batteryonlybert - uncased - abstract Open-Source Text Classification Model: Accurately Handle Battery Paper Abstract Classification

Batteryonlybert Uncased Abstract

Developed by batterydata

This is a text classification model based on BatteryOnlyBERT-uncased, specifically designed for classifying abstracts of battery-related research papers.

Text Classification

Transformers

EnglishOpen Source License:Apache-2.0 #Battery Literature Classification #High-Precision Text Classification #Battery Material Analysis

Downloads 19

Release Time : 3/2/2022

Model Overview

The model is based on the BatteryOnlyBERT-uncased architecture and is specifically tailored for classifying abstracts in the battery research domain. It can accurately identify and categorize technical content related to batteries.

Model Features

High Accuracy

Achieves 97.18% and 97.08% accuracy on the validation and test sets, respectively.

Domain-Specific

Optimized specifically for abstracts of battery research papers.

Based on BatteryOnlyBERT

Uses a domain-specific pre-trained model for battery research as its foundation.

Model Capabilities

Battery-related text classification

Academic paper abstract analysis

Use Cases

Academic Research

Battery Paper Abstract Classification

Automatically classifies abstracts of academic papers related to battery technology.

Classification accuracy exceeds 97%.

Knowledge Management

Battery Literature Organization

Helps researchers organize and categorize literature in the battery field.

🚀 BatteryOnlyBERT-uncased for Battery Abstract Classification

A language model designed for battery abstract classification, offering high accuracy in text classification tasks.

🚀 Quick Start

This model is specifically tailored for battery abstract classification. It uses the batteryonlybert-uncased language model and is trained on English text.

✨ Features

Text Classification: Specialized in classifying battery 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/batteryonlybert-uncased-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	batteryonlybert-uncased
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 = 16
n_epochs = 13
base_LM_model = "batteryonlybert-uncased"
learning_rate = 3e-5

Performance

"Validation accuracy": 97.18,
"Test accuracy": 97.08,

🔧 Technical Details

The model is trained on battery abstracts to enhance its performance in text classification related to the battery domain. The hyperparameters are carefully tuned to achieve high accuracy.

📄 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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