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models_en_sentence_embeddings.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>SBERT.net Models</title>
<!-- Vue.js -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/vue/2.6.12/vue.min.js" integrity="sha512-BKbSR+cfyxLdMAsE0naLReFSLg8/pjbgfxHh/k/kUC82Hy7r6HtR5hLhobaln2gcTvzkyyehrdREdjpsQwy2Jw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
<!-- Bootstrap -->
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css"
integrity="sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh" crossorigin="anonymous">
<script src="https://code.jquery.com/jquery-3.4.1.slim.min.js"
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crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/umd/popper.min.js"
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crossorigin="anonymous"></script>
<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/js/bootstrap.min.js"
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crossorigin="anonymous"></script>
<!-- Axios -->
<!-- <script src="https://cdnjs.cloudflare.com/ajax/libs/axios/0.21.1/axios.min.js" integrity="sha512-bZS47S7sPOxkjU/4Bt0zrhEtWx0y0CRkhEp8IckzK+ltifIIE9EMIMTuT/mEzoIMewUINruDBIR/jJnbguonqQ==" crossorigin="anonymous" referrerpolicy="no-referrer"></script> -->
<!-- Font-awesome -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css"
integrity="sha512-iBBXm8fW90+nuLcSKlbmrPcLa0OT92xO1BIsZ+ywDWZCvqsWgccV3gFoRBv0z+8dLJgyAHIhR35VZc2oM/gI1w=="
crossorigin="anonymous" referrerpolicy="no-referrer"/>
<!-- Lodash -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.21/lodash.min.js"
integrity="sha512-WFN04846sdKMIP5LKNphMaWzU7YpMyCU245etK3g/2ARYbPK9Ub18eG+ljU96qKRCWh+quCY7yefSmlkQw1ANQ=="
crossorigin="anonymous" referrerpolicy="no-referrer"></script>
<style>
.fa-active {
color: #337ab7;
}
.header-cell {
cursor: pointer;
}
.models-table thead th {
position: sticky;
top: 0;
z-index: 1;
background-color: #ffffff;
}
.info-icon {
color: #007bff;
}
.info-icon:hover {
color: #0056b3;
}
.info-icon-model {
padding-left: 10px;
}
.bs-popover-auto[x-placement^=bottom], .bs-popover-bottom {
margin-top: .5rem;
}
.popover {
max-width: 400px;
}
</style>
</head>
<body>
<div id="app">
<table class="table table-bordered table-sm">
<thead>
<tr>
<th class="header-cell" @click="sortAsc = (sortBy=='name') ? sortAsc = !sortAsc : false; sortBy='name'">
<i class="fas fa-active" v-if="sortBy == 'name'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
Model Name
</th>
<th class="header-cell text-center" @click="sortAsc = (sortBy=='stsb') ? sortAsc = !sortAsc : false; sortBy='stsb'">
<i class="fas fa-active" v-if="sortBy == 'stsb'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
STSb
<span class="info-icon" data-trigger="hover" data-toggle="popover" title="STSbenchmark" data-content="Spearman-rank correlation on the STSbenchmark test set. Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
</th>
<th class="header-cell text-center" @click="sortAsc = (sortBy=='dupq') ? sortAsc = !sortAsc : false; sortBy='dupq'">
<i class="fas fa-active" v-if="sortBy == 'dupq'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
DupQ
<span class="info-icon" data-trigger="hover" data-toggle="popover" title="Duplicate Questions" data-content="Combination of two datasets for duplicate questions detection:<br>Mean-Average-Precision on the Quora Duplicate Questions Semantic Search test set.<br>Average-Precision on the Sprint duplicate questions test set.<br> Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
