awesome-sentence-embedding / Word Embeddings |
| WebVectors: A Toolkit for Building Web Interfaces for Vector Semantic Models | | | |
| RusVectōrēs | | | |
| Efficient Estimation of Word Representations in Vector Space | | | |
| C | 1,516 | over 1 year ago | |
| Word2Vec | | | |
| Word Representations via Gaussian Embedding | | | |
| Cython | 190 | over 6 years ago | |
| A Probabilistic Model for Learning Multi-Prototype Word Embeddings | | | |
| DMTK | 116 | over 8 years ago | |
| Dependency-Based Word Embeddings | | | |
| C++ | | | |
| word2vecf | | | |
| GloVe: Global Vectors for Word Representation | | | |
| C | 6,835 | about 1 year ago | |
| GloVe | 6,835 | about 1 year ago | |
| Sparse Overcomplete Word Vector Representations | | | |
| C++ | 54 | about 7 years ago | |
| From Paraphrase Database to Compositional Paraphrase Model and Back | | | |
| Theano | 30 | over 8 years ago | |
| PARAGRAM | | | |
| Non-distributional Word Vector Representations | | | |
| Python | 62 | about 7 years ago | |
| WordFeat | 62 | about 7 years ago | |
| Joint Learning of Character and Word Embeddings | | | |
| C | 299 | about 4 years ago | |
| SensEmbed: Learning Sense Embeddings for Word and Relational Similarity | | | |
| SensEmbed | | | |
| Topical Word Embeddings | | | |
| Cython | 315 | over 6 years ago | |
| | | | |
| Swivel: Improving Embeddings by Noticing What's Missing | | | |
| TF | 76,987 | 1 day ago | |
| Counter-fitting Word Vectors to Linguistic Constraints | | | |
| Python | 142 | over 4 years ago | |
| counter-fitting | | | (broken) |
| Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec | | | |
| Chainer | 3,146 | almost 3 years ago | |
| Siamese CBOW: Optimizing Word Embeddings for Sentence Representations | | | |
| Theano | | | |
| Siamese CBOW | | | |
| Matrix Factorization using Window Sampling and Negative Sampling for Improved Word Representations | | | |
| Go | 802 | almost 4 years ago | |
| lexvec | 802 | almost 4 years ago | |
| Enriching Word Vectors with Subword Information | | | |
| C++ | 25,869 | 7 months ago | |
| fastText | | | |
| Morphological Priors for Probabilistic Neural Word Embeddings | | | |
| Theano | 52 | almost 8 years ago | |
| A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks | | | |
| C++ | 23 | 11 months ago | |
| charNgram2vec | | | |
| ConceptNet 5.5: An Open Multilingual Graph of General Knowledge | | | |
| Python | 1,286 | about 2 years ago | |
| Numberbatch | 1,286 | about 2 years ago | |
| Learning Word Meta-Embeddings | | | |
| Meta-Emb | | | (broken) |
| Offline bilingual word vectors, orthogonal transformations and the inverted softmax | | | |
| Python | 1,195 | over 1 year ago | |
| Multimodal Word Distributions | | | |
| TF | 283 | about 5 years ago | |
| word2gm | 283 | about 5 years ago | |
| Poincaré Embeddings for Learning Hierarchical Representations | | | |
| Pytorch | 1,670 | 2 months ago | |
| Context encoders as a simple but powerful extension of word2vec | | | |
| Python | 20 | over 4 years ago | |
| Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints | | | |
| TF | 64 | about 7 years ago | |
| Attract-Repel | 64 | about 7 years ago | |
| Learning Chinese Word Representations From Glyphs Of Characters | | | |
| C | 30 | over 6 years ago | |
| Making Sense of Word Embeddings | | | |
| Python | 212 | over 3 years ago | |
| sensegram | | | |
| Hash Embeddings for Efficient Word Representations | | | |
| Keras | 42 | almost 7 years ago | |
| BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages | | | |
| Gensim | 1,179 | 5 days ago | |
| BPEmb | 1,179 | 5 days ago | |
| SPINE: SParse Interpretable Neural Embeddings | | | |
| Pytorch | 50 | over 4 years ago | |
| SPINE | | | |
| AraVec: A set of Arabic Word Embedding Models for use in Arabic NLP | | | |
| Gensim | 391 | over 3 years ago | |
| AraVec | 391 | over 3 years ago | |
| Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics | | | |
| C | 841 | about 5 years ago | |
| Dict2vec : Learning Word Embeddings using Lexical Dictionaries | | | |
| C++ | 115 | over 3 years ago | |
| Dict2vec | 115 | over 3 years ago | |
| Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components | | | |
| C | 99 | over 5 years ago | |
