DebERTA-v3-base-finetuned Open-source Financial Text Classification Model - Accurately Analyze Market Sentiment in Financial News

Deberta V3 Base Finetuned Finance Text Classification

Developed by nickmuchi

A financial sentiment analysis model fine-tuned based on microsoft/deberta-v3-base, specifically designed for analyzing financial news and market sentiment

Text Classification

Transformers

Open Source License:MIT #Financial Sentiment Analysis #High-Precision Classification #Financial News Processing

Downloads 1,540

Release Time : 5/29/2022

Model Overview

This model is optimized for financial texts, capable of accurately identifying bullish, bearish, and neutral sentiments in financial news, suitable for financial scenarios such as stock market sentiment analysis

Model Features

Financial Domain Optimization

Specially fine-tuned for financial texts, accurately understanding financial terminology and market sentiment expressions

High Accuracy

Achieves 89.13% accuracy on the evaluation set with an F1 score of 89.12%

Multi-Source Data Training

Trained using a combination of financial phrase banks, Kaggle self-annotated datasets, and nickmuchi financial classification datasets

Fine-Grained Sentiment Classification

Not only identifies basic sentiments (positive/negative/neutral) but also more nuanced sentiments like 'mildly bearish'

Model Capabilities

Financial Text Sentiment Analysis

Market Sentiment Identification

Financial News Classification

Stock Market Sentiment Prediction

Use Cases

Financial Analysis

Financial News Sentiment Analysis

Analyze market sentiment in financial news to help investors understand market trends

Accurately identifies bullish, bearish, and neutral sentiments

Investment Decision Support

Provide sentiment indicators for investment decisions by analyzing large volumes of financial news

Market Risk Warning

Monitor changes in market sentiment to promptly identify potential risk signals

🚀 DeBERTa-v3-base Finetuned for Finance Text Classification

This model fine-tunes DeBERTa-v3-base on financial news data for sentiment analysis, offering high accuracy in classifying financial sentiments.

🚀 Quick Start

This model is a fine-tuned version of microsoft/deberta-v3-base on the sentence_50Agree financial-phrasebank + Kaggle Dataset, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle dataset includes Covid-19 sentiment data and can be found here: sentiment-classification-selflabel-dataset.

It achieves the following results on the evaluation set:

Loss: 0.7687
Accuracy: 0.8913
F1: 0.8912
Precision: 0.8927
Recall: 0.8913

✨ Features

Fine-tuned on Financial Data: Trained on a dataset of 4840 financial news categorized by sentiment, including Covid-19 sentiment data.
High Performance: Achieves an accuracy of 0.8913 on the evaluation set, along with strong F1, precision, and recall scores.

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	285	0.4187	0.8399	0.8407	0.8687	0.8399
0.5002	2.0	570	0.3065	0.8755	0.8733	0.8781	0.8755
0.5002	3.0	855	0.4148	0.8775	0.8775	0.8778	0.8775
0.1937	4.0	1140	0.4249	0.8696	0.8699	0.8719	0.8696
0.1937	5.0	1425	0.5121	0.8834	0.8824	0.8831	0.8834
0.0917	6.0	1710	0.6113	0.8775	0.8779	0.8839	0.8775
0.0917	7.0	1995	0.7296	0.8775	0.8776	0.8793	0.8775
0.0473	8.0	2280	0.7034	0.8953	0.8942	0.8964	0.8953
0.0275	9.0	2565	0.6995	0.8834	0.8836	0.8846	0.8834
0.0275	10.0	2850	0.7736	0.8755	0.8755	0.8789	0.8755
0.0186	11.0	3135	0.7173	0.8814	0.8814	0.8840	0.8814
0.0186	12.0	3420	0.7659	0.8854	0.8852	0.8873	0.8854
0.0113	13.0	3705	0.8415	0.8854	0.8855	0.8907	0.8854
0.0113	14.0	3990	0.7577	0.8953	0.8951	0.8966	0.8953
0.0074	15.0	4275	0.7687	0.8913	0.8912	0.8927	0.8913

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1

📄 License

This project is licensed under the MIT License.

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