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Everything about FastText
Programming library
fastText is an open-source library developed by Facebook AI Research (FAIR) for learning word representations and performing text classification. It was publicly released in 2016. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: FastTextA 5-minute orientation
- Skim a recent paper: pNPs-CapsNet: Predicting Neuropeptides Using Protein Language Models and FastText Encoding-Based Weighted Multi-View Feature Integration with Deep Capsule Neural NetworkInfluential work since 2022
- Pick up a classic book: FastText Quick Start GuideThe most-reprinted book on the subject
- Read the landmark paper: Impact of convolutional neural network and FastText embedding on text classificationCited 167 times
- Go sideways: Tomáš MikolovA closely related topic
- Go sideways: Word2VecA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Impact of convolutional neural network and FastText embedding on text classification
Advancing Fake News Detection: Hybrid Deep Learning With FastText and Explainable AI
AIPs-SnTCN: Predicting Anti-Inflammatory Peptides Using fastText and Transformer Encoder-Based Hybrid Word Embedding with Self-Normalized Temporal Convolutional Networks
Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams
Depression and Suicide Risk Detection on Social Media using fastText Embedding and XGBoost Classifier
Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics
Combining FastText and Glove Word Embedding for Offensive and Hate speech Text Detection
Sentiment Classification Using fastText Embedding and Deep Learning Model
🆕 Influential since 2022
pNPs-CapsNet: Predicting Neuropeptides Using Protein Language Models and FastText Encoding-Based Weighted Multi-View Feature Integration with Deep Capsule Neural Network
Deepfake Detection on Social Media: Leveraging Deep Learning and FastText Embeddings for Identifying Machine-Generated Tweets
Attention-Based 1D CNN-BiLSTM Hybrid Model Enhanced with FastText Word Embedding for Korean Voice Phishing Detection
Measuring the novelty of scientific publications: A fastText and local outlier factor approach
Optimizing an English text reading recommendation model by integrating collaborative filtering algorithm and FastText classification method
📚 Books worth reading
- 📕FastText Quick Start GuideJoydeep Bhattacharjee · 2018
fastText Quick Start Guide: Get started with Facebook's library for text representation and classificationJoydeep Bhattacharjee · 2018
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Tomáš Mikolov Tomáš Mikolov is a Czech computer scientist known for his work on neural language models and word representations. He was the lead author of the 2013 paper…
- Word2Vec In natural language processing, Word2Vec is a technique for obtaining vector representations of words as word embeddings. These vectors capture information…
- Word embedding In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is…
- Morphological parsing Morphological parsing, in natural language processing, is the process of determining the morphemes from which a given word is constructed. It must be able…
- Language identification In natural language processing, language identification or language guessing is the problem of determining which natural language a given content is in…
- Representation learning In machine learning (ML), representation learning or feature learning is a set of techniques that allow a system to automatically discover the representations…
- BERT (language model) Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent…
- Semantic space Semantic spaces in the natural language domain aim to create representations of natural language that are capable of capturing meaning. The original motivation…