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Description

BERT is a significant language model that utilizes a technique for pre-training language representations. This pre-training process involves initially training BERT on an extensive dataset, including resources like Wikipedia. Once this foundation is established, the model can be utilized for diverse Natural Language Processing (NLP) applications, including tasks such as question answering and sentiment analysis. Additionally, by leveraging BERT alongside AI Platform Training, it becomes possible to train various NLP models in approximately half an hour, streamlining the development process for practitioners in the field. This efficiency makes it an appealing choice for developers looking to enhance their NLP capabilities.

Description

CodeQwen serves as the coding counterpart to Qwen, which is a series of large language models created by the Qwen team at Alibaba Cloud. Built on a transformer architecture that functions solely as a decoder, this model has undergone extensive pre-training using a vast dataset of code. It showcases robust code generation abilities and demonstrates impressive results across various benchmarking tests. With the capacity to comprehend and generate long contexts of up to 64,000 tokens, CodeQwen accommodates 92 programming languages and excels in tasks such as text-to-SQL queries and debugging. Engaging with CodeQwen is straightforward—you can initiate a conversation with just a few lines of code utilizing transformers. The foundation of this interaction relies on constructing the tokenizer and model using pre-existing methods, employing the generate function to facilitate dialogue guided by the chat template provided by the tokenizer. In alignment with our established practices, we implement the ChatML template tailored for chat models. This model adeptly completes code snippets based on the prompts it receives, delivering responses without the need for any further formatting adjustments, thereby enhancing the user experience. The seamless integration of these elements underscores the efficiency and versatility of CodeQwen in handling diverse coding tasks.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

AWS Marketplace Yes 
Alibaba Cloud No 
Alpaca Yes 
Amazon SageMaker Model Training Yes 
Code Llama No 
Conda No 
DeepSeek Coder No 
GPT-4 No 
Gopher Yes 
Haystack Yes 
Hugging Face No 
LangChain No 
LeetCode No 
LlamaIndex No 
ModelScope No 
Ollama No 
PostgresML Yes 
PyTorch No 
Python No 
Spark NLP Yes 

Integrations

AWS Marketplace No 
Alibaba Cloud Yes 
Alpaca No 
Amazon SageMaker Model Training No 
Code Llama Yes 
Conda Yes 
DeepSeek Coder Yes 
GPT-4 Yes 
Gopher No 
Haystack No 
Hugging Face Yes 
LangChain Yes 
LeetCode Yes 
LlamaIndex Yes 
ModelScope Yes 
Ollama Yes 
PostgresML No 
PyTorch Yes 
Python Yes 
Spark NLP No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

cloud.google.com/ai-platform/training/docs/algorithms/bert-start

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

github.com/QwenLM/CodeQwen1.5

Product Features

Natural Language Processing

Co-Reference Resolution No 
In-Database Text Analytics No 
Named Entity Recognition No 
Natural Language Generation (NLG) No 
Open Source Integrations No 
Parsing No 
Part-of-Speech Tagging No 
Sentence Segmentation No 
Stemming/Lemmatization No 
Tokenization No 

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