all-mpnet-base-v2
A all-mpnet-base-v2 is a SentenceTransformers pre-trained model (pre-trained sentence embedding model for SentenceTransformers) that maps sentences and paragraphs to a 768-dimensional sentence embedding.
- Context:
- It can (typically) have Model Size of: ~420 MB.
- It can (typically) produce embeddings in milliseconds.
- It can (typically) produce 768-dimensional dense vectors.
- It can (typically) handle a 384-token maximum sequence length.
- It can support usage both with and without the sentence-transformers package, providing flexibility in implementation.
- It can be trained using a self-supervised contrastive learning objective on a dataset comprising over 1 billion sentence pairs.
- It can be fine-tuned from the pretrained microsoft/mpnet-base model (to enhance its sentence embedding capabilities).
- It can have been developed during a community week using JAX/Flax for NLP & CV.
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- Example(s):
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- Counter-Example(s):
- See: Contrastive Learning Objective, Dense Vector Space, Flax Library, Sentence Pair Encoding.
References
2024
- https://huggingface.co/sentence-transformers/all-mpnet-base-v2
- NOTES:
- It is a model from the sentence-transformers library, designed to map sentences and paragraphs to a 768-dimensional dense vector space.
- It supports usage both with and without the sentence-transformers package, providing flexibility in implementation.
- It is trained using a self-supervised contrastive learning objective on a dataset comprising over 1 billion sentence pairs.
- It is fine-tuned from the pretrained microsoft/mpnet-base model to enhance its sentence embedding capabilities.
- It uses a variety of datasets for training, including Reddit comments, citation pairs, and question-answer pairs, amounting to a diverse training background.
- It is intended for use in tasks like clustering, semantic search, and information retrieval by encoding sentences into semantically meaningful vectors.
- It was developed during a community week using JAX/Flax for NLP & CV, showcasing collaboration with Google's Flax, JAX, and Cloud teams for efficient deep learning framework utilization.
- NOTES: