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Fastc
Overview :
fastc is a simple and lightweight text classification tool based on large language model embeddings. It focuses on CPU execution and uses efficient models like deepset/tinyroberta-6l-768d to generate embeddings. It achieves text classification through cosine similarity classification instead of fine-tuning, and it can run multiple classifiers using the same model without adding extra overhead.
Target Users :
Aimed at developers and data scientists who need text classification, especially those with limited computing resources or who want to quickly deploy text classification models.
Use Cases
Social media sentiment analysis, quickly identifying the emotional sentiment of user reviews.
Product review classification, automatically categorizing user feedback as positive or negative.
News article topic classification, automatically distributing news to corresponding topic columns.
Features
Focuses on CPU execution and uses efficient models to generate embeddings.
Performs text classification using cosine similarity, without the need for fine-tuning.
Supports multiple classifier execution, sharing the same model's embeddings.
Supports model training and export, making it convenient for future use.
Allows model deployment to the HuggingFace model hub.
Supports loading pre-trained models from directories or HuggingFace.
Provides class prediction functionality, including single and batch prediction.
How to Use
Install the fastc library: Install fastc using Python's package manager, pip.
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Prepare the dataset: Gather and organize the text data used for training the classifier.
Train the model: Use the SentenceClassifier class provided by fastc to train the text classifier.
Save the model: After training, save the model using the save_model method for future use.
Load the model: Load the local or pre-trained model from HuggingFace using the SentenceClassifier class.
Perform prediction: Use the predict_one or predict method to predict the sentiment classification of new text.
