diff --git a/model_cards/mrm8488/bert-multi-uncased-finetuned-xquadv1/README.md b/model_cards/mrm8488/bert-multi-uncased-finetuned-xquadv1/README.md
index 79acde75..39368ef3 100644
--- a/model_cards/mrm8488/bert-multi-uncased-finetuned-xquadv1/README.md
+++ b/model_cards/mrm8488/bert-multi-uncased-finetuned-xquadv1/README.md
@@ -3,9 +3,9 @@ language: multilingual
 thumbnail:
 ---
 
-# BERT (base-multilingual-uncased) fine-tuned on XQuAD
+# BERT (base-multilingual-uncased) fine-tuned for multilingual Q&A
 
-This model was created by [Google](https://github.com/google-research/bert/blob/master/multilingual.md) and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) for multilingual (`11 different languages`) **Q&A** downstream task.
+This model was created by [Google](https://github.com/google-research/bert/blob/master/multilingual.md) and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) like data for multilingual (`11 different languages`) **Q&A** downstream task.
 
 ## Details of the language model('bert-base-multilingual-uncased')
 
@@ -77,19 +77,6 @@ As **XQuAD** is just an evaluation dataset, I used `Data augmentation techniques
 The model was trained on a Tesla P100 GPU and 25GB of RAM.
 The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/distillation/run_squad_w_distillation.py)
 
-## Results:
-
-| Metric    | # Value     |
-| --------- | ----------- |
-| **Exact** | **93.03** |
-| **F1**    | **94.62** |
-
-## Comparison:
-
-| Model    | Exact     | F1 score |
-| --------- | ----------- | ------- |
-| [bert-multi-cased-finetuned-xquadv1](https://huggingface.co/mrm8488/bert-multi-cased-finetuned-xquadv1) | 91.43 | 94.14 |
-|bert-multi-uncased-finetuned-xquadv1 | **93.03** | **94.62**
 
 ## Model in action