EzioDevio/iraqi_dialect_llm

Here’s a sample README.md file for your iraqi_dialect_llm repository, including descriptions, usage instructions, and an example. Following this, I’ll show you how to set up a demo using Gradio to interact with the model.

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README.md

# Iraqi Dialect Language Model

This model is fine-tuned to generate text in the Iraqi Arabic dialect, making it suitable for applications such as text generation, conversational agents, and language-specific tasks in Iraqi Arabic. It has been trained on various sources, including Iraqi proverbs, dialogues, and other dialect-specific content.

## Model Overview

- **Model Name**: Iraqi Dialect LLM
- **Base Model**: [aubmindlab/aragpt2-base](https://huggingface.co/aubmindlab/aragpt2-base)
- **Language**: Iraqi Arabic
- **Task**: Text generation, sentiment analysis, and general sentence construction in Iraqi dialect

## Example Usage

Below is an example of how to load and generate text with this model using Python and Hugging Face's `transformers` library.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("EzioDevio/iraqi_dialect_llm")
model = AutoModelForCausalLM.from_pretrained("EzioDevio/iraqi_dialect_llm")

# Define a prompt
prompt = "شلونك اليوم؟"

# Encode the input
inputs = tokenizer.encode(prompt, return_tensors="pt")

# Generate text
outputs = model.generate(inputs, max_length=50)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Generated text:", generated_text)

Training Data

The model was trained on a collection of Iraqi Arabic texts, including proverbs, informal dialogues, and conversational text. The dataset has been preprocessed to normalize dialectal variations and remove any diacritics.

How to Use the Model

1. Install Dependencies: Make sure you have transformers and torch installed:

   pip install transformers torch
   

2. Load the Model: Use the provided example code to load and use the model for text generation in Iraqi Arabic.

3. Demo: A live demo is available on Hugging Face Spaces here for generating text with the model interactively.

Limitations

While the model performs well for conversational Iraqi Arabic, it may struggle with highly specific terminology or niche dialectal variations not covered in the training data.

License

This model is released under the Apache 2.0 License.

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### Creating a Gradio Demo for Hugging Face Spaces

To create a demo using Gradio on Hugging Face Spaces, follow these steps:

1. **Prepare the App Code**:

   Create a new Python file named `app.py` with the following code:

   ```python
   import gradio as gr
   from transformers import AutoTokenizer, AutoModelForCausalLM

   # Load the model and tokenizer
   tokenizer = AutoTokenizer.from_pretrained("EzioDevio/iraqi_dialect_llm")
   model = AutoModelForCausalLM.from_pretrained("EzioDevio/iraqi_dialect_llm")

   # Define the text generation function
   def generate_text(prompt):
       inputs = tokenizer.encode(prompt, return_tensors="pt")
       outputs = model.generate(inputs, max_length=50, num_return_sequences=1)
       return tokenizer.decode(outputs[0], skip_special_tokens=True)

   # Set up the Gradio interface
   iface = gr.Interface(
       fn=generate_text,
       inputs="text",
       outputs="text",
       title="Iraqi Dialect Language Model",
       description="Generate text in Iraqi Arabic dialect.",
       examples=["شلونك اليوم؟", "وين رايح؟", "صباح الخير"]
   )

   iface.launch()

2. Upload to Hugging Face Spaces:

   • Go to the Spaces section on Hugging Face and create a new Space.
   • Name it something like iraqi_dialect_demo.
   • Choose Gradio as the application type.
   • Upload the app.py file and any additional files like requirements.txt (if you need specific dependencies listed).

3. Requirements File (optional):

   If you need to add specific dependencies, create a requirements.txt file in the Space:

   transformers
   torch
   gradio
   

4. Launch the Space:

   Once the files are uploaded, the Hugging Face Space will automatically build and deploy. You can view and test the demo on the link provided, which you can share in your README.