FiftyOne Embeddings Combined — Embeding dataset for semantic search
FiftyOne Embeddings Combined is a dataset bringing together textual embeding vectors from different sources, intended to facilitate semantic and similarity research tasks in textual corpora.
Several thousand examples, numerical vectors of embedings in JSON format
Apache 2.0
Description
The dataset FiftyOne Embeddings Combined contains embedding vectors generated by several models on various original datasets. Each entry associates a text query with its embedding, as well as a response and a content type.
What is this dataset for?
- Perform semantic searches by similarity in textual corpora
- Test and compare embeding and information retrieval models
- Train or evaluate NLP systems using embeding vectors
Can it be enriched or improved?
Yes, it is possible to add new embedings calculated with other models or on other sources, or to integrate additional annotations on the relevance of the answers.
🔎 In summary
🧠 Recommended for
- NLP developers
- Information researchers
- AI teams
🔧 Compatible tools
- NumPy
- SciPy
- PyTorch
- TensorFlow
- Hugging Face Datasets
💡 Tip
Use this dataset to quickly build powerful semantic similarity search engines.
Frequently Asked Questions
What types of data does this dataset contain?
Numeric embeding vectors associated with textual requests and responses.
How do I use embedings for semantic research?
By calculating the cosine distance between vectors, we can find the texts that are most similar to a given query.
Is this dataset suitable for training models?
It is mainly used for evaluation and research, but can be used indirectly for fine-tuning.
