Pre-trained models (platform-provided models)
Pre-trained models are ready-to-use models provided by the platform. You can call them directly using API operations.
On March 24, 2023, the pre-trained models on the NLP Self-learning Platform were upgraded, categorized by industry, and fully migrated to Natural Language Processing (NLP). The NLP Self-learning Platform provides methods and interfaces for model invocation. For more information about the API operations, see the following documents.
Service invocation
For more information about how to call a model, see Model invocation.
For software development kit (SDK) examples, see SDK examples.
API operation list
E-commerce industry capabilities
Model name | Model description | Default QPS | Maximum text length |
Analyzes product review text from multiple dimensions. This model supports 55 industries in the e-commerce realm and 192 review properties. | 10 | 500 characters | |
Analyzes product review text from multiple dimensions. This model supports two local service industries (beauty/hair/nails and food/dining) and 11 review properties. | 10 | 500 characters | |
Analyzes product review text from multiple dimensions. This model supports 68 review properties in the automotive realm. | 10 | 500 characters | |
Purchase decision analysis from product reviews for e-commerce | Analyzes purchase decision information, such as user motivations, application scenarios, feature requirements, and questions. Use this model to improve products, enhance user experience, segment user profiles, and run targeted marketing campaigns. | 10 | 500 characters |
Purchase decision analysis from product reviews for the automotive realm | Analyzes purchase decision information, such as user motivations, application scenarios, feature requirements, and questions. Use this model to improve products, enhance user experience, segment user profiles, and run targeted marketing campaigns. | 10 | 500 characters |
Parses consumer conversations in online chat scenarios for e-commerce and other industries to determine consumer intent, sentiment, and emotion. | 10 | / | |
Extracts customer service scripts and user questions from online chat conversations. Use this model to analyze hot spot issues or build a script library. | 10 | / | |
Generates product descriptions related to specified selling points for a given product. | 10 | / |
General industry capabilities
Model name | Model description | Default QPS | Maximum text length |
Classifies bidding announcements. This model currently supports two types: "tender invitation" and "bid award". | 10 | / | |
Extracts 13 fields from bidding information, such as project name, project number, bidder name, and winning bid amount. | 10 | / | |
Parses tender invitations and bid awards separately. Extracts 22 fields from tender information. | 10 | / | |
Parses tender invitations and bid awards separately. Extracts 29 fields from bid award information. | 10 | / | |
Extracts common elements from contracts. This model supports 26 general element fields. | 10 | / | |
Extracts information that follows a key-value pattern from documents such as resumes, contracts, and reports. | 10 | / | |
Classifies telemarketing outbound call dialogues by industry and scenario for applications such as voice quality inspection. This model supports over 30 industries and over 170 scenarios. | 10 | / | |
Supports application scenarios such as customer service quality inspection for telemarketing dialogues and streamer monitoring for live streaming. | 10 | / | |
Recognizes user intent (reaction) in manual or intelligent telemarketing outbound call scenarios. | 10 | / | |
Identifies conversations with fraud risks in telemarketing outbound call scenarios. You can use this for voice quality inspection. | 10 | / | |
Extracts 10 resume fields from English resumes, such as name, contact information, degree, company, and position. | 10 | / | |
Extracts 33 resume fields from Chinese resumes, such as name, gender, age, education, and employer. | 10 | / | |
Extracts events from English news articles. This model includes 33 event categories. | 10 | / | |
Predicts the category of a product based on its title in e-commerce scenarios. The category system is consistent with e-commerce platforms such as Taobao. | 10 | / | |
Detects pornographic or erotic content in Chinese novels for content moderation scenarios. The model outputs a confidence level for pornography and the related text. | 10 | 600 characters | |
Predicts the sentiment of Russian text from social media (short text) in e-commerce scenarios. Sentiments are classified as positive, neutral, or negative. | 10 | / | |
Predicts the sentiment of English text from social media (short text) in e-commerce scenarios. Sentiments are classified as positive, neutral, or negative. | 10 | / | |
Predicts the sentiment of Spanish text from social media (short text) in e-commerce scenarios. Sentiments are classified as positive, neutral, or negative. | 10 | / | |
Recognizes customer or agent emotions in application scenarios such as telemarketing and online support. This model supports eight common emotions and three business-specific emotions. | 10 | 1000 characters | |
Classifies one or more news texts. | 10 | / | |
Detects poorly readable text caused by multiple people speaking at the same time in live streaming scenarios where speech is converted to text using Automatic Speech Recognition (ASR). | 10 | 600 characters | |
Parses documents for 10 causes of action and extracts 38 fields. | 10 | / | |
Extracts keywords or summaries from documents. | 10 | 500 characters | |
Generates text summaries or article titles. This model is designed for common text generation needs in real-world scenarios. | 10 | 500 characters | |
Generates an in-vehicle startup welcome message based on given weather information fields. | 10 | 500 characters | |
Takes Chinese text as input and outputs the corresponding vector representation. | 10 | / | |
Identifies irony and sarcasm in social media content. | 10 | / | |
Analyzes humorous content to predict its type (such as pun, convention, or inversion). | 10 | / |