| Representation Tradeoffs for Hyperbolic Embeddings | | | |
| Pytorch | 370 | about 1 year ago | |
| h-MDS | 370 | about 1 year ago | |
| Dynamic Meta-Embeddings for Improved Sentence Representations | | | |
| Pytorch | 332 | about 4 years ago | |
| DME/CDME | 332 | about 4 years ago | |
| Analogical Reasoning on Chinese Morphological and Semantic Relations | | | |
| ChineseWordVectors | 11,775 | 11 months ago | |
| Probabilistic FastText for Multi-Sense Word Embeddings | | | |
| C++ | 148 | over 6 years ago | |
| Probabilistic FastText | 148 | over 6 years ago | |
| Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks | | | |
| TF | 289 | over 1 year ago | |
| SynGCN | | | |
| FRAGE: Frequency-Agnostic Word Representation | | | |
| Pytorch | 118 | over 5 years ago | |
| Wikipedia2Vec: An Optimized Tool for LearningEmbeddings of Words and Entities from Wikipedia | | | |
| Cython | 934 | 5 months ago | |
| Wikipedia2Vec | | | |
| Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings | | | |
| ChineseEmbedding | | | |
| cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information | | | |
| C++ | 275 | over 1 year ago | |
| VCWE: Visual Character-Enhanced Word Embeddings | | | |
| Pytorch | 15 | over 5 years ago | |
| VCWE | 15 | over 5 years ago | |
| Learning Cross-lingual Embeddings from Twitter via Distant Supervision | | | |
| Text | 14 | over 4 years ago | |
| An Unsupervised Character-Aware Neural Approach to Word and Context Representation Learning | | | |
| TF | 0 | almost 6 years ago | |
| ViCo: Word Embeddings from Visual Co-occurrences | | | |
| Pytorch | 25 | about 5 years ago | |
| ViCo | 25 | about 5 years ago | |
| Spherical Text Embedding | | | |
| C | 176 | 11 months ago | |
| Unsupervised word embeddings capture latent knowledge from materials science literature | | | |
| Gensim | 617 | over 1 year ago | |
awesome-sentence-embedding / OOV Handling |
| ALaCarte | 103 | almost 6 years ago | : |
| Mimick | 153 | almost 5 years ago | : |
| CompactReconstruction | 9 | over 1 year ago | : |
awesome-sentence-embedding / Contextualized Word Embeddings |
| Language Models are Unsupervised Multitask Learners | | | |
| TF | 22,303 | about 2 months ago | |
| 117M | 22,303 | about 2 months ago | GPT-2( , , , , , ) |
| Learned in Translation: Contextualized Word Vectors | | | |
| Pytorch | 472 | over 2 years ago | |
| CoVe | 472 | over 2 years ago | |
| Universal Language Model Fine-tuning for Text Classification | | | |
| Pytorch | 26,138 | 16 days ago | |
| English | | | ULMFit( , ) |
| Deep contextualized word representations | | | |
| Pytorch | 11,741 | almost 2 years ago | |
| AllenNLP | | | ELMO( , ) |
| Efficient Contextualized Representation:Language Model Pruning for Sequence Labeling | | | |
| Pytorch | 146 | over 4 years ago | |
| LD-Net | 146 | over 4 years ago | |
| Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation | | | |
| Pytorch | 1,458 | over 3 years ago | |
| ELMo | 1,458 | over 3 years ago | |
| Direct Output Connection for a High-Rank Language Model | | | |
| Pytorch | 12 | over 5 years ago | |
| DOC | | | |
| BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | | | |
| TF | 37,897 | 2 months ago | |
| BERT | 37,897 | 2 months ago | BERT( , , ) |
| Contextual String Embeddings for Sequence Labeling | | | |
| Pytorch | 13,852 | 7 days ago | |
| Flair | 13,852 | 7 days ago | |
| Improving Language Understanding by Generative Pre-Training | | | |
| TF | 2,140 | over 5 years ago | |
| GPT | 2,140 | over 5 years ago | |
| Multi-Task Deep Neural Networks for Natural Language Understanding | | | |
| Pytorch | 2,227 | 7 months ago | |
| MT-DNN | 2,227 | 7 months ago | |
| BioBERT: pre-trained biomedical language representation model for biomedical text mining | | | |
| TF | 1,931 | about 1 year ago | |
| BioBERT | 653 | over 4 years ago | |
| Cross-lingual Language Model Pretraining | | | |
| Pytorch | 2,874 | over 1 year ago | |
| XLM | 2,874 | over 1 year ago | |
| Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context | | | |
| TF | 3,606 | about 2 years ago | |
| Transformer-XL | 3,606 | about 2 years ago | |
| Efficient Contextual Representation Learning Without Softmax Layer | | | |
| Pytorch | 4 | about 4 years ago | |
