diff --git a/model_cards/mrm8488/t5-base-finetuned-sarcasm-twitter/README.md b/model_cards/mrm8488/t5-base-finetuned-sarcasm-twitter/README.md
index 6483658bf43..deec38e3ffa 100644
--- a/model_cards/mrm8488/t5-base-finetuned-sarcasm-twitter/README.md
+++ b/model_cards/mrm8488/t5-base-finetuned-sarcasm-twitter/README.md
@@ -1,4 +1,4 @@
---
+---
 language: english
 ---
 
@@ -11,8 +11,7 @@ The **T5** model was presented in [Exploring the Limits of Transfer Learning wit
 
 Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
 
-![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67)
-
+![model image](https://i.imgur.com/jVFMMWR.png)
 ## Details of the downstream task (Sequence Classification as Text generation) - Dataset 📚
 
 [ Twitter Sarcasm Dataset](https://github.com/EducationalTestingService/sarcasm)
@@ -105,7 +104,7 @@ conversation = twit1 + me
 
 eval_conversation(conversation) #Output: 'derison'
 
-# We will get 'normal' when not sarcasm detected and 'derison' when detected
+# We will get 'normal' when sarcasm is not detected and 'derison' when detected
 ```
 
 > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)