Update README.md

README.md

@@ -39,10 +39,6 @@ _[If you wanted to join our community to keep up with news, models and datasets
     - [Source Data](#source-data)
       - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
     - [Personal and Sensitive Information](#personal-and-sensitive-information)
-  - [Considerations for Using the Data](#considerations-for-using-the-data)
-    - [Social Impact of Dataset](#social-impact-of-dataset)
-    - [Discussion of Biases](#discussion-of-biases)
-    - [Other Known Limitations](#other-known-limitations)
   - [Additional Information](#additional-information)
     - [Dataset Curators](#dataset-curators)
     - [Licensing Information](#licensing-information)
@@ -156,35 +152,20 @@ We used a preprocessor based on some stream-based Linux kernel commands so that
 
 ### Personal and Sensitive Information
 
-State whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).
+Since this corpus is briefly a compilation of some former corpora we take no responsibility for personal information included in this corpus. If you detect any of these violations please let us know, we try our best to remove them from the corpus ASAP.
 
-State whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).  
-
-If efforts were made to anonymize the data, describe the anonymization process.
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-Farsi is a language used by millions of people, for thousands of years; therefore, there exists numerous resources for this language. However, no-one has ever published a big enough easy to use corpus of textual data. Our dataset eases the path of pre-training and fine-tuning Farsi Language Models (LMs) in self-supervised manner which can lead to better tools for retention and development of Farsi. As a matter of fact, the informal portion of naab contains various dialects including, Turkish, Luri, etc. which are under-represented languages. Although the amount of data is comparably small, but it can be helpful in training a multi-lingual Tokenizer for Farsi variations. As mentioned before, some parts of our dataset are crawled from social media which in result means it contains ethnic, religious, and gender biases.
-
-### Discussion of Biases
-
-During Exploratory Data Analysis (EDA), we found samples of data including biased opinions about race, religion, and gender. Based on the result we saw in our samples, only a small portion of informal data can be considered biased. Therefore, we anticipate that it won’t affect the trained language model on this data. Furthermore, we decided to keep this small part of data as it may become helpful in training models for classifying harmful and hateful texts.
-
-### Other Known Limitations
-
-If studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.
+We tried our best to provide anonymously while keeping the crucial information. We shuffled some parts of the corpus so the information passing through possible conversations wouldn't be harmful. 
 
 ## Additional Information
 
 ### Dataset Curators
 
-List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
++ Sadra Sabouri (Sharif University of Technology)
++ Elnaz Rahmati (Sharif University of Technology)
 
 ### Licensing Information
 
-Provide the license and link to the license webpage if available.
+mit?
 
 ### Citation Information