| SciBERT: Pretrained Contextualized Embeddings for Scientific Text | | | |
| Pytorch, TF | 1,489 | over 2 years ago | |
| SciBERT | 1,489 | over 2 years ago | |
| Publicly Available Clinical BERT Embeddings | | | |
| Text | 659 | about 4 years ago | |
| clinicalBERT | | | |
| ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission | | | |
| Pytorch | 372 | almost 2 years ago | |
| ClinicalBERT | | | |
| ERNIE: Enhanced Language Representation with Informative Entities | | | |
| Pytorch | 1,410 | 9 months ago | |
| ERNIE | | | |
| Unified Language Model Pre-training for Natural Language Understanding and Generation | | | |
| Pytorch | 19,604 | about 1 month ago | |
| unilm1-large-cased | | | UniLMv1( , ) |
| HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization | | | |
| Pre-Training with Whole Word Masking for Chinese BERT | | | |
| Pytorch, TF | 9,587 | about 1 year ago | |
| BERT-wwm | 9,587 | about 1 year ago | |
| XLNet: Generalized Autoregressive Pretraining for Language Understanding | | | |
| TF | 6,177 | over 1 year ago | |
| XLNet | 6,177 | over 1 year ago | |
| ERNIE 2.0: A Continual Pre-training Framework for Language Understanding | | | |
| PaddlePaddle | 6,294 | about 1 month ago | |
| ERNIE 2.0 | 6,294 | about 1 month ago | |
| SpanBERT: Improving Pre-training by Representing and Predicting Spans | | | |
| Pytorch | 884 | about 1 year ago | |
| SpanBERT | 884 | about 1 year ago | |
| RoBERTa: A Robustly Optimized BERT Pretraining Approach | | | |
| Pytorch | 30,200 | 26 days ago | |
| RoBERTa | 30,200 | 26 days ago | |
| Subword ELMo | | | |
| Pytorch | 12 | over 4 years ago | |
| Knowledge Enhanced Contextual Word Representations | | | |
| TinyBERT: Distilling BERT for Natural Language Understanding | | | |
| Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism | | | |
| Pytorch | 10,169 | 1 day ago | |
| BERT-345M | | | Megatron-LM( , ) |
| MultiFiT: Efficient Multi-lingual Language Model Fine-tuning | | | |
| Pytorch | 284 | over 4 years ago | |
| Extreme Language Model Compression with Optimal Subwords and Shared Projections | | | |
| MULE: Multimodal Universal Language Embedding | | | |
| Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks | | | |
| K-BERT: Enabling Language Representation with Knowledge Graph | | | |
| UNITER: Learning UNiversal Image-TExt Representations | | | |
| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations | | | |
| TF | 3,921 | almost 2 years ago | |
| BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension | | | |
| Pytorch | 30,200 | 26 days ago | |
| bart.base | | | BART( , , , , ) |
| DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter | | | |
| Pytorch, TF2.0 | 132,640 | 8 days ago | |
| DistilBERT | 132,640 | 8 days ago | |
| Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer | | | |
| TF | 6,122 | 15 days ago | |
| T5 | 6,122 | 15 days ago | |
| CamemBERT: a Tasty French Language Model | | | |
| CamemBERT | | | |
| ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations | | | |
| Pytorch | 642 | about 2 years ago | |
| Unsupervised Cross-lingual Representation Learning at Scale | | | |
| Pytorch | 2,874 | over 1 year ago | |
| xlmr.large | | | XLM-R (XLM-RoBERTa)( , ) |
| ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training | | | |
| Pytorch | 685 | 2 months ago | |
| ProphetNet-large-16GB | | | ProphetNet( , ) |
| CodeBERT: A Pre-Trained Model for Programming and Natural Languages | | | |
| Pytorch | 2,183 | about 1 year ago | |
| CodeBERT | | | |
| UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training | | | |
| Pytorch | 19,604 | about 1 month ago | |
| ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators | | | |
| TF | 2,325 | 7 months ago | |
| ELECTRA-Small | | | ELECTRA( , , ) |
| MPNet: Masked and Permuted Pre-training for Language Understanding | | | |
| Pytorch | 286 | about 3 years ago | |
| MPNet | | | |
| ParsBERT: Transformer-based Model for Persian Language Understanding | | | |
| Pytorch | 326 | over 1 year ago | |
| ParsBERT | | | |
| Language Models are Few-Shot Learners | | | |
| InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training | | | |
| Pytorch | 19,604 | about 1 month ago | |
