Stock-news-distilbert open-source stock news sentiment classification model - Accurately handle financial news text classification

Stock News Distilbert

Developed by KernAI

A stock news sentiment classification model fine-tuned based on the DistilBERT architecture, excelling in financial news text classification tasks

Text Classification

Transformers

Open Source License:MIT #Stock News Sentiment Analysis #Financial Text Classification #Lightweight BERT

Downloads 72

Release Time : 5/21/2023

Model Overview

This model was fine-tuned using Kern AI refinery's HuggingFace adapter on 50,000 stock news articles, specifically designed for sentiment classification of stock and financial news. It outputs three categories: 'positive', 'neutral', 'negative' along with their corresponding confidence scores.

Model Features

Efficient and Lightweight

Based on the DistilBERT architecture, with 40% fewer parameters than standard BERT and 60% faster operation speed

Domain-Specific Optimization

Fine-tuned using 50,000 stock news articles, particularly adept at financial text sentiment analysis

Easy Integration

Supports HuggingFace Pipeline API for quick deployment to production environments

Model Capabilities

Stock news sentiment classification

Financial text analysis

Three-category sentiment recognition

Use Cases

Financial Analysis

Public company news sentiment monitoring

Real-time analysis of sentiment trends in news related to listed companies

Helps investors quickly grasp market sentiment changes

Financial media content analysis

Batch processing of news content published by financial media

Generates media sentiment indices for quantitative trading reference

🚀 Finetuned distilBERT model for stock news classification

This DistilBERT model is designed for stock news classification, offering efficient and accurate sentiment analysis.

🚀 Quick Start

This distilbert model was fine-tuned on 50,000 stock news articles using the HuggingFace adapter from Kern AI refinery. The articles consisted of the headlines plus abstract of the article. For the finetuning, a single NVidia K80