Update README.md

README.md

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 # Model Details
 
-TukaBERT models are a family of encoder models trained on Persian in two sizes base and large. These Models pre-trained on over 300GB of Persian data including a variety of topics such as News, Blogs, Forums, Books, etc. They pre-trained with the MLM (WWM) objective using two context lengths.
+TookaBERT models are a family of encoder models trained on Persian in two sizes base and large. These Models pre-trained on over 300GB of Persian data including a variety of topics such as News, Blogs, Forums, Books, etc. They pre-trained with the MLM (WWM) objective using two context lengths.
 
 ## How to use
 
@@ -45,15 +45,18 @@ inference_pipeline("شهر برلین در کشور <mask> واقع شده اس
 
 You can use this model to fine-tune it over your dataset and prepare it for your task.
 
+- DeepSentiPers (Sentiment Analysis) <a href="https://colab.research.google.com/drive/1Vn5QTYutdCo6iXVTmsPW9K4t8xVk14ji#scrollTo=1B1YrypZxajF"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab Code" width="87" height="15"/></a>
+- ParsiNLU - Multiple-choice (Multiple-choice) <a href="https://colab.research.google.com/drive/1boXMnRIwqAYGU7oxJtRjgib7Fu-O--x5#scrollTo=7jVb9E4SDPNb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab Code" width="87" height="15"/></a>
+
 ## Evaluation
 
-TukaBERT models are evaluated on a wide range of NLP downstream tasks, such as Sentiment Analysis (SA), Text Classification, Multiple-choice, Question Answering, and Named Entity Recognition (NER).
+TookaBERT models are evaluated on a wide range of NLP downstream tasks, such as Sentiment Analysis (SA), Text Classification, Multiple-choice, Question Answering, and Named Entity Recognition (NER).
 Here are some key performance results:
 
 | Model name       | DeepSentiPers (f1/acc) | MultiCoNER-v2 (f1/acc) | PQuAD (best_exact/best_f1/HasAns_exact/HasAns_f1)   | FarsTail (f1/acc)  | ParsiNLU-Multiple-choice (f1/acc) | ParsiNLU-Reading-comprehension (exact/f1) | ParsiNLU-QQP (f1/acc) |
 |------------------|------------------------|------------------------|-----------------------------------------------------|--------------------|-----------------------------------|-------------------------------------------|-----------------------|
-| TukaBERT-large   | **85.66/85.78**        | **69.69/94.07**        | **75.56/88.06/70.24/87.83**                         | **89.71/89.72**    | **36.13/35.97**                   | **33.6/60.5**                             | **82.72/82.63**       |
-| TukaBERT-base    | <u>83.93/83.93</u>     | <u>66.23/93.3</u>      | <u>73.18</u>/<u>85.71</u>/<u>68.29</u>/<u>85.94</u> | <u>83.26/83.41</u> | 33.6/<u>33.81</u>                 | 20.8/42.52                                | <u>81.33/81.29</u>    |
+| TookaBERT-large  | **85.66/85.78**        | **69.69/94.07**        | **75.56/88.06/70.24/87.83**                         | **89.71/89.72**    | **36.13/35.97**                   | **33.6/60.5**                             | **82.72/82.63**       |
+| TookaBERT-base   | <u>83.93/83.93</u>     | <u>66.23/93.3</u>      | <u>73.18</u>/<u>85.71</u>/<u>68.29</u>/<u>85.94</u> | <u>83.26/83.41</u> | 33.6/<u>33.81</u>                 | 20.8/42.52                                | <u>81.33/81.29</u>    |
 | Shiraz           | 81.17/81.08            | 59.1/92.83             | 65.96/81.25/59.63/81.31                             | 77.76/77.75        | <u>34.73/34.53</u>                | 17.6/39.61                                | 79.68/79.51           |
 | ParsBERT         | 80.22/80.23            | 64.91/93.23            | 71.41/84.21/66.29/84.57                             | 80.89/80.94        | **35.34/35.25**                   | 20/39.58                                  | 80.15/80.07           |
 | XLM-V-base       | <u>83.43/83.36</u>     | 58.83/92.23            | <u>73.26</u>/<u>85.69</u>/<u>68.21</u>/<u>85.56</u> | 81.1/81.2          | **35.28/35.25**                   | 8/26.66                                   | 80.1/79.96            |