</th>
<th class="header-cell text-center" @click="sortAsc = (sortBy=='TwitterP') ? sortAsc = !sortAsc : false; sortBy='TwitterP'">
<i class="fas fa-active" v-if="sortBy == 'TwitterP'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
TwitterP
<span class="info-icon" data-trigger="hover" data-toggle="popover" title="Twitter Paraphrases" data-content="A test to find tweets that are considered paraphrases. Combination of the SemEval2015 Tweet paraphrase test set and the Twitter-URL-Corpus test set. Performance is measured using Average Precision.<br> Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
</th>
<th class="header-cell text-center" @click="sortAsc = (sortBy=='scidocs') ? sortAsc = !sortAsc : false; sortBy='scidocs'">
<i class="fas fa-active" v-if="sortBy == 'scidocs'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
SciDocs
<span class="info-icon" data-trigger="hover" data-toggle="popover" title="SciDocs" data-content="A test to find similar scientific publications given a paper title. From SciDocs, we use the information which papers are often co-cited, co-read, or co-viewed. Performance is measured using MAP.<br> Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
</th>
<th class="header-cell text-center" @click="sortAsc = (sortBy=='clustering') ? sortAsc = !sortAsc : false; sortBy='clustering'">
<i class="fas fa-active" v-if="sortBy == 'clustering'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
Clustering
<span class="info-icon" data-trigger="hover" data-toggle="popover" title="Clustering" data-content="Here we test how well the embeddings can be used for clustering. We use three datasets: email subjects from 20NewsGroups, titles from 199 popular subreddits, questions from 121 StackExchanges. We cluster different sentence collections with sentences from 10-50 categories. Performance is measured using V-Measure. <br> Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
</th>
<th class="header-cell text-center" @click="sortAsc = (sortBy=='final_score') ? sortAsc = !sortAsc : false; sortBy='final_score'">
<i class="fas fa-active" v-if="sortBy == 'final_score'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
Avg. Performance
<span class="info-icon" data-trigger="hover" data-toggle="popover" title="Average Performance" data-content="Average Performance over all tasks.<br>Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
</th>
<th class="header-cell text-center" @click="sortAsc = (sortBy=='speed') ? sortAsc = !sortAsc : false; sortBy='speed'">
<i class="fas fa-active" v-if="sortBy == 'speed'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
Speed
<span class="info-icon" data-trigger="hover" data-toggle="popover" title="Encoding Speed" data-content="Encoding speed (sentences / sec) on a V100 GPU.<br>Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
</th>
</tr>
</thead>
<tbody>
<tr v-for="item in sortedModels">
<td>
{{ item.name }}
<span class="info-icon info-icon-model" data-trigger="hover" data-toggle="popover" :title="item.name" :data-content="'<b>Base Model:</b> '+item.base_model+'<br><b>Pooling:</b> '+item.pooling+'<br><b>Training Data:</b> '+item.training_data+'<br><b>Dimensions:</b> '+item.dim+'<br><b>Size:</b> '+item.size+' MB'" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
</td>
<td class="text-center">{{ item.stsb.toFixed(2) }}</td>
