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BERT Tiny L 2 H 128 A 2
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BERT Tiny L 2 H 128 A 2

Developed by nreimers
BERT-Medium is a lightweight version of the BERT model released by Google, featuring fewer network layers and hidden units, making it suitable for resource-constrained environments.
Large Language Model#Lightweight BERT#Dual Attention Heads#Short Text Processing
Downloads 8,394
Release Time : 3/2/2022

Model Overview

BERT-Medium is a pre-trained language model based on the Transformer architecture, primarily used for natural language processing tasks. It is a lightweight version of the BERT model with fewer parameters and computational requirements.

Model Features

Lightweight Design
The model has a streamlined structure, making it suitable for resource-constrained environments while maintaining high performance.
Bidirectional Context Understanding
Based on BERT's bidirectional Transformer architecture, it can understand the contextual information of text.
Pre-training and Fine-tuning
Supports pre-training on large-scale corpora and can be fine-tuned for specific tasks.

Model Capabilities

Text Classification
Question Answering Systems
Named Entity Recognition
Text Similarity Calculation

Use Cases

Text Analysis
Sentiment Analysis
Analyze the sentiment tendency of text (positive, negative, or neutral).
Performs well on standard datasets.
Question Answering System
Answer questions based on given text.
Suitable for simple question-answering tasks.
Information Extraction
Named Entity Recognition
Identify entities such as person names, place names, and organization names in text.
Performs well on standard datasets.
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