Update README.md

README.md

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 # AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages
 
-This repository contains the dataset for our paper `AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages` which will appear at the third Simple and Efficient Natural Language Processing, at EMNLP 2022.
+This repository contains the dataset for our paper 'AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages` which will appear at the third Simple and Efficient Natural Language Processing, at EMNLP 2022.
 
 ## Our self-active learning framework
 ![Model](afrolm.png)
@@ -9,7 +9,7 @@ This repository contains the dataset for our paper `AfroLM: A Self-Active Learni
 AfroLM has been pretrained from scratch on 23 African Languages: Amharic, Afan Oromo, Bambara, Ghomalá, Éwé, Fon, Hausa, Ìgbò, Kinyarwanda, Lingala, Luganda, Luo, Mooré, Chewa, Naija, Shona, Swahili, Setswana, Twi, Wolof, Xhosa, Yorùbá, and Zulu.
 
 ## Evaluation Results
-AfroLM was evaluated on MasakhaNER1.0 (10 African Languages) and MasakhaNER2.0 (21 African Languages) datasets; on text classification and sentiment analysis. AfroLM outperformed AfriBERTa, mBERT, and XLMR-base, and was very competitive with AfroXLMR. AfroLM is also very data efficient because it was pretrained on a dataset 14x+ smaller than its competitors' datasets. Below is the average of performance of various models, across various datasets. Please consult our paper for more language-level performance.
+AfroLM was evaluated on MasakhaNER1.0 (10 African Languages) and MasakhaNER2.0 (21 African Languages) datasets, on text classification and sentiment analysis. AfroLM outperformed AfriBERTa, mBERT, and XLMR-base, and was very competitive with AfroXLMR. AfroLM is also very data efficient because it was pretrained on a dataset 14x+ smaller than its competitors' datasets. Below is the average performance of various models across various datasets. Please consult our paper for more language-level performance.
 
 Model | MasakhaNER | MasakhaNER2.0* | Text Classification (Yoruba/Hausa) | Sentiment Analysis (YOSM) | OOD Sentiment Analysis (Twitter -> YOSM) |
 |:---: |:---: |:---: | :---: |:---: | :---: |
@@ -19,7 +19,7 @@ Model | MasakhaNER | MasakhaNER2.0* | Text Classification (Yoruba/Hausa) | Senti
 `XLMR-base` | 79.16 | 83.09 | --- | --- | --- |
 `AfroXLMR-base` | `81.90` | `84.55` | --- | --- | --- |
 
-- (*) The evaluation was made on the 11 additional languages of the dataset.
+-(*) The evaluation was conducted on the 11 additional languages of the dataset.
 - Bold numbers represent the performance of the model with the **smallest pretrained data**.
 ## Pretrained Models and Dataset
 
@@ -42,7 +42,7 @@ tokenizer.model_max_length = 256
     
 
 ## Citation
-We will share the proceeding citation as soon as possible. Stay tuned, and if you have liked our work, give it a star.
+We will share the proceeding citation as soon as possible. Stay tuned, and if you like our work, give it a star.
 
 ## Reach out