Arabic Quote Generator Using GPT-2

The goal of this project was to utilize a dataset of Arabic quotes in order to fine-tune a GPT-2 model for the generation of Arabic quotations. The process involved importing and pre-processing the dataset, preparing it for use as input for the GPT-2 model, fine-tuning the model, and evaluating the generated quotations.

Part 1: Importing Data & Pre-processing the Arabic Quote Dataset

The first step in this project was to import the Arabic quote dataset and perform any necessary pre-processing. This included cleaning the data and removing any invalid or irrelevant entries.

Part 2: Preparing the Dataset

Once the data had been imported and pre-processed, it needed to be prepared for use as input for the GPT-2 model. This involved converting the data from a dataframe into a text file and ensuring that it was in the proper format for the model to consume.

Part 3: Fine-tuning the GPT-2 Model

With the dataset prepared, the next step was to fine-tune the GPT-2 model using the Arabic quote dataset. This involved training the model on the dataset and adjusting its hyperparameters to optimize performance.

Part 4: Generating Arabic Quotes Based on User Inputs

With the GPT-2 model fine-tuned, the next step was to use it to generate Arabic quotations based on user inputs. This involved providing the model with a prompt and allowing it to generate a quotation in response.

Part 5: Evaluation of Results

The final step in this project was to evaluate the quality of the generated quotations. This was done by comparing them to the original dataset and assessing their relevance, coherence, and overall quality.

Conclusion

Overall, this project was successful in achieving its goal of using a dataset of Arabic quotes to fine-tune a GPT-2 model for the generation of Arabic quotations. The resulting model was able to generate quotations that were relevant, coherent, and of high quality, demonstrating the effectiveness of the fine-tuning process. 

Project resources and overview:

Tech Used in this project: Python, GPT-2.

GitHub project Link: https://github.com/BoulahiaAhmed/Arabic-quote-generator

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YTbrief: AI solution that will make your YouTube experience easier

 YouTube one of the Biggest Search Engines, But…

On YouTube, there is a proliferation of clickbait or misleading content, which can be frustrating for users trying to find reliable information. Many videos on YouTube cover multiple topics at the same time, which can make it difficult for users to locate the specific information they are seeking. Longer videos can be challenging to follow and may lack the engagement necessary to keep the viewer's attention until the end.

Solution: YTbrief

With YTbrief (AI-powered application), users can easily navigate to the specific parts of a video that they wish to watch or listen to with just a few simple steps.

By pasting any YouTube link and entering their search query, users can quickly access the desired content within the video, displayed as a YouTube video that starts at the relevant section in response to their queries.

This innovative tool streamlines the video viewing experience, allowing users to efficiently find and enjoy the content that they desire with ease.

YTbrief will allow users to easily skip to the specific parts of a video that they want to watch or listen to by asking questions, in very few steps.

1. Past any YouTube link.

2. Enter your search query and click Search.

3. Display the results (A YouTube video that starts at the relevant section in response to user queries)

It works better with:

The results of this application are particularly effective when applied to podcasts, lectures, educational videos, seminars, and documentaries. The ability to easily skip to specific sections within these types of audio and video content greatly enhances the user's ability to absorb and retain information, making it an invaluable resource for learners and professionals alike.

Project resources and overview:

Tech Used in this project: Python, Streamlit, Cohere.ai, and Faiss.

GitHub project Link: https://github.com/BoulahiaAhmed/YTBrief

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