Behpouyan
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Behpouyan-Fill-Mask

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 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags: [Fill Mask, Persian , BERT]
 ---
 ## Model Details
 ### Model Description
+This model is fine-tuned for the task of masked language modeling in Persian. The model can predict missing words in Persian sentences when a word is replaced by the [MASK] token. It is useful for a range of NLP applications, including text completion, question answering, and contextual understanding of Persian texts.
+- **Developed by:** Behpouyan
+- **Model type:** Encoder
+- **Language(s) (NLP):** Persian
 ### Direct Use
+The model is intended to be used directly for the task of predicting the most likely word for a masked token in Persian sentences. By simply providing a sentence with a masked word (e.g., <mask>), users can leverage the model for text completion, semantic prediction, and contextual understanding.
 ## How to Get Started with the Model
+``` python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+import torch
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Behpouyan/Behpouyan-Fill-Mask")
+model = AutoModelForMaskedLM.from_pretrained("Behpouyan/Behpouyan-Fill-Mask")
+# List of 5 Persian sentences with a masked word (replacing a word with [MASK])
+sentences = [
+    "این کتاب بسیار <mask> است.",  # The book is very [MASK]
+    "مشتری همیشه از <mask> شما راضی است.",  # The customer is always satisfied with your [MASK]
+    "من به دنبال <mask> هستم.",  # I am looking for [MASK]
+    "این پروژه نیاز به <mask> دارد.",  # This project needs [MASK]
+    "تیم ما برای انجام کارها <mask> است."  # Our team is [MASK] to do the tasks
+]
+# Function to predict masked words
+def predict_masked_word(sentence):
+    # Tokenize the input sentence
+    inputs = tokenizer(sentence, return_tensors="pt")
+    # Forward pass to get logits
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    # Get the position of the [MASK] token
+    mask_token_index = torch.where(inputs.input_ids == tokenizer.mask_token_id)[1].item()
+    # Get the predicted token
+    predicted_token_id = torch.argmax(logits[0, mask_token_index]).item()
+    predicted_word = tokenizer.decode([predicted_token_id])
+    return predicted_word
+# Test the model on the sentences
+for sentence in sentences:
+    predicted_word = predict_masked_word(sentence)
+    print(f"Sentence: {sentence}")
+    print(f"Predicted word: {predicted_word}")
+    print("-" * 50)
+```