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74 lines
3.8 KiB
Markdown
74 lines
3.8 KiB
Markdown
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<!---
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title: "Glossary"
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metaTitle: "Glossary"
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metaDescription: ""
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slug: "/docs/terms"
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date: "2020-09-03"
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id: "termsmd"
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--->
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# Glossary
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**BERT** - A popular, transformer based language model which has been improved upon but is still considered a common benchmark.
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**Dense** - Vectors that contain many non-zero values are considered dense.
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Retrieval methods can also be called dense if they create dense vector representations of documents.
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**Document** - A Document in Haystack refers to the individual pieces of text that are stored in the DocumentStore.
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Multiple Documents might originally come from the one file.
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It is ultimately up to you how to divide up your corpus into Documents.
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**Document Store** - The component in Haystack that stores the text documents and their metadata.
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Can have a variety of backends such as Elasticsearch, SQL or FAISS.
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**FARM** - An open-source transfer learning [framework](https://github.com/deepset-ai/FARM) by deepset.
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FARM’s question answering models are used in Haystack’s Readers.
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**Finder** - The component in Haystack that connects the Retriever to the Reader.
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<div class="recommendation">
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**Tip:** The Finder class is being deprecated and has been replaced by a more powerful [Pipelines class](/docs/latest/pipelinesmd).
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</div>
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**Indexing** - To store data in a database in a way that optimises retrieval time.
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The exact steps involved in indexing depend on what kind of retrieval method is chosen.
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**Language Model** - The component in an NLP model that stores general language understanding, but no task specific knowledge.
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**Model Hub** - The [repository](https://huggingface.co/models) set up by HuggingFace where trained models can be saved to and loaded from.
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With Haystack, you can directly load and use any question answering model found on the model hub.
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**Neural Network** - A machine learning architecture composed of artificial neurons that learn a task when exposed to labelled training data.
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**Prediction Head** - The modelling component that adapts the general knowledge of the language model for a specific task.
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In question answering models (and hence in Haystack Readers), this is usually a single layer neural network.
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**Querying** - The task of returning relevant documents from a database.
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**Question Answering (QA)** - A popular task in the world of NLP where systems have to find answers to questions.
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The term is generally used to refer to extractive question answering,
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where a system has to find the minimal text span in a given document that contains the answer to the question.
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Note however, that it may also refer to abstractive question answering or FAQ matching.
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**Reader** - The component in Haystack that does the closest reading of a document to extract
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the exact text which answers a question.
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It is, at its core, a trained Question Answering model.
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**Retriever** - A lightweight filter that selects only the most relevant documents for the Reader to further process.
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**Semantic Search** - A style of search that relies not on the matching of exact string forms
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but on the similarity of meaning between a query and a piece of text.
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**Sparse** - Vectors that are composed primarily of zeros are called sparse.
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Retrieval methods are also considered sparse if they build sparse vector representations of documents.
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**SQuAD** - The [Stanford Question Answering Dataset](https://rajpurkar.github.io/SQuAD-explorer/) is the defacto standard QA dataset.
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The documents are paragraphs from Wikipedia and the question / answer pairs are created by human annotators.
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**Transformers** - Originally refers to the deep learning architecture that is composed of stacked self-attention layers
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(first conceptualised [here](https://arxiv.org/pdf/1706.03762.pdf)).
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Can also refer to HuggingFace’s [repository](https://github.com/huggingface/transformers)
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which contains implementations of popular model architectures.
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