From 878c94570b237ae01aaa5d096f2ec3e429fce161 Mon Sep 17 00:00:00 2001 From: David Berenstein Date: Wed, 10 May 2023 09:59:12 +0200 Subject: [PATCH] Apply suggestions from code review Co-authored-by: Basile Dura --- website/meta/universe.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/meta/universe.json b/website/meta/universe.json index 3d12a98a1..09b48b4ff 100644 --- a/website/meta/universe.json +++ b/website/meta/universe.json @@ -2814,7 +2814,7 @@ "id": "adeptaugmentations", "title": "Adept Augmentations", "slogan": " A Python library aimed at dissecting and augmenting NER training data for a few-shot scenario.", - "description": "EntitySwapAugmenter takes either a datasets.Dataset or a spacy.tokens.DocBin. Additionally, it is optional to provide a set of labels. It initially creates a knowledge base of entities belonging to a certain label. When running augmenter.augment() for N runs, it then creates N new sentences with random swaps of the original entities with an entity of the same corresponding label from the knowledge base. For example, assuming that we have knowledge base for PERSONS, LOCATIONS and PRODUCTS. We can then create additional data for the sentence \"Momofuko Ando created instant noodles in Osaka.\" using augmenter.augment(N=2), resulting in \"David created instant noodles in Madrid.\" or \"Tom created Adept Augmentations in the Netherlands\".", + "description": "EntitySwapAugmenter takes either a `datasets.Dataset` or a `spacy.tokens.DocBin`. Additionally, it is optional to provide a set of labels. It initially creates a knowledge base of entities belonging to a certain label. When running `augmenter.augment()` for N runs, it then creates N new sentences with random swaps of the original entities with an entity of the same corresponding label from the knowledge base.\n\nFor example, assuming that we have knowledge base for `PERSONS`, `LOCATIONS` and `PRODUCTS`. We can then create additional data for the sentence \"Momofuko Ando created instant noodles in Osaka.\" using `augmenter.augment(N=2)`, resulting in \"David created instant noodles in Madrid.\" or \"Tom created Adept Augmentations in the Netherlands\".", "github": "davidberenstein1957/adept-augmentations", "pip": "adept-augmentations", "thumb": "https://raw.githubusercontent.com/Pandora-Intelligence/crosslingual-coreference/master/img/logo.png", @@ -2825,7 +2825,7 @@ "from adept_augmentations import EntitySwapAugmenter", "", "TRAIN_DATA = [", - " \"Apple is looking at buying U.K. startup for $1 billion\"", + " \"Apple is looking at buying U.K. startup for $1 billion\",", " \"Microsoft acquires GitHub for $7.5 billion\"", "]", "docs = nlp.pipe(TRAIN_DATA)",