Get embeddings and classify using QWEN 2. A Multi-lingual model, now capable to do classification.
Notebook: colab.research.google.com/dri...
Github: github.com/mosh98/Embedding_C...
Synthetic Data Generation with embeddings to boost performance,
Join Skool Community for $129:
www.skool.com/data-society-42...
Have questions or ideas, meet similar people?
join the discord : / discord
Don't fall behind the AI revolution, I can help integrate machine learning/AI into your company.
mosleh587084.typeform.com/to/...
Text classification is a powerful tool in natural language processing (NLP) that can be used in various applications to automatically categorize text into predefined classes or labels. Here are ten examples of what can be done with text classification:
1. Spam Detection: Classifying emails or messages as spam or not spam. This helps in filtering out unwanted and potentially harmful emails.
2. Sentiment Analysis: Determining the sentiment of a piece of text, such as whether a product review is positive, negative, or neutral.
3. Topic Categorization: Classifying news articles, blog posts, or research papers into topics like sports, politics, technology, health, etc.
4. Language Identification: Automatically detecting the language in which a text is written, which is useful for multilingual applications.
5. Customer Support: Classifying customer queries and complaints to route them to the appropriate department or support agent.
6. Toxic Comment Detection: Identifying and filtering out toxic or abusive comments in online forums, social media, or comment sections.
7. Document Tagging: Assigning tags or keywords to documents for easier indexing and retrieval in content management systems.
8. Product Categorization: Automatically categorizing products in an e-commerce platform based on their descriptions.
9. Fraud Detection: Identifying fraudulent activity or transactions based on text data, such as descriptions of transactions or communications.
10. Medical Text Classification: Classifying medical records, clinical notes, or research papers into categories like diagnosis, treatment, symptoms, etc., to assist healthcare professionals in organizing and retrieving relevant information.
These examples demonstrate the versatility of text classification in various fields, from improving user experience and automating tasks to enhancing security and decision-making processes.
What is Qwen 2?
Qwen 2 is a series of large language models developed by Alibaba Cloud, designed to excel in various AI tasks. The Qwen 2 models range in size from 0.5 billion to 72 billion parameters, making them versatile for applications such as language understanding, generation, multilingual tasks, coding, and mathematics.
Негізгі бет Ғылым және технология Classify Using Qwen2
Пікірлер: 3