awesome-sentence-embedding / Pooling Methods |
| SIF | 1,083 | about 5 years ago | : |
| TF-IDF | 9 | over 1 year ago | : |
| P-norm | 185 | almost 4 years ago | : |
| DisC | 54 | over 4 years ago | : |
| GEM | 19 | almost 6 years ago | : |
| SWEM | 283 | almost 2 years ago | : |
| VLAWE | 10 | over 5 years ago | : |
| Efficient Sentence Embedding using Discrete Cosine Transform | | | |
| fse: Gensim add-on for fast sentence embeddings. Supports Mean, Max, SIF,
uSIF | 618 | over 1 year ago | |
| Efficient Sentence Embedding via Semantic Subspace Analysis | | | |
awesome-sentence-embedding / Encoders |
| Incremental Domain Adaptation for Neural Machine Translation in Low-Resource Settings | | | |
| Python | 5 | about 5 years ago | |
| Distributed Representations of Sentences and Documents | | | |
| Pytorch | 411 | almost 2 years ago | |
| Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models | | | |
| Theano | 425 | over 7 years ago | |
| Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books | | | |
| Theano | 2,048 | over 4 years ago | |
| Order-Embeddings of Images and Language | | | |
| Theano | 186 | almost 8 years ago | |
| Towards Universal Paraphrastic Sentence Embeddings | | | |
| Theano | 193 | over 8 years ago | |
| From Word Embeddings to Document Distances | | | |
| C, Python | 537 | 4 months ago | |
| Learning Distributed Representations of Sentences from Unlabelled Data | | | |
| Python | 124 | over 7 years ago | |
| Charagram: Embedding Words and Sentences via Character n-grams | | | |
| Theano | 125 | about 8 years ago | |
| Learning Generic Sentence Representations Using Convolutional Neural Networks | | | |
| Theano | 34 | about 7 years ago | |
| Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features | | | |
| C++ | 1,193 | about 2 years ago | |
| Learning to Generate Reviews and Discovering Sentiment | | | |
| TF | 1,507 | over 1 year ago | |
| Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings | | | |
| Theano | 33 | over 7 years ago | |
| Supervised Learning of Universal Sentence Representations from Natural Language Inference Data | | | |
| Pytorch | 2,278 | about 3 years ago | |
| VSE++: Improving Visual-Semantic Embeddings with Hard Negatives | | | |
| Pytorch | 488 | almost 3 years ago | |
| Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm | | | |
| Keras | 1,512 | 2 months ago | |
| StarSpace: Embed All The Things! | | | |
| C++ | 3,939 | almost 2 years ago | |
| DisSent: Learning Sentence Representations from Explicit Discourse Relations | | | |
| Pytorch | 33 | over 4 years ago | |
| Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations | | | |
| Theano | 101 | 10 months ago | |
| Dual-Path Convolutional Image-Text Embedding with Instance Loss | | | |
| Matlab | 285 | over 1 year ago | |
| An efficient framework for learning sentence representations | | | |
| TF | 205 | about 5 years ago | |
| Universal Sentence Encoder | | | |
| TF-Hub | | | |
| End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions | | | |
| Theano | 16 | over 6 years ago | |
| Learning general purpose distributed sentence representations via large scale multi-task learning | | | |
| Pytorch | 311 | about 4 years ago | |
| Embedding Text in Hyperbolic Spaces | | | |
| TF | 8 | about 7 years ago | |
| Representation Learning with Contrastive Predictive Coding | | | |
| Keras | 520 | over 5 years ago | |
| Context Mover’s Distance & Barycenters: Optimal transport of contexts for building representations | | | |
| Python | 21 | over 3 years ago | |
| Learning Universal Sentence Representations with Mean-Max Attention Autoencoder | | | |
| TF | 16 | almost 6 years ago | |
| Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model | | | |
| TF-Hub | | | |
| Improving Sentence Representations with Consensus Maximisation | | | |
| BioSentVec: creating sentence embeddings for biomedical texts | | | |
| Python | 572 | about 1 year ago | |
| Word Mover's Embedding: From Word2Vec to Document Embedding | | | |
| C, Python | 81 | almost 6 years ago | |
| A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks | | | |
| Pytorch | 1,189 | about 1 year ago | |
| Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond | | | |
| Pytorch | 3,583 | 5 months ago | |
