Abstract is: fastText is a library for learning of word embeddings and text classification created by Facebook's AI Research (FAIR) lab. The model allows one to create an unsupervised learning or supervised learning algorithm for obtaining vector representations for words. Facebook makes available pretrained models for 294 languages. Several papers describe the techniques used by fastText.
| free software | Q341 |
| natural language processing software | Q110509708 |
| Python package | Q29642950 |
| embedding | Q980509 |
| P4162 | AUR package | fasttext |
| P9100 | GitHub topic | fasttext |
| P7502 | Golden ID | FastText |
| P2671 | Google Knowledge Graph ID | /g/11hbpdhgpz |
| P8443 | Homebrew formula name | fasttext |
| P856 | official website | https://research.fb.com/fasttext/ |
| P5568 | PyPI project | fasttext |
| P3417 | Quora topic ID | fastText |
| P6931 | Repology project name | fasttext |
| P1324 | source code repository URL | https://github.com/facebookresearch/fastText |
| P275 | copyright license | BSD licenses | Q191307 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P1343 | described by source | Enriching Word Vectors with Subword Information | Q28775150 |
| Bag of Tricks for Efficient Text Classification | Q28942761 | ||
| P178 | developer | Facebook AI Research | Q28943651 |
| P366 | has use | machine learning | Q2539 |
| word embedding | Q18395344 | ||
| P277 | programmed in | C++11 | Q1061570 |
| P577 | publication date | 2016-07-21 | |
| P348 | software version identifier | 0.9.2 | |
| P1476 | title | fastText |
| Q47001695 | Advances in Pre-Training Distributed Word Representations |
| Q50359881 | Annotation Artifacts in Natural Language Inference Data |
| Q118823928 | Assessing the impact of Word Embeddings for Relation Prediction: An Empirical Study |
| Q62101883 | Combining embedding methods for a word intrusion task |
| Q70188422 | Combining embedding methods for a word intrusion task |
| Q70452381 | Deep learning for Free Indirect Representation |
| Q100324073 | Dict2vec : Learning Word Embeddings using Lexical Dictionaries |
| Q113503382 | Distilling Relation Embeddings from Pre-trained Language Models |
| Q43091995 | Fast Linear Model for Knowledge Graph Embeddings |
| Q113531353 | Integrating Knowledge Graph embedding and pretrained Language Models in Hypercomplex Spaces |
| Q49985142 | Learning Word Vectors for 157 Languages |
| Q114453727 | Relative representations enable zero-shot latent space communication |
| Q62228615 | Synapse at CAp 2017 NER challenge: Fasttext CRF |
| Q86591666 | Towards Detection of Subjective Bias using Contextualized Word Embeddings |
| Q70202796 | Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning |
| Q29124331 | Analysis and Optimization of fastText Linear Text Classifier |
| Q28942761 | Bag of Tricks for Efficient Text Classification |
| Q28775150 | Enriching Word Vectors with Subword Information |
| Q43091995 | Fast Linear Model for Knowledge Graph Embeddings |
| Q30713354 | FastText.zip: Compressing text classification models |
| Q49985142 | Learning Word Vectors for 157 Languages |
| Q96210591 | Software for Creating and Analyzing Semantic Representations |
| Q62228615 | Synapse at CAp 2017 NER challenge: Fasttext CRF |
| Q106201926 | Weaviate | based on | P144 |
| Q42309704 | Using Word Embeddings for Search in Linked Data with Ontodia | uses | P2283 |
| FastText | wikipedia | |
| Persian (fa / Q9168) | فستتکست | wikipedia |
| 패스트텍스트 | wikipedia | |
| FastText | wikipedia |
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