Update README.md

README.md

@@ -22,7 +22,7 @@ First versions of our models are all trained on our own dataset called **Divan**
 
 
 # Use Model
-You can easily access the models using the sample code provided in the below.
+You can easily access the models using the sample code provided below.
 
 ```python
 from transformers import AutoTokenizer, AutoModelForMaskedLM, FillMaskPipeline
@@ -48,7 +48,7 @@ print(result[0])
 
 # Results
 
-The **Shiraz** is evaluated on three downstream NLP tasks comprising **NER**, **Sentiment Analysis**, and **Emotion Detection** . Shiraz is considerably faster, and its accuracy remains highly competitive without compromising much on speed. According to [**MobileBERT paper**](https://arxiv.org/pdf/2004.02984.pdf), this model is 4.3× smaller and 5.5× faster than BERT-base. 
+The **Shiraz** is evaluated on three downstream NLP tasks comprising **NER**, **Sentiment Analysis**, and **Emotion Detection**. Shiraz is considerably faster, and its accuracy remains highly competitive without compromising much on speed. According to [**MobileBERT paper**](https://arxiv.org/pdf/2004.02984.pdf), this model is 4.3× smaller and 5.5× faster than BERT-base. 
 
 
 Obvious from the table below, you can find the colab codes for each task to use as a tutorial besides the macro F1 score. 
@@ -71,6 +71,14 @@ Obvious from the table below, you can find the colab codes for each task to use
     <td class="tg-c3ow"> Snappfood </td>
     <td class="tg-c3ow"> Arman </td>
   </tr>
+  <tr>
+    <td class="tg-0pky">lifeweb-ai/tehran</td>
+    <td class="tg-c3ow"> **72%** <br>
+    <td class="tg-c3ow"> **91%** <br>
+    <td class="tg-c3ow"> **64%** <br>
+    <td class="tg-c3ow"> **89%** <br>
+    <td class="tg-c3ow"> **76%** <br>
+  </tr>
   <tr>
     <td class="tg-0pky">LifeWeb-ai/Shiraz</td>
     <td class="tg-c3ow"> 68% <br><a href="https://colab.research.google.com/drive/15PUAGy9MUSBO3LPdMJ4h9DVKibREv9oY"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab Code" width="87" height="15"></td>
@@ -124,7 +132,7 @@ You are welcome to use our LM models in your work or research, if so, we kindly
 # Contributors
 
 - Mehrdad Azizi: [**Linkedin**](https://www.linkedin.com/in/mehrdad-azizi-50839489/), [**Github**](https://github.com/mehrazi)
-- Reza Salehi: [**Linkedin**](https://www.linkedin.com/in/reza-salehi-chegeni-6988ba271/), [**Github**](https://github.com/rezasalehichegeni)
+- Reza Salehi Chegeni: [**Linkedin**](https://www.linkedin.com/in/reza-salehi-chegeni-6988ba271/), [**Github**](https://github.com/rezasalehichegeni)
 - Parisa Mousavi: [**Linkedin**](https://www.linkedin.com/in/seyede-parisa-mousavi/), [**Github**](https://github.com/Mousavi-Parisa)
 - Iman Hashemi: [**Linkedin**](https://www.linkedin.com/in/iman-hashemi-403738a5), [**Github**](https://github.com/hashemiiman)
 - Lifeweb: [**HuggingFace**](https://huggingface.co/lifeweb-ai), [**Official Website**](https://lifewebco.com/), [**Linkedin**](https://www.linkedin.com/company/lifewebir/mycompany/)