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Albert Base V1

Developed by albert
ALBERT is a lightweight pre-trained language model based on the Transformer architecture, trained on English text through self-supervised learning with parameter-sharing features to reduce memory usage.
Downloads 18.34k
Release Time : 3/2/2022

Model Overview

The model employs masked language modeling and sentence order prediction objectives for pre-training and is suitable for fine-tuning downstream tasks such as text classification and question answering.

Model Features

Weight-Sharing Architecture
All Transformer layers share weights, significantly reducing memory consumption.
Lightweight Design
The base version has only 11 million parameters, making it suitable for resource-constrained scenarios.
Bidirectional Context Understanding
Learns bidirectional text representations through masked language modeling.

Model Capabilities

Text Feature Extraction
Masked Token Prediction
Sentence Order Judgment
Downstream Task Fine-Tuning

Use Cases

Text Understanding
Sequence Classification
Tasks such as sentiment analysis and topic classification
Achieved 90.3% accuracy on the SST-2 sentiment analysis task.
Question Answering
Text-based question answering tasks
F1 score of 89.3 on SQuAD1.1.
Language Modeling
Text Completion
Predicting masked tokens
Examples demonstrate the ability to predict contextually relevant tokens.
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