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:
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