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Seal. 1,632,826 likes · 2,477 talking about this. http://seal.com/.
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- Overview
- Changelog
- Introduction
- The FM-index
- Install
- Download
- Retrieval
- Constrained decoding
- Licence
This repo hosts the code for our paper, SEAL.
https://arxiv.org/abs/2204.10628
UPDATE! (05/22/2022) Preprocessing/training scripts added!
We propose a approach to retrieval that uses guided LM decoding to search for occurrences of ngrams of any size in an arbitrary large collection of documents. Constrained decoding blocks the generation of ngrams that never appear in the corpus: generated ngrams are always grounded in one or multiple documents in the retrieval corpus. Documents are then scored by aggregating the scores for individual generated "identifiers".
We use the Ferragina Manzini index (FM-index), an opportunistic, compressed suffix array as the unified data structure for constrained decoding, retrieval and full-text storage.
You can think of the FM-index as a trie that not indexes not only a set of strings s, but the union of every substring of each string. We can perform constrained decoding of ngrams of unbounded length from any point in the retrieval corpus, from simple unigrams to entire sentences.
Our implementation relies on sdsl-lite.
SEAL needs a working installation of SWIG, e.g. (on Ubuntu):
We also assume that pytorch is already available in your environment. SEAL has been tested with version 1.11.
Clone this repo with --recursive so that you also include the submodule in res/external.
Compile and install sdsl-lite:
Install other dependencies:
Now install this library.
We make available both model checkpoints and pre-built indices for both Natural Questions and the KILT benchmark:
•Natural Questions (SEAL_NQ.tar.gz)
Command-line interface
To run prediction, launch the following command: The script will generate the DPR prediction file output.json. The kilt format is also supported.
The Searcher class
Our codebase relies on a pyserini-like searcher class, that incapsulates both constrained decoding and retrieval. You can use it programmatically:
Building the FM-index (CLI)
To most straightforward way to build the FM-index is to use the script we have provided in scripts/build_fm_index.py! You only need to put your retrieval corpus in a very simple TSV format as in the following example: Fields are: •document id •document title •text Then you can build the FM-index with: The parameter --jobs only speeds up the tokenization at the moment. --include_title only makes sense if your retrieval corpus has non-empty titles.
Building the FM-index (Python)
Check out seal/fm_index.py!
Decoding with the FM-index
You can easily plug in our constrained decoding code in your project by using the fm_index_generate function. In the following snippet we show a use case beyond retrieval: paraphrase mining.
SEAL is licensed under the CC-BY-NC 4.0 license. The text of the license can be found here.
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Seal. 1,768,485 likes · 82,264 talking about this. http://seal.com/.
Seal ist ein britischer Sänger. Seine bekanntesten Songs sind Killer, Crazy und Kiss from a Rose.
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