Description
The Skip-Thought Vectors project offers the Sent2Vec encoder and associated training code, directly derived from the research paper "Skip-Thought Vectors." This tool is designed to generate high-quality, dense vector representations for sentences. These sentence embeddings can then be utilized across a wide range of natural language processing (NLP) applications, significantly enhancing their performance.
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The implementation is primarily in Python, requiring specific dependencies such as Python 2.7, Theano 0.7, NumPy, SciPy, scikit-learn, NLTK 3, and optionally Keras and gensim for specific experiments. Users need to download pre-trained model files and word embeddings, which are substantial in size, necessitating adequate storage space. The setup involves configuring paths to these downloaded files within the `skipthoughts.py` script and setting Theano flags for device selection (CPU or GPU).
Once set up, the encoder can process lists of sentences to produce numerical vectors. The output vectors are 4800-dimensional, combining uni-skip and bi-skip models, with the combined vectors recommended for optimal performance as demonstrated in the paper's experiments. The encoding process can be controlled for verbosity, showing progress based on sentence lengths being processed.
The repository also includes code for replicating experiments detailed in the paper, covering tasks such as TREC Question-Type Classification, Image-Sentence Ranking (using COCO dataset), Semantic-Relatedness (SICK dataset), Paraphrase Detection (Microsoft Research Paraphrase Corpus), and various binary classification benchmarks (MR, CR, SUBJ, MPQA). These experimental scripts guide users on data preparation and execution to achieve reported results, often involving cross-validation for hyperparameter tuning.
The project emphasizes the importance of an End-of-Sentence (EOS) token, which can be optionally used during encoding to potentially improve performance, especially for sentences lacking standard punctuation. The code is released under the Apache License 2.0, and users are encouraged to cite the relevant research papers if they find the code useful.
- CategoryNatural Language Processing Tools
- Pricing ModelOpen source
- DeploymentSelf-hosted
- PlatformsLinux, macOS, Windows
Skip-Thought Vectors's Core Features
Sent2Vec encoder implementation
Training code from 'Skip-Thought Vectors' paper
Generates dense sentence vector representations
Supports uni-skip and bi-skip models
Recommended combine-skip vectors for best performance
Includes code for TREC classification experiments
Includes code for Image-Sentence Ranking experiments
Includes code for Semantic-Relatedness experiments
Includes code for Paraphrase Detection experiments
Includes code for binary classification benchmarks
Optional EOS token usage for encoding
Python-based implementation
Getting Started with Skip-Thought Vectors
Download model files and word embeddings
Configure paths to downloaded files in skipthoughts.py
Set THEANO_FLAGS for device selection (CPU/GPU)
Import skipthoughts and load the model
Initialize the encoder with the loaded model
Encode lists of sentences to obtain vectors
Run provided scripts for specific NLP experiments
Skip-Thought Vectors's Use Cases
- Sentence Embedding Generation
- Text Classification
- Image-Sentence Ranking
- Semantic Similarity
- Paraphrase Detection
- Binary Classification
