---
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


## 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)
```