https://github.com/shibing624/text2vec
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Papers with Code 아카이브
Word Embeddings
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Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.
Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.
<span style="color:grey; opacity: 0.6">( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shyamupa/biling-survey
Cross-lingual Models of Word Embeddings: An Empirical Comparison
https://github.com/sidak/SentEval
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/sonsus/albert_paraphrase
https://github.com/soumyaumass/umwe
https://github.com/sreejukomath/NLPProjects
An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation
https://github.com/svjan5/gnns-for-nlp
Neural Graph Embedding Methods for Natural Language Processing
https://github.com/swapnilagashe/Bi-directional_LSTM_for_NER
Few-shot Learning for Named Entity Recognition in Medical Text
https://github.com/swordelucidator/nanobackpacklm
https://github.com/tensorflow/models/tree/master/research/adversarial_text
Adversarial Training Methods for Semi-Supervised Text Classification
https://github.com/textflint/textflint
https://github.com/thunlp/ReInfoSelect
TU Wien @ TREC Deep Learning '19 -- Simple Contextualization for Re-ranking
https://github.com/trhgu/awesome-fashion-contents
Style2Vec: Representation Learning for Fashion Items from Style Sets
https://github.com/trietnm2/sent2vec4j
https://github.com/tsandefer/capstone_2
A Latent Variable Model Approach to PMI-based Word Embeddings
https://github.com/tsandefer/dsi_capstone_2
A Latent Variable Model Approach to PMI-based Word Embeddings
https://github.com/tshev/faster-FastText
https://github.com/tshi04/ACCE
An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment Analysis
https://github.com/uclanlp/socialbias-dataset-construction-biases
On Measuring and Mitigating Biased Inferences of Word Embeddings
https://github.com/uclnlp/emoji2vec
emoji2vec: Learning Emoji Representations from their Description
https://github.com/ulf1/augtxt
https://github.com/umanlp/debie-command-line
A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces
https://github.com/uzairakbar/info-retrieval
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
https://github.com/vackosar/fasttext-vector-norms-and-oov-words
https://github.com/vamshi009/End-to-End-HRED-Dialogue-System
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
https://github.com/vano1205/efficientcl
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
https://github.com/vasilikivmo/repo2_Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs_amalkraj
https://github.com/vfcarida/Semantic-Sensitive-TF-IDF-to-Determine-Word-Relevance-in-Documents
Semantic Sensitive TF-IDF to Determine Word Relevance in Documents
https://github.com/vinairesearch/phogpt
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
https://github.com/vinhdv1628/etnlp
ETNLP: a visual-aided systematic approach to select pre-trained embeddings for a downstream task
https://github.com/vinhkhuc/JFastText
https://github.com/vt257/allnews-am
pioNER: Datasets and Baselines for Armenian Named Entity Recognition
https://github.com/vyraun/Half-Size
Simple and Effective Dimensionality Reduction for Word Embeddings
https://github.com/wang-h/neuralcompressor
Compressing Word Embeddings via Deep Compositional Code Learning
https://github.com/wangxfng/rdrec
Enhancing High-order Interaction Awareness in LLM-based Recommender Model
https://github.com/wayalhruhi/julianser
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.