- 28 files changed, 14 insertions(+), 14 deletions(-) diff --git a/docs/.doctrees/bi_encoders.text_text.dpr2vec.doctree b/docs/.doctrees/bi_encoders.text_text.dpr2vec.doctree index f9b1f58c8f55ab1b4c4714d3e72386c7df4f3cd8..5b5a1fabbc8635d6f3ad2055945a2bffe4cc0ddb 100644 GIT binary patch delta 102 zcmbO`iD};?CYA=4sm2>wdRY|}w6wxflS}f86ns+i(n~V5w5$}&%`6l&Qc}|rOLIyz fCog9$*5*eOoYFd_aZ2qJjSSWvbg9kyZ10)?g4rQI delta 69 zcmdltiD~X6CYA=4sX`lBdRfIpwY0)glS}f86ns+i(n~V5w5$}&EGBPaEf!?~v8J?6 UX`E6!MI(c?2h86b!1k^Q0Q?FTVE_OC diff --git a/docs/.doctrees/bi_encoders.text_text.lareqa_qa2vec.doctree b/docs/.doctrees/bi_encoders.text_text.lareqa_qa2vec.doctree index f9127eb45a0a7b48b95b48629f8647be87bec6c0..ab4295b15c7f74c1d8753b1940fa4074ab5309a3 100644 GIT binary patch delta 110 zcmdnIhw0)TCYA=4scsutp0X+`XlaF|CYR(FDfp!3rI%!AX;~?l8X75Rq@<=Lmgbab nPGT!I;71di(mJJaO6?Sl4AveDsSN3i!pR#2jW!=-E1C`f-GC(k delta 81 zcmcb(hiUU3CYA=4sTvzup0bLIX=#O}CYR(FDfp!3rI%!AX;~?l8X8UJVk?$m1v96# dPHCJ{J4GXdwFfGYA)Qe;*+9~0^L@6W=>RwJ8dLxP diff --git a/docs/.doctrees/bi_encoders.text_text.use_qa2vec.doctree b/docs/.doctrees/bi_encoders.text_text.use_qa2vec.doctree index 0f23248c8ed8872cc9a8d55840a01f131c83e4ef..a60fb1f87a8beb46fa3200d23179488012c194d5 100644 GIT binary patch delta 85 zcmX>xh3VN8CYA=4snHu*dRQ41CQoD)<5JK_Nli;E%_-5GyqvXIo8L;o)X)eaIHh$; 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DPR2Vec

Transformers

Model Name: Dense Passage Retrieval

-

Vector Length: 68

+

Vector Length: 768 (default)

Description: Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.

Paper: https://arxiv.org/abs/2004.04906

diff --git a/docs/bi_encoders.text_text.lareqa_qa2vec.html b/docs/bi_encoders.text_text.lareqa_qa2vec.html index 8f42f409..50881ccc 100644 --- a/docs/bi_encoders.text_text.lareqa_qa2vec.html +++ b/docs/bi_encoders.text_text.lareqa_qa2vec.html @@ -200,7 +200,7 @@

LAReQA2Vec

TFHub

Model Name: LAReQA: Language-agnostic answer retrieval from a multilingual pool

-

Vector Length: 512

+

Vector Length: 512 (default)

Description: We present LAReQA, a challenging new benchmark for language-agnostic answer retrieval from a multilingual candidate pool. Unlike previous cross-lingual tasks, LAReQA tests for “strong” cross-lingual alignment, requiring semantically related cross-language pairs to be closer in representation space than unrelated same-language pairs. Building on multilingual BERT (mBERT), we study different strategies for achieving strong alignment. We find that augmenting training data via machine translation is effective, and improves significantly over using mBERT out-of-the-box. Interestingly, the embedding baseline that performs the best on LAReQA falls short of competing baselines on zero-shot variants of our task that only target “weak” alignment. This finding underscores our claim that languageagnostic retrieval is a substantively new kind of cross-lingual evaluation.

Paper: https://arxiv.org/abs/2004.05484

diff --git a/docs/bi_encoders.text_text.use_qa2vec.html b/docs/bi_encoders.text_text.use_qa2vec.html index 307640b2..8a240ecf 100644 --- a/docs/bi_encoders.text_text.use_qa2vec.html +++ b/docs/bi_encoders.text_text.use_qa2vec.html @@ -200,7 +200,7 @@

USEQA2Vec

TFHub

Model Name: Universal Sentence Encoder Question Answering

-

Vector Length: 512

+

Vector Length: 512 (default)

Description: - Developed by researchers at Google, 2019, v2 [1]. - It is trained on a variety of data sources and tasks, with the goal of learning text representations that diff --git a/docs/encoders.audio.speech_embedding2vec.html b/docs/encoders.audio.speech_embedding2vec.html index 38c4e132..26827da1 100644 --- a/docs/encoders.audio.speech_embedding2vec.html +++ b/docs/encoders.audio.speech_embedding2vec.html @@ -200,7 +200,7 @@

SpeechEmbedding2Vec

TFHub

Model Name: Speech Embedding

-

Vector Length: 96

+

Vector Length: 96 (default)

Description: With the rise of low power speech-enabled devices, there is a growing demand to quickly produce models for recognizing arbitrary sets of keywords. As with many machine learning tasks, one of the most challenging parts in the model creation process is obtaining diff --git a/docs/encoders.audio.trill2vec.html b/docs/encoders.audio.trill2vec.html index 09edb306..a8f98e6c 100644 --- a/docs/encoders.audio.trill2vec.html +++ b/docs/encoders.audio.trill2vec.html @@ -200,7 +200,7 @@

Trill2Vec

TFHub

Model Name: Trill - Triplet Loss Network

-

Vector Length: 512

+

Vector Length: 512 (default)

