This model is a fine-tuned version of google-t5/t5-base on the dataset which is curated using my own data. It achieves the following results on the evaluation set:

This model is a fine-tuned version of google-t5/t5-base specifically adapted to generate tweets in a particular style. The goal is to take a longer text input (e.g., a concept, a piece of news, a paragraph from an article) and transform it into a concise, engaging tweet that mimics the stylistic characteristics of a specific user (e.g., tone, common phrasing, use of hashtags, and desired structure like a catchy headline followed by a short elaboration). The model was fine-tuned on a custom dataset of input-output pairs, where the inputs are longer texts and the outputs are example tweets written in the target style. The fine-tuning process aimed to teach the model to understand the input content and rephrase it according to the stylistic nuances present in the training data.

Content Generation: To assist in drafting tweets that align with a specific personal or brand voice. Given a topic or a longer piece of text, the model can suggest a tweet. Workflow Automation: Designed to be integrated into workflows (e.g., via N8N) to automate the process of generating initial tweet drafts from other content sources. Style Transfer: To apply a specific tweet-like style to informational content. Creative Assistance: As a tool to quickly generate stylistic variations of a message for social media.

The model was fine-tuned on a custom dataset composed of two JSON files: dataset(sample).json and dataset(purpose).json. These files contain input-output pairs: Input: Longer-form text, descriptions, or ideas that serve as the source material for a tweet. Output: Example tweets written in the target user's characteristic style, intended to capture their typical tone, phrasing, use of hashtags, and conciseness. The combined dataset contains approximately [Insert Total Number of Examples Before Splitting - e.g., 85] examples. This dataset was then split into a training set (90%) and a validation set (10%) for the fine-tuning process. A prefix "tweet like me: " was added to each input before tokenization during training to guide the model on the task. (Self-correction/Future improvement note: To achieve a more specific output structure like "catchy headline + 5 lines," the 'output' examples in the training data would need to be consistently formatted in this way. The current dataset primarily reflects a general tweet style.)

The validation set (10% of the combined custom dataset, approximately [Insert Number of Validation Examples - e.g., 8 or 9] examples) was used to monitor the model's performance during training. Key metrics tracked were: Validation Loss: To check for overfitting and generalization. ROUGE Scores (ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-Lsum): To measure n-gram overlap and longest common subsequence between generated and reference tweets. The final reported evaluation metrics on this validation set (after 3 epochs of training) were: eval_loss: 11.9889 eval_rouge1: 25.2391 eval_rouge2: 5.7802 eval_rougeL: 17.8758 eval_rougeLsum: 19.1195 eval_gen_len: 20.0 Qualitative evaluation (manual inspection of generated outputs) is also a critical part of assessing this model's performance, especially for stylistic nuances not captured by ROUGE scores.

The following hyperparameters were used during training: