ahmedbesbes/overview-and-benchmark-of-traditional-and-deep-learning-models-in-text-classification
NLP tutorial
This resource helps data scientists and machine learning engineers compare and choose the best text classification models for their projects. It provides practical examples of different NLP models, from traditional methods like logistic regression with n-grams to deep learning architectures such as CNNs and RNNs. You input your text data, and it shows you how to implement and benchmark various models, helping you identify which approach performs best for your specific classification task.
No commits in the last 6 months.
Use this if you need to build a text classification system and want to understand the performance trade-offs of different machine learning and deep learning models on a real dataset.
Not ideal if you are looking for a plug-and-play API or a no-code solution for text classification without needing to understand model implementation details.
Stars
42
Forks
20
Language
Jupyter Notebook
License
—
Last pushed
Jun 13, 2018
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/ahmedbesbes/overview-and-benchmark-of-traditional-and-deep-learning-models-in-text-classification"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
gaussic/text-classification-cnn-rnn
CNN-RNN中文文本分类，基于TensorFlow
ShawnyXiao/TextClassification-Keras
Text classification models implemented in Keras, including: FastText, TextCNN, TextRNN,...
prakashpandey9/Text-Classification-Pytorch
Text classification using deep learning models in Pytorch
TobiasLee/Text-Classification
Implementation of papers for text classification task on DBpedia
FreedomIntelligence/TextClassificationBenchmark
A Benchmark of Text Classification in PyTorch