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Albert Large V1

Developed by albert
ALBERT is a lightweight BERT variant pre-trained on English corpora, reducing memory usage through parameter sharing, supporting masked language modeling and sentence order prediction tasks.
Downloads 979
Release Time : 3/2/2022

Model Overview

This model is pre-trained in a self-supervised manner on BookCorpus and English Wikipedia, primarily used for feature extraction in natural language processing tasks and fine-tuning for downstream tasks.

Model Features

Parameter-Sharing Architecture
All Transformer layers share the same parameters, significantly reducing model size (only 1/18 of BERT-large).
Dual-Task Pre-training
Simultaneously performs masked language modeling (MLM) and sentence order prediction (SOP), enhancing semantic understanding capabilities.
Lightweight Design
Balanced design with 128-dimensional word embeddings and 1024-dimensional hidden layers, balancing performance and efficiency.

Model Capabilities

Text Feature Extraction
Masked Word Prediction
Sentence Relation Judgment
Downstream Task Fine-tuning

Use Cases

Text Understanding
Missing Word Completion
Predicts masked words, e.g., 'Hello I'm a [MASK] model'
Can output reasonable predictions like 'modeling' or 'modelling'.
Educational Applications
Reading Comprehension System
Can be fine-tuned on the SQuAD dataset for question-answering systems.
V1 version achieves 90.6/83.9 (F1/EM) on SQuAD1.1.
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