Update README.md

README.md

@@ -18,14 +18,12 @@ size_categories:
 language:
 - amh
 - ary
-- ar
 - arq
 - hau
 - ibo
 - kin
 - por
 - pcm
-- eng
 - oro
 - swa
 - tir
@@ -35,8 +33,6 @@ language:
 pretty_name: AfriSenti
 ---
 
-# Dataset Card for AfriSenti Dataset
-
 <p align="center">
 <img src="https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/images/afrisenti-twitter.png", width="700" height="500">
 
@@ -50,7 +46,7 @@ pretty_name: AfriSenti
 - **Paper:**  [AfriSenti: AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://arxiv.org/pdf/2302.08956.pdf)
 - **Paper:**  [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://arxiv.org/pdf/2201.08277.pdf)
 - **Leaderboard:** N/A
-- **Point of Contact:** [shamsuddeen Muhammad]([email protected])
+- **Point of Contact:** [Shamsuddeen Muhammad]([email protected])
 
 
 ### Dataset Summary
@@ -114,16 +110,16 @@ The AfriSenti dataset has 3 splits: train, validation, and test. Below are the s
 from  datasets  import  load_dataset
 
 # you can load specific languages (e.g., Amharic). This download train, validation and test sets. 
-ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh")
+ds = load_dataset("HausaNLP/AfriSenti-Twitter", "amh")
 
 # train set only
-ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "train")
+ds = load_dataset("HausaNLP/AfriSenti-Twitter", "amh", split = "train")
 
 # test set only
-ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "test")
+ds = load_dataset("HausaNLP/AfriSenti-Twitter", "amh", split = "test")
 
 # validation set only
-ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "validation")
+ds = load_dataset("HausaNLP/AfriSenti-Twitter", "amh", split = "validation")
 
 
 ```
@@ -141,25 +137,6 @@ AfriSenti Version 1.0.0 aimed to be used in the first Afrocentric SemEval shared
 
 Twitter
 
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-
-
-[More Information Needed]
 
 ### Personal and Sensitive Information
 
@@ -172,15 +149,6 @@ We anonymized the tweets by replacing all *@mentions* by *@user* and removed all
 
 The Afrisenti dataset has the potential to improve sentiment analysis for African languages, which is essential for understanding and analyzing the diverse perspectives of people in the African continent. This dataset can enable researchers and developers to create sentiment analysis models that are specific to African languages, which can be used to gain insights into the social, cultural, and political views of people in African countries. Furthermore, this dataset can help address the issue of underrepresentation of African languages in natural language processing, paving the way for more equitable and inclusive AI technologies.
 
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
 
 ## Additional Information
 
@@ -235,9 +203,4 @@ This AfriSenti is licensed under a Creative Commons Attribution 4.0 Internationa
   journal={arXiv preprint arXiv:2304.06845},
   year={2023}
 }
-```
-
-
-### Contributions
-
-[More Information Needed]
+```