<td class="text-center"><span :title="'Quora: '+item.qqp.toFixed(2)+' Sprint: '+item.sprint.toFixed(2)">{{ item.dupq.toFixed(2) }}</span></td>
<td class="text-center">{{ item.TwitterP.toFixed(2) }}</td>
<td class="text-center">{{ item.scidocs.toFixed(2) }}</td>
<td class="text-center"><span :title="'Newsgroups: '+item.newsgroups.toFixed(2)+' Reddit: '+item.reddit.toFixed(2)+' StackExchange: '+item.stackexchange.toFixed(2)">{{ item.clustering.toFixed(2) }}</span></td>
<td class="text-center">{{ item.final_score.toFixed(2) }}</td>
<td class="text-center">{{ item.speed }}</td>
</tr>
</tbody>
</table>
</div>
<script>
var app = new Vue({
el: '#app',
data: {
models: [
{
"name": "stsb-mpnet-base-v2",
"base_model": "microsoft/mpnet-base",
"pooling": "Mean Pooling",
"training_data": "NLI+STSb",
"stsb": 88.57,
"qqp": 79.74,
"sprint": 90.34,
"TwitterP": 75.35,
"scidocs": 72.48,
"newsgroups": 31.56,
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"stackexchange": 46.41,
"speed": 2800,
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},
{
"name": "stsb-roberta-base-v2",
"base_model": "roberta-base",
"pooling": "Mean Pooling",
"training_data": "NLI+STSb",
"stsb": 87.21,
"qqp": 78.68,
"sprint": 86.42,
"TwitterP": 73.44,
"scidocs": 69.83,
"newsgroups": 26.87,
"reddit": 36.91,
"stackexchange": 45.48,
"speed": 2300,
"size": 440,
"dim": 768
},
{
"name": "stsb-distilroberta-base-v2",
"base_model": "distilroberta-base",
"pooling": "Mean Pooling",
"training_data": "NLI+STSb",
"stsb": 86.41,
"qqp": 78.13,
"sprint": 87.28,
"TwitterP": 73.68,
"scidocs": 69.85,
"newsgroups": 28.63,
"reddit": 38.26,
"stackexchange": 46.16,
"speed": 4000,
"size": 292,
"dim": 768
},
{
"name": "nli-mpnet-base-v2",
"base_model": "microsoft/mpnet-base",
"pooling": "Mean Pooling",
"training_data": "NLI",
"stsb": 86.53,
"qqp": 80.65,
"sprint": 85.79,
"TwitterP": 76.24,
"scidocs": 72.90,
"newsgroups": 36.56,
"reddit": 42.68,
"stackexchange": 50.90,
"speed": 2800,
"size": 385,
"dim": 768
},
{
"name": "nli-roberta-base-v2",
"base_model": "roberta-base",
"pooling": "Mean Pooling",
"training_data": "NLI",
"stsb": 85.54,
"qqp": 78.73,
"sprint": 81.67,
"TwitterP": 74.28,
"scidocs": 69.86,
"newsgroups": 31.28,
"reddit": 39.58,
"stackexchange": 49.51,
"speed": 2300,
"size": 440,
"dim": 768
},
{
"name": "nli-distilroberta-base-v2",
"base_model": "distilroberta-base",
"pooling": "Mean Pooling",
"training_data": "NLI",
"stsb": 84.38,
"qqp": 78.47,
"sprint": 83.03,
"TwitterP": 73.86,
"scidocs": 70.23,
"newsgroups": 31.87,
"reddit": 39.12,
"stackexchange": 49.27,
"speed": 4000,
"size": 292,
"dim": 768
},
{
"name": "average_word_embeddings_glove.6B.300d",
"base_model": "Word Embeddings: GloVe",
"pooling": "Mean Pooling",
"training_data": "-",
"stsb": 61.77,
"qqp": 69.18,
"sprint": 86.96,
"TwitterP": 68.60,
"scidocs": 63.69,
"newsgroups": 26.65,
"reddit": 28.37,
"stackexchange": 36.37,
"speed": 34000,
"size": 422,
"dim": 300
},
{
"name": "average_word_embeddings_komninos",
"base_model": "Word Embeddings: Komninos et al.",
"pooling": "Mean Pooling",
"training_data": "-",
"stsb": 61.56,
"qqp": 69.83,
"sprint": 85.55,
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"reddit": 29.54,
"stackexchange": 39.35,
"speed": 22000,
"size": 237,
"dim": 300
},
/*{
"name": "average_word_embeddings_levy_dependency",
"base_model": "Word Embeddings: Levy et al.",
"pooling": "Mean Pooling",
"training_data": "-",
"stsb": 59.22,
"qqp": 64.62,
"sprint": 80.12,
