Open-source BatteryBERT-cased-abstract Model - Precise Classification of Battery Paper Abstracts to Aid Research and Analysis

Batterybert Cased Abstract

Developed by batterydata

BatteryBERT-cased is a pre-trained language model specifically designed for classifying battery-related paper abstracts. Based on the BERT architecture, it is optimized for battery domain texts.

Text Classification

Transformers

EnglishOpen Source License:Apache-2.0 #Battery Material Classification #High-Precision Text Classification #Research Paper Abstracts

Downloads 37

Release Time : 3/2/2022

Model Overview

This model is primarily used for classifying academic paper abstracts in the battery field, accurately identifying and categorizing battery-related technical content.

Model Features

Domain-Specific

Specifically optimized and trained for battery domain texts, improving classification accuracy in this field.

High Accuracy

Achieved accuracy rates of 97.29% and 96.85% on the validation and test sets, respectively, demonstrating excellent performance.

BERT-Based Architecture

Utilizes the powerful BERT architecture as its foundation, inheriting BERT's superior feature extraction capabilities.

Model Capabilities

Battery Domain Text Classification

Academic Paper Abstract Analysis

Use Cases

Academic Research

Battery Paper Abstract Classification

Automatically classify abstracts of battery-related academic papers

Accuracy rate exceeds 96%

Knowledge Management

Battery Literature Organization

Assist researchers in organizing and categorizing large volumes of battery-related literature

Improves literature retrieval efficiency

🚀 BatteryBERT-cased for Battery Abstract Classification

BatteryBERT-cased is a pre - trained language model designed for battery abstract classification, offering high - performance text classification capabilities in the battery domain.

🚀 Quick Start

BatteryBERT-cased is a powerful language model dedicated to battery abstract classification. It can be easily integrated into your projects for text classification tasks. For more details, please refer to the sections below.

✨ Features

Text Classification: Specialized in classifying battery - related abstracts.
High Performance: Achieves high accuracy in validation and test sets.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_name = "batterydata/batterybert-cased-abstract"

# a) Get predictions
nlp = pipeline('text-classification', model=model_name, tokenizer=model_name)
input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'}
res = nlp(input)

# b) Load model & tokenizer
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

📚 Documentation

Model Information

Property	Details
Model Type	batterybert-cased
Language	English
Downstream-task	Text Classification
Training Data	training_data.csv
Eval Data	val_data.csv
Code	See example
Infrastructure	8x DGX A100

Hyperparameters

batch_size = 32
n_epochs = 11
base_LM_model = "batterybert-cased"
learning_rate = 2e-5

Performance

"Validation accuracy": 97.29,
"Test accuracy": 96.85,

🔧 Technical Details

The model is trained with specific hyperparameters and infrastructure to achieve high - performance text classification in the battery abstract domain. The hyperparameters are carefully tuned to optimize the model's performance on the given training and evaluation datasets.

📄 License

This project is licensed under the Apache - 2.0 license.

📝 Authors

Shu Huang: sh2009 [at] cam.ac.uk
Jacqueline Cole: jmc61 [at] cam.ac.uk

📚 Citation

BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement

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