{"id":18662286,"url":"https://github.com/dimits-ts/text_analytics","last_synced_at":"2026-04-09T22:59:06.976Z","repository":{"id":230718937,"uuid":"743946081","full_name":"dimits-ts/text_analytics","owner":"dimits-ts","description":"Language Modelling (text generation, spell correction) and Sentiment Analysis / POS Tagging with MLP,  RNN, CNN and BERT models and LLM prompting","archived":false,"fork":false,"pushed_at":"2024-03-31T10:56:11.000Z","size":72411,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-12-27T16:33:20.432Z","etag":null,"topics":["baseline-model","bert","cnn","fasttext-embeddings","language-modeling","llm","mlp-classifier","pos-tagging","prompting","pytorch","rnn","sentiment-analysis","sklearn","spell-correction","tensorflow","text-generation","transformers","transformers-library"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Text Analytics\n\n## Language Moddeling\n\nWe create bigram, trigram and linear interpolation language models which are used for language generation and spell correction. \n\n[Source code](https://github.com/dimits-exe/text_analytics/tree/master/language_modeling) [Report](https://github.com/dimits-exe/text_analytics/blob/master/language_modeling/report.pdf)\n\n## Sentiment Classification and POS Tagging tasks\n\nWe create deep learning models using the `Transformers\\Datasets`, `Pytorch` and `Tensorflow` libraries. \nWe also use the `keras_tuner` / `transformers_trainer` frameworks to optimize hyperparameters and model architecture.\n\nWe briefly mention additional tasks carried out:\n\n* Sentiment Analysis: Dataset selection, exploratory analysis, custom stopwords, data augmentation. \u003c!--- βαλε εδω αν εχω ξεχασει κατι --\u003e\n* POS Taggging: Dataset selection, exploratory analysis, custom parsing, custom baseline (\"smart dummy\") model, local caching of heavy computations, automated results generation (python -\u003e LaTeX).\n\nEach task features two IPython notebooks containing the executed code, python source files for repeated custom tasks and a unified report.\n\nThe reports discuss in detail the design decisions for each classifier and include graphs and aggregated results comparing the current model to the previous models.   \n\n### Simple MLP model\n\n[Sentiment classification](https://github.com/dimits-exe/text_analytics/blob/master/mlp_nlp/ex_9.ipynb) [POS Tagging](https://github.com/dimits-exe/text_analytics/blob/master/mlp_nlp/ex_10.ipynb) [Report](https://github.com/dimits-exe/text_analytics/blob/master/mlp_nlp/report.pdf)\n\n### RNN Model\n\n[Sentiment classification](https://github.com/dimits-exe/text_analytics/blob/master/rnn/ex_1.ipynb) [POS Tagging](https://github.com/dimits-exe/text_analytics/blob/master/rnn/ex2.ipynb) [Report](https://github.com/dimits-exe/text_analytics/blob/master/rnn/report.pdf)\n\n### CNN Model\n\n[Sentiment classification](https://github.com/dimits-exe/text_analytics/blob/master/cnn/ex_2.ipynb) [POS Tagging](https://github.com/dimits-exe/text_analytics/blob/master/cnn/ex3.ipynb) [Report](https://github.com/dimits-exe/text_analytics/blob/master/cnn/report.pdf)\n\n### BERT Model\n\n[Sentiment classification](https://github.com/dimits-exe/text_analytics/blob/master/transformers/ex_1.ipynb) [POS Tagging](https://github.com/dimits-exe/text_analytics/blob/master/transformers/ex3.ipynb) [Report](https://github.com/dimits-exe/text_analytics/blob/master/transformers/report.tex)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdimits-ts%2Ftext_analytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdimits-ts%2Ftext_analytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdimits-ts%2Ftext_analytics/lists"}