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Model Card for fine-tuned-gpt2-wordpress
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This is a fine-tuned GPT-2 model on a WordPress-related dataset.
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Model Description
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This model is a fine-tuned version of the GPT-2 model, a transformer-based language model developed by OpenAI. It has been further trained on a dataset related to WordPress, with the goal of generating text relevant to WordPress queries, concepts, or tasks.
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Intended Use
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This model is intended for text generation tasks within the domain of WordPress. This could include:
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Generating responses to WordPress-related questions.
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Creating content snippets for WordPress websites.
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Assisting in writing documentation or tutorials related to WordPress.
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Exploring and generating ideas for WordPress themes, plugins, or features.
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This model is not intended for:
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Generating harmful, biased, or offensive content.
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Deployment in critical applications without further fine-tuning and rigorous evaluation.
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Generating content outside of the WordPress domain.
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Training Data
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The model was fine-tuned on a dataset that, for demonstration purposes, was simulated using a dummy dataset due to issues loading specific WordPress datasets. The dummy dataset contained text data designed to mimic potential WordPress-related text.
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(Replace this section with details about your actual training dataset once it is used, including its source, size, and characteristics.)
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Training Procedure
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The model was fine-tuned using the Hugging Face transformers library and Trainer class.
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Base Model: gpt2
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Training Arguments:
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output_dir: ./results
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num_train_epochs: 3
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per_device_train_batch_size: 8
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save_steps: 10_000
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save_total_limit: 2
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logging_dir: ./logs
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logging_steps: 500
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report_to: "none" (to disable W&B logging)
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(Adjust these details based on your actual training configuration.)
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Evaluation Results
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The model was evaluated on a dummy test dataset. The evaluation results are as follows:
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{'eval_loss': 5.172921657562256, 'eval_runtime': 4.4501, 'eval_samples_per_second': 4.494, 'eval_steps_per_second': 0.674, 'epoch': 3.0}
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(Replace these results with the evaluation metrics from your actual test set.)
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Limitations and Bias
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(Add information about any known limitations or biases of the model based on the training data or model architecture.)
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Further Information
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(Include links to the original model, the dataset used, or any other relevant resources.)
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