Description: The ultimate goal of transfer learning is to reduce labeled data requirements by exploiting a pre-existing embedding model trained for different datasets or tasks. The visual and language communities have established benchmarks to compare embeddings, but the speech diff --git a/docs/encoders.audio.vggish2vec.html b/docs/encoders.audio.vggish2vec.html index e59e2da5..e7ed2d4b 100644 --- a/docs/encoders.audio.vggish2vec.html +++ b/docs/encoders.audio.vggish2vec.html @@ -200,7 +200,7 @@

Vggish2Vec

TFHub

Model Name: VGGish

-

Vector Length: 512

+

Vector Length: 512 (default)

Description: An audio event embedding model trained on the YouTube-8M dataset. VGGish should be used: diff --git a/docs/encoders.audio.yamnet2vec.html b/docs/encoders.audio.yamnet2vec.html index 59567272..d3c13b1e 100644 --- a/docs/encoders.audio.yamnet2vec.html +++ b/docs/encoders.audio.yamnet2vec.html @@ -200,7 +200,7 @@

Yamnet2Vec

TFHub

Model Name: Yamnet

-

Vector Length: 1024

+

Vector Length: 1024 (default)

Description: YAMNet is an audio event classifier that takes audio waveform as input and makes independent predictions for each of 521 audio events from the AudioSet ontology. The model uses the MobileNet v1 architecture and was trained using diff --git a/docs/encoders.image.bit2vec.html b/docs/encoders.image.bit2vec.html index 423b29c3..c2317f08 100644 --- a/docs/encoders.image.bit2vec.html +++ b/docs/encoders.image.bit2vec.html @@ -200,7 +200,7 @@

Bit2Vec

TFHub

Model Name: BiT - Big Transfer, General Visual Representation Learning (Small)

-

Vector Length: 2048

+

Vector Length: 2048 (default)

Description: Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model diff --git a/docs/encoders.image.inception_resnet2vec.html b/docs/encoders.image.inception_resnet2vec.html index d1f11e34..8fe9e04e 100644 --- a/docs/encoders.image.inception_resnet2vec.html +++ b/docs/encoders.image.inception_resnet2vec.html @@ -200,7 +200,7 @@

InceptionResnet2Vec

TFHub

Model Name: Inception Resnet

-

Vector Length: 1536

+

Vector Length: 1536 (default)

Description: Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at diff --git a/docs/encoders.image.mobilenet2vec.html b/docs/encoders.image.mobilenet2vec.html index cc48bfcf..747b92ce 100644 --- a/docs/encoders.image.mobilenet2vec.html +++ b/docs/encoders.image.mobilenet2vec.html @@ -200,7 +200,7 @@

MobileNet2Vec

TFHub

Model Name: MobileNet

-

Vector Length: 1024

+

Vector Length: 1024 (default)

Description: We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.

Paper: https://arxiv.org/abs/1704.04861

diff --git a/docs/encoders.image.resnet2vec.html b/docs/encoders.image.resnet2vec.html index 4b4c6481..4a730606 100644 --- a/docs/encoders.image.resnet2vec.html +++ b/docs/encoders.image.resnet2vec.html @@ -200,7 +200,7 @@

ResNet2Vec

TFHub

Model Name: ResNet

-

Vector Length: 2048

+

Vector Length: 2048 (default)

Description: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.

Paper: https://arxiv.org/abs/1512.03385

diff --git a/docs/encoders.text.labse2vec.html b/docs/encoders.text.labse2vec.html index 62be48b8..1eddaeff 100644 --- a/docs/encoders.text.labse2vec.html +++ b/docs/encoders.text.labse2vec.html @@ -200,7 +200,7 @@

LaBSE2Vec

TFHub

Model Name: LaBSE - Language-agnostic BERT Sentence Embedding

-

Vector Length: 768

+

Vector Length: 768 (default)

Description: The language-agnostic BERT sentence embedding encodes text into high dimensional vectors. The model is trained and optimized to produce similar representations exclusively for bilingual sentence pairs that are translations of each other. So it can be used for mining for translations of a sentence in a larger corpus.

Paper: https://arxiv.org/pdf/2007.01852v1.pdf

diff --git a/docs/encoders.text.legalbert2vec.html b/docs/encoders.text.legalbert2vec.html index c38c252f..6e660547 100644 --- a/docs/encoders.text.legalbert2vec.html +++ b/docs/encoders.text.legalbert2vec.html @@ -200,7 +200,7 @@

LegalBert2Vec

Transformers

Model Name: Legal Bert

-

Vector Length: 768

+

Vector Length: 768 (default)

Description: BERT has achieved impressive performance in several NLP tasks. However, there has been limited investigation on its adaptation guidelines in specialised domains. Here we focus on the legal domain, where we explore several approaches for applying BERT models to downstream legal tasks, evaluating on multiple datasets. Our findings indicate that the previous guidelines for pre-training and fine-tuning, often blindly followed, do not always generalize well in the legal domain. Thus we propose a systematic investigation of the available strategies when applying BERT in specialised domains. These are: (a) use the original BERT out of the box, (b) adapt BERT by additional pre-training on domain-specific corpora, and (c) pre-train BERT from scratch on domain-specific corpora. We also propose a broader hyper-parameter search space when fine-tuning for downstream tasks and we release LEGAL-BERT, a family of BERT models intended to assist legal NLP research, computational law, and legal technology applications.

Paper: https://arxiv.org/abs/2010.02559

diff --git a/docs/searchindex.js b/docs/searchindex.js index 508acf4d..0a85e518 100644 --- a/docs/searchindex.js +++ b/docs/searchindex.js @@ -1 +1 @@ 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