Audience

Developers interested in a powerful large language model

About BERT

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.

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Pricing

Starting Price:
Free
Free Version:
Free Version available.

Integrations

See Integrations

Ratings/Reviews - 1 User Review

Overall 4.0 / 5
ease 4.0 / 5
features 4.0 / 5
design 3.0 / 5
support 3.0 / 5
More Reviews Write a Review

Company Information

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

Videos and Screen Captures

BERT Screenshot 1
BERT Screenshot 1
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Product Details

Platforms Supported
Cloud
Training
Documentation

BERT Frequently Asked Questions

Q: What kinds of users and organization types does BERT work with?
Q: What languages does BERT support in their product?
Q: What other applications or services does BERT integrate with?
Q: What type of training does BERT provide?
Q: How much does BERT cost?

BERT Product Features

Natural Language Processing

Sentence Segmentation
Tokenization
Stemming/Lemmatization
Part-of-Speech Tagging
Parsing
Named Entity Recognition
Co-Reference Resolution
In-Database Text Analytics
Open Source Integrations
Natural Language Generation (NLG)

BERT Verified User Reviews

Write a Review
  • A BERT User
    Backend Developer
    Used the software for: Less than 6 months
    Frequency of Use: Monthly
    User Role: User
    Company Size: 100 - 499
    Design
    Ease
    Features
    Pricing
    Support
    Probability You Would Recommend?
    1 2 3 4 5 6 7 8 9 10

    "BERT Implementation"

    Posted 2024-08-27

    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.

    Overall: BERT provided results with high accuracy. BERT allowed flexibility to cdeo and handle edge cases better.

    Read More...
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