BERT

BERT

Google
CodeQwen

CodeQwen

Alibaba
+
+

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About

BERT is a large language model and a method of pre-training language representations. Pre-training refers to how BERT is first trained on a large source of text, such as Wikipedia. You can then apply the training results to other Natural Language Processing (NLP) tasks, such as question answering and sentiment analysis. With BERT and AI Platform Training, you can train a variety of NLP models in about 30 minutes.

About

CodeQwen is the code version of Qwen, the large language model series developed by the Qwen team, Alibaba Cloud. It is a transformer-based decoder-only language model pre-trained on a large amount of data of codes. Strong code generation capabilities and competitive performance across a series of benchmarks. Supporting long context understanding and generation with the context length of 64K tokens. CodeQwen supports 92 coding languages and provides excellent performance in text-to-SQL, bug fixes, etc. You can just write several lines of code with transformers to chat with CodeQwen. Essentially, we build the tokenizer and the model from pre-trained methods, and we use the generate method to perform chatting with the help of the chat template provided by the tokenizer. We apply the ChatML template for chat models following our previous practice. The model completes the code snippets according to the given prompts, without any additional formatting.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Developers interested in a powerful large language model

Audience

Anyone seeking an AI tool to improve their natural language understanding operations and text generation tasks

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 4.0 / 5
ease 4.0 / 5
features 4.0 / 5
design 3.0 / 5
support 3.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Pros & Cons from Real Users

Pros

  • When BERT model implemented on stress detection use case, BERT as it handles context of the text was easily able to identify negation sentence like detecting "I am NOT happy" as a stressful text which was not happening in other models like logistic regression, decision tree, random forest, multinomial naive bayes, CNN, RNN, LSTM etc.

Cons

  • difficulty in finding a suitable multilingual datastet to train the model for both hind and english use cases.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Google
Founded: 1998
United States
cloud.google.com/ai-platform/training/docs/algorithms/bert-start

Company Information

Alibaba
Founded: 1999
China
github.com/QwenLM/CodeQwen1.5

Alternatives

Gemini

Gemini

Google

Alternatives

CodeGemma

CodeGemma

Google
ALBERT

ALBERT

Google
Qwen-7B

Qwen-7B

Alibaba
BLOOM

BLOOM

BigScience
Qwen2.5-Max

Qwen2.5-Max

Alibaba
RoBERTa

RoBERTa

Meta
Qwen2

Qwen2

Alibaba
GPT-4

GPT-4

OpenAI
Qwen

Qwen

Alibaba

Categories

Categories

Integrations

AWS Marketplace
Alibaba Cloud
Alpaca
Amazon SageMaker Model Training
AtCoder
Conda
DeepSeek Coder
GPT-3.5
GPT-4
Gopher
Haystack
Hugging Face
LangChain
ModelScope
Ollama
PyTorch
Python
Qwen Studio
Spark NLP
StarCoder

Integrations

AWS Marketplace
Alibaba Cloud
Alpaca
Amazon SageMaker Model Training
AtCoder
Conda
DeepSeek Coder
GPT-3.5
GPT-4
Gopher
Haystack
Hugging Face
LangChain
ModelScope
Ollama
PyTorch
Python
Qwen Studio
Spark NLP
StarCoder
Claim BERT and update features and information
Claim BERT and update features and information
Claim CodeQwen and update features and information
Claim CodeQwen and update features and information