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README.md

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+## 🧐 About <a name = "about"></a>
+
+tunbert_zied is language model for the tunisian dialect based on a similar architecture to the RoBERTa model created BY zied sbabti.
+
+The model was trained for over 600 000 phrases written in the tunisian dialect. 
+
+## 🏁 Getting Started <a name = "getting_started"></a>
+
+Load <strong>tunbert_zied</strong> and its sub-word tokenizer
+
+Don'use the <em>AutoTokenizer.from_pretrained(...)</em> method to load the tokenizer, instead use <em>BertTokeinzer.from_pretrained(...)</em> method. (this is because I haven't use the bultin tokenizer of roberta model which is the GPT tokenizer, instead i have used BertTokenizer)
+
+### Example
+
+
+
+```
+import transformers as tr
+
+tokenizer = tr.BertTokenizer.from_pretrained("ziedsb19/tunbert_zied")
+
+model = tr.AutoModelForMaskedLM.from_pretrained("ziedsb19/tunbert_zied")
+
+pipeline = tr.pipeline("fill-mask", model= model, tokenizer=tokenizer)
+
+#test the model by masking a word in a phrase with [MASK]
+
+pipeline("Ahla winek [MASK] lioum ?")
+
+#results 
+"""
+[{'sequence': 'ahla winek cv lioum?',
+  'score': 0.07968682795763016,
+  'token': 869,
+  'token_str': 'c v'},
+ {'sequence': 'ahla winek enty lioum?',
+  'score': 0.06116843968629837,
+  'token': 448,
+  'token_str': 'e n t y'},
+ {'sequence': 'ahla winek ch3amla lioum?',
+  'score': 0.057379286736249924,
+  'token': 7342,
+  'token_str': 'c h 3 a m l a'},
+ {'sequence': 'ahla winek cha3malt lioum?',
+  'score': 0.028112901374697685,
+  'token': 4663,
+  'token_str': 'c h a 3 m a l t'},
+ {'sequence': 'ahla winek enti lioum?',
+  'score': 0.025781650096178055,
+  'token': 436,
+  'token_str': 'e n t i'}]
+"""
+```
+
+## ✍️ Authors <a name = "authors"></a>
+
+- [zied sbabti](https://www.linkedin.com/in/zied-sbabti-a58a56139) - Idea & Initial work
+