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Adebert

Developed by Jacobberk
adeBERT is a fine-tuned model based on the BERT-large architecture, focusing on domain-specific tasks and demonstrating excellent performance on evaluation datasets.
Large Language Model
Transformers
Open Source License:Apache-2.0#High-precision text classification#BERT fine-tuned model#Medical entity recognition
Downloads 25
Release Time : 4/21/2024

Model Overview

This model is a fine-tuned version based on the BERT-large architecture, suitable for natural language processing tasks such as text classification, achieving an F1 score of 0.9551 on evaluation datasets.

Model Features

High-precision text classification
Achieved an outstanding F1 score of 0.9551 on evaluation datasets
Based on BERT-large architecture
Utilizes the powerful language understanding capabilities of BERT-large for fine-tuning
Efficient fine-tuning
Requires only 3 training epochs to achieve high performance

Model Capabilities

Text classification
Natural language understanding
Sequence labeling

Use Cases

Text analysis
Sentiment analysis
Analyzes the sentiment tendency of text
Highly accurate sentiment classification
Topic classification
Classifies text into predefined topic categories
F1 score performance of 0.9551
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