| Convolutional Neural Network for Universal Sentence Embeddings | | | |
| Theano | 2 | over 6 years ago | |
| No Training Required: Exploring Random Encoders for Sentence Classification | | | |
| Pytorch | 184 | over 4 years ago | |
| CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model | | | |
| Pytorch | 21 | over 5 years ago | |
| GLOSS: Generative Latent Optimization of Sentence Representations | | | |
| Multilingual Universal Sentence Encoder | | | |
| TF-Hub | | | |
| Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks | | | |
| Pytorch | 14,907 | 16 days ago | |
| SBERT-WK: A Sentence Embedding Method By Dissecting BERT-based Word Models | | | |
| Pytorch | 177 | over 3 years ago | |
| DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations | | | |
| Pytorch | 378 | over 1 year ago | |
| Language-agnostic BERT Sentence Embedding | | | |
| TF-Hub | | | |
| On the Sentence Embeddings from Pre-trained Language Models | | | |
| TF | 528 | over 3 years ago | |
awesome-sentence-embedding / Evaluation |
| decaNLP | 2,343 | 9 months ago | : |
| SentEval | 2,082 | 7 months ago | : |
| GLUE | 760 | about 3 years ago | : |
| Exploring Semantic Properties of Sentence Embeddings | | | |
| Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks | | | |
| Word Embeddings Benchmarks | 435 | almost 4 years ago | : |
| MLDoc | 152 | over 2 years ago | : |
| LexNET | 76,987 | 1 day ago | : |
| wordvectors.net | 120 | over 3 years ago | : |
| jiant | 1,637 | about 1 year ago | : |
| jiant | 1,637 | about 1 year ago | : |
| Evaluation of sentence embeddings in downstream and linguistic probing tasks | | | |
| QVEC | 75 | over 6 years ago | : |
| Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments | | | |
| EQUATE : A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference | | | |
| Evaluating Word Embedding Models: Methods andExperimental Results | | | |
| How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions | | | |
| Linguistic Knowledge and Transferability of Contextual Representations | | | : |
| LINSPECTOR | 23 | almost 5 years ago | : |
| Pitfalls in the Evaluation of Sentence Embeddings | | | |
| Probing Multilingual Sentence Representations With X-Probe | | | : |
awesome-sentence-embedding / Misc |
| Word Embedding Dimensionality Selection | 329 | over 4 years ago | : |
| Half-Size | 128 | over 3 years ago | : |
| magnitude | 1,623 | about 1 year ago | : |
| To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks | | | |
| Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors | | | : |
| The Pupil Has Become the Master: Teacher-Student Model-BasedWord Embedding Distillation with Ensemble Learning | | | : |
| Improving Distributional Similarity with Lessons Learned from Word Embeddings | | | : |
| Misspelling Oblivious Word Embeddings | | | : |
| Single Training Dimension Selection for Word Embedding with
PCA | | | |
| Compressing Word Embeddings via Deep Compositional Code Learning | | | : |
| UER: An Open-Source Toolkit for Pre-training
Models | | | : |
| Situating Sentence Embedders with Nearest Neighbor
Overlap | | | |
| German BERT | | | |
awesome-sentence-embedding / Vector Mapping |
| Cross-lingual Word Vectors Projection Using CCA | 56 | about 6 years ago | : |
| vecmap | 642 | over 1 year ago | : |
| MUSE | 3,182 | about 2 years ago | : |
| CrossLingualELMo | 98 | over 4 years ago | : |
awesome-sentence-embedding / Articles |
| Comparing Sentence Similarity Methods | | | |
| The Current Best of Universal Word Embeddings and Sentence Embeddings | | | |
| On sentence representations, pt. 1: what can you fit into a single #$!%@*&% blog post? | | | |
| Deep-learning-free Text and Sentence Embedding, Part 1 | | | |
| Deep-learning-free Text and Sentence Embedding, Part 2 | | | |
| An Overview of Sentence Embedding Methods | | | |
| Word embeddings in 2017: Trends and future directions | | | |
| A Walkthrough of InferSent – Supervised Learning of Sentence Embeddings | | | |
| A survey of cross-lingual word embedding models | | | |
| Introducing state of the art text classification with universal language models | | | |
| Document Embedding Techniques | | | |
awesome-sentence-embedding 
0ex/more-awesome