{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Transformers Text Classification\nSuggesting a neural network architecture for analyzing and recognizing texts, where transformers were used through a pre-trained BERT model, in addition to its integration with the LSTM layer with the Global Pooling layers, in order to reach a model capable of analyzing texts.\n# Result:\n## Neural Network Architecture:\n\n![image](https://user-images.githubusercontent.com/108609519/184367055-1e1bb9c4-b5eb-446a-97f1-356ff0d90b73.png)\n\n## Metrics:\n### Accuracy, Recall, Precision:\n![image](https://user-images.githubusercontent.com/108609519/184367276-b61f256b-3004-498f-b38e-cdb0c3723430.png)\n### Loss While Training:\n![image](https://user-images.githubusercontent.com/108609519/184368689-8afa080e-c96a-4833-9b1e-932ecb965078.png)\n\n## Evaluate with Validation Data:\n\n![image](https://user-images.githubusercontent.com/108609519/184368509-239d06e2-f071-4e45-b9a4-1fd50a7eaea4.png)\n\n\n# NoteBook Link:\nhttps://github.com/kaledhoshme123/Transformers-Text-Classification","metadata":{}}]}