BERT 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.

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ALBERT is a self-supervised Transformer architecture that undergoes pretraining on a vast dataset of English text, eliminating the need for manual annotations by employing an automated method to create inputs and corresponding labels from unprocessed text. This model is designed with two primary training objectives in mind. The first objective, known as Masked Language Modeling (MLM), involves randomly obscuring 15% of the words in a given sentence and challenging the model to accurately predict those masked words. This approach sets it apart from recurrent neural networks (RNNs) and autoregressive models such as GPT, as it enables ALBERT to capture bidirectional representations of sentences. The second training objective is Sentence Ordering Prediction (SOP), which focuses on the task of determining the correct sequence of two adjacent text segments during the pretraining phase. By incorporating these dual objectives, ALBERT enhances its understanding of language structure and contextual relationships. This innovative design contributes to its effectiveness in various natural language processing tasks.
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Pricing

Pricing Starts At:
Free
Free Version:
Yes

Integrations

View Integrations

Reviews - 1 Verified Review

Total
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features
design
support
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Company Details

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

Media

BERT Screenshot 1
BERT Screenshot 1
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Product Details

Platforms
Web-Based
Types of Training
Training Docs

BERT Features and Options

Natural Language Processing Software

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

BERT Lists

BERT User Reviews

Write a Review
  • Name: Anonymous (Verified)
    Job Title: Backend Developer
    Length of product use: Less than 6 months
    Used How Often?: Monthly
    Role: User
    Organization Size: 100 - 499
    Features
    Design
    Ease
    Pricing
    Support
    Likelihood to Recommend to Others
    1 2 3 4 5 6 7 8 9 10

    BERT Implementation

    Date: Aug 27 2024

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

    Positive: 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.

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

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