spelling

README.md

@@ -19,7 +19,7 @@ size_categories:
 
 ### Dataset Summary
 
-Multidomain Russian Spellcheck dataset is a benchmark of 1711 sentence pairs dedicated to a problem of automatic spelling correction in Russian language. Dataset is gathered along five different domains including news, Russian classic literature, social media texts, open web and strategic documents. It has been passed through two-stage manual labeling process with native speakers as annotators to correct spelling violation and preserve original style of text at the same time. 
+Multidomain Russian Spellcheck dataset is a benchmark of 1711 sentence pairs dedicated to a problem of automatic spelling correction in Russian language. The dataset is gathered from five different domains including news, Russian classic literature, social media texts, open web and strategic documents. It has been passed through two-stage manual labeling process with native speakers as annotators to correct spelling violation and preserve original style of text at the same time. 
 
 ## Dataset Structure
 
@@ -89,11 +89,11 @@ which is then splitted into following domain-relaited shards:
 #### Annotation process
 All of the sentences undergo a two-stage annotation procedure on [Toloka](https://toloka.ai), a crowd-sourcing platform for data labeling.
 
-Each stage includes an unpaid training phase with explanations, control tasks for tracking annotation quality, and the main annotation task. Before starting, the worker is given detailed instructions describing the task, explaining the labels, and showing plenty of examples.
-The instruction is available at any time during both the training and main annotation phases. To get access to the main phase, the worker should first complete the training phase by labeling more than 70% of its examples correctly. To ensure high-quality expertise on the matter of spelling, we set up additional test phase on the small portion of data, manually revised the results and approved only those annotators, who managed to avoid any mistakes.  
+Each stage includes an unpaid training phase with explanations, control tasks for tracking annotation quality, and the main annotation task. Before starting, a worker is given detailed instructions describing the task, explaining the labels, and showing plenty of examples.
+The instruction is available at any time during both the training and main annotation phases. To get access to the main phase, the worker should first complete the training phase by labeling more than 70% of its examples correctly. To ensure high-quality expertise on the matter of spelling, we set up additional test phase on a small portion of data, manually revised the results and approved only those annotators, who managed to avoid any mistakes.  
 
 - **Stage 1: Data gathering**
-We provide the texts with possible mistakes to annotators and ask them to write the sentence correctly preserving the original style-markers of the text. 
+We provide texts with possible mistakes to annotators and ask them to write the sentence correctly preserving the original style-markers of the text. 
 
 - **Stage 2: Validation**
 We provide annotators with the pair of sentences (origin and its corresponding correction from the previous stage) and ask them to check if the correction is right.