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facebookresearch/fastTextArchived

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fasttext.cc↗

FastText

fastText is a library and framework for word embedding generation, text vectorization, and supervised text classification. It provides tools to transform raw text into fixed-length vector representations and to train models that assign category labels to sentences or documents.

The system utilizes subword-based vectorization and character n-gram embeddings, allowing it to generate meaningful vectors for words that were not present during training. To manage resource usage, it includes a quantized language model implementation that employs product quantization and dimensionality reduction to decrease the memory footprint of trained models.

The project covers broader capabilities for machine learning workflows, including text classifier training, label prediction, and the generation of vectors for full sentences or paragraphs.

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Features

  • Word Embeddings - Generates mathematical vector representations of words and character n-grams to capture semantic meaning.
  • Classification Frameworks - Offers a supervised framework for assigning category labels to text using efficient linear models.
  • Text Vectorizations - Transforms raw sentences and paragraphs into fixed-length numerical vectors for machine learning.
  • Character N-Grams - Uses character n-gram embeddings to represent words and handle out-of-vocabulary terms.
  • Subword Representation Models - Decomposes words into subword units to generate vectors for unseen terms based on internal structure.
  • Out-of-Vocabulary Vector Generation - Generates word representations for terms missing from the training vocabulary using subword-based embeddings.
  • Supervised Classification - Provides supervised learning capabilities to automatically assign category labels to text documents.
  • Text Vectorization Tools - Provides tools to transform raw text into fixed-length vector representations for downstream AI models.
  • Sentence Embeddings - Transforms full paragraphs or sentences into single fixed-size vector representations.
  • Word Embedding Libraries - Provides a comprehensive library for training vectors that represent words and character n-grams.
  • Hierarchical Softmax - Implements hierarchical softmax to optimize label prediction and reduce training complexity.
  • Linear Classifiers - Employs a linear model that averages word embeddings for fast text classification.
  • Model Quantization - Reduces model memory requirements using quantization while preserving prediction functionality.
  • Negative Sampling Techniques - Uses negative sampling to update a small subset of weights, accelerating the training process.
  • Model Compression - Reduces the memory footprint of text models through quantization and dimensionality reduction.
  • Quantized Model Implementations - Implements a memory-efficient language model using quantization to reduce the overall footprint.
  • Text Classifier Training - Trains supervised models to categorize text and evaluates them using precision and recall.
  • Single-Label Prediction - Predicts the most likely labels or probabilities for text using a trained supervised model.
  • Product Quantization - Implements product quantization to compress large vector matrices into smaller codebooks.
  • Embedding Dimension Reduction - Decreases the dimensionality of pre-trained word vectors to lower the overall model footprint.
  • Frameworks And Libraries - Library for fast text representation and classification from Facebook.
  • Natural Language Processing - Efficient word representations and text classification library.
  • NLP - Efficient text classification and representation learning.
  • Text Embeddings - Library for efficient learning of word representations and classification.
26,543 stars·4,809 forks·HTML·MIT·24 views

Star history

Star history chart for facebookresearch/fasttextStar history chart for facebookresearch/fasttext

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Projects sharing features with FastText

These projects share indexed features with FastText. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • chatopera/synonymschatopera avatar

    chatopera/Synonyms

    5,107View on GitHub↗

    Synonyms is a natural language processing library and semantic similarity engine specifically designed for Chinese text. It functions as a word embedding toolkit and tokenizer that extracts semantic meaning and identifies synonyms by calculating the conceptual closeness between words and sentences. The system provides a toolkit for Chinese word embedding and synonym discovery, allowing for the retrieval of semantically similar words to expand vocabulary. It distinguishes itself through a configuration-driven approach to model loading, which supports the integration of custom word embeddings t

    Pythonaichatbotnlp
    View on GitHub↗5,107
  • johnsnowlabs/spark-nlpJohnSnowLabs avatar

    JohnSnowLabs/spark-nlp

    4,135View on GitHub↗

    Spark NLP is a toolkit for scalable text analysis and machine learning built on the Apache Spark distributed computing framework. It provides a multimodal machine learning framework and a distributed pipeline system for sequencing annotators to process large-scale linguistic data. The library includes a transformer text processor for generating contextual vector embeddings and a dedicated inference engine for managing large language models. The project distinguishes itself through its ability to process heterogeneous data types, including text, audio, and images, within a unified vision-langu

    Scala
    View on GitHub↗4,135
  • zalandoresearch/flairzalandoresearch avatar

    zalandoresearch/flair

    14,378View on GitHub↗

    Flair is a natural language processing framework for training and applying models for sequence labeling and text classification. It provides a system for generating word embeddings and identifying semantic entities within text. The framework includes a dedicated system for zero and few-shot learning, enabling text classification and entity extraction using minimal training examples by leveraging pre-trained knowledge. Its capabilities cover named entity recognition, sentiment analysis, and the training of specialized models using custom datasets. It also includes tooling for the visual highl

    Python
    View on GitHub↗14,378
  • facebookresearch/pytextfacebookresearch avatar

    facebookresearch/pytext

    6,298View on GitHub↗

    PyText is an extensible PyTorch-based framework for building, training, and deploying custom natural language processing models, including text classifiers, sequence taggers, and intent-slot predictors. It provides a modular toolkit that allows developers to assemble these models using pluggable registries for model architectures, data formats, and tensorizers, all configurable through YAML files without requiring code changes. The framework distinguishes itself through its comprehensive support for the full NLP model lifecycle, from training to production inference. It includes pre-built neu

    Python
    View on GitHub↗6,298
Compare all 30 related projects→

Frequently asked questions

What does facebookresearch/fasttext do?

fastText is a library and framework for word embedding generation, text vectorization, and supervised text classification. It provides tools to transform raw text into fixed-length vector representations and to train models that assign category labels to sentences or documents.

What are the main features of facebookresearch/fasttext?

The main features of facebookresearch/fasttext are: Word Embeddings, Classification Frameworks, Text Vectorizations, Character N-Grams, Subword Representation Models, Out-of-Vocabulary Vector Generation, Supervised Classification, Text Vectorization Tools.

Which projects share features with facebookresearch/fasttext?

Projects with overlapping indexed features include: chatopera/synonyms — Synonyms is a natural language processing library and semantic similarity engine specifically designed for Chinese… johnsnowlabs/spark-nlp — Spark NLP is a toolkit for scalable text analysis and machine learning built on the Apache Spark distributed computing… zalandoresearch/flair — Flair is a natural language processing framework for training and applying models for sequence labeling and text… facebookresearch/pytext — PyText is an extensible PyTorch-based framework for building, training, and deploying custom natural language… d2l-ai/d2l-en — This project is an educational platform and research toolkit designed to teach deep learning through a combination of… shibing624/text2vec — text2vec is a text vectorization toolkit and semantic similarity framework used to convert words and sentences into…