Update README.md

README.md

@@ -16,7 +16,7 @@ library_name: transformers
 ---
 
 # XLM-R and EXLMR Model
-
+We introduce the EXLMR model, an extension of XLM-R, which expands its tokenizer vocabulary to incorporate new languages and alleviate out-of-vocabulary (OOV) issues. We initialize the embeddings for the newly added vocabulary in a way that allows the model to leverage this newly added vocabularies effectively. Our approach not only benefits low-resource languages but also improves performance on high-resource languages, that were part of the original XLM-R model. 
 ## Model Overview
 
 The **XLM-R** (Cross-lingual Language Model - RoBERTa) is a multilingual model trained on 100 languages. The **EXLMR** (Extended XLM-RoBERTa) is an extended version designed to improve performance on low-resource languages spoken in Ethiopia, including Amharic, Tigrinya, and Afaan Oromo.
@@ -32,7 +32,7 @@ The **XLM-R** (Cross-lingual Language Model - RoBERTa) is a multilingual model t
 
 
 EXLMR addresses tokenization issues inherent to the XLM-R model, such as out-of-vocabulary (OOV) tokens and over-tokenization, especially for low-resource languages.
-Fine-tuning on specific datasets will help adapt the model to particular tasks and improve its performance.You can use this model with the `transformers` library for various NLP tasks. 
+Fine-tuning on specific datasets will help adapt the model to particular tasks and improve its performance. You can use this model with the `transformers` library for various NLP tasks. 
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
@@ -44,7 +44,7 @@ tokenizer = AutoTokenizer.from_pretrained(checkpoint)
 model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
 
 
-EXLMR has been designed with specific support for underrepresented languages, particularly those spoken in Ethiopia (such as Amharic, Tigrinya, and Afaan Oromo). Like XLM-RoBERTa, EXLMR can be finetuned to handle multiple languages simultaneously, making it effective for cross-lingual tasks such as machine translation, multilingual text classification, and question answering.EXLMR-base follows the same architecture as RoBERTa-base, with 12 layers, 768 hidden dimensions, and 12 attention heads, totaling approximately 270M parameters.
+EXLMR has been designed to support underrepresented languages, particularly those spoken in Ethiopia (such as Amharic, Tigrinya, and Afaan Oromo). Like XLM-RoBERTa, EXLMR can be finetuned to handle multiple languages simultaneously, making it effective for cross-lingual tasks such as machine translation, multilingual text classification, and question answering. EXLMR-base follows the same architecture as RoBERTa-base, with 12 layers, 768 hidden dimensions, and 12 attention heads, totaling approximately 270M parameters.
 
 |Model|Vocabulary Size|
 |---|---|