"TwitterP": 70.79,
"scidocs": 60.04 ,
"newsgroups": 22.72,
"reddit": 24.23,
"stackexchange": 33.66,
"speed": 22000,
"size": 186,
"dim": 300
},*/
{
"name": "paraphrase-MiniLM-L12-v2",
"base_model": "microsoft/MiniLM-L12-H384-uncased",
"pooling": "Mean Pooling",
"training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
"stsb": 84.41,
"qqp": 84.64,
"sprint": 89.91,
"TwitterP": 75.34,
"scidocs": 80.08,
"newsgroups": 41.81,
"reddit": 44.42,
"stackexchange": 54.63,
"speed": 7500,
"size": 118,
"dim": 384
},
{
"name": "paraphrase-MiniLM-L6-v2",
"base_model": "nreimers/MiniLM-L6-H384-uncased",
"pooling": "Mean Pooling",
"training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
"stsb": 84.12,
"qqp": 84.25,
"sprint": 90.21,
"TwitterP": 76.32,
"scidocs": 78.91,
"newsgroups": 40.16,
"reddit": 42.71,
"stackexchange": 53.14,
"speed": 14200,
"size": 80,
"dim": 384
},
{
"name": "paraphrase-MiniLM-L3-v2",
"base_model": "nreimers/MiniLM-L3-H384-uncased",
"pooling": "Mean Pooling",
"training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
"stsb": 82.41,
"qqp": 83.29,
"sprint": 92.9,
"TwitterP": 76.14,
"scidocs": 77.71,
"newsgroups": 37.73,
"reddit": 41.18,
"stackexchange": 51.25,
"speed": 19000,
"size": 61,
"dim": 384
},
{
"name": "paraphrase-distilroberta-base-v2",
"base_model": "distilroberta-base",
"pooling": "Mean Pooling",
"training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
"stsb": 85.37,
"qqp": 84.31,
"sprint": 89.64,
"TwitterP": 73.96,
"scidocs": 80.25,
"newsgroups": 42.12,
"reddit": 47.53,
"stackexchange": 57.90,
"speed": 4000,
"size": 292,
"dim": 768
},
{
"name": "paraphrase-TinyBERT-L6-v2",
"base_model": "https://huggingface.co/nreimers/TinyBERT_L-6_H-768_v2",
"pooling": "Mean Pooling",
"training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
"stsb": 84.91,
"qqp": 84.23,
"sprint": 89.62,
"TwitterP": 75.39,
"scidocs": 81.51,
"newsgroups": 43.82,
"reddit": 44.61,
"stackexchange": 55.69,
"speed": 4500,
"size": 238,
"dim": 768
},
{
"name": "paraphrase-mpnet-base-v2",
"base_model": "microsoft/mpnet-base",
"pooling": "Mean Pooling",
"training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
"stsb": 86.99,
"qqp": 84.93,
"sprint": 90.67,
"TwitterP": 76.05,
"scidocs": 80.57,
"newsgroups": 48.13,
"reddit": 50.52,
"stackexchange": 59.79,
"speed": 2800,
"size": 387,
"dim": 768
},
/*{
"name": "paraphrase-albert-base-v2",
"base_model": "albert-base-v2",
"pooling": "Mean Pooling",
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{
"name": "paraphrase-multilingual-mpnet-base-v2",
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"training_data": "Multi-lingual model of paraphrase-mpnet-base-v2, extended to 50+ languages.",
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{
"name": "distiluse-base-multilingual-cased-v1",
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"name": "distiluse-base-multilingual-cased-v2",
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models_ext = models_ext.map(function(elem, index) { elem.clustering = (elem.newsgroups + elem.reddit + elem.stackexchange)/3.0; return elem;} );
//Final score
models_ext = models_ext.map(function(elem, index) { elem.final_score = (elem.stsb + elem.dupq + elem.TwitterP + elem.scidocs + elem.clustering)/ 5.0; return elem;} );
return _.orderBy(models_ext, (item) => item[this.sortBy] || (this.sortAsc ? 9999 : -9999), this.sortAsc ? 'asc' : 'desc')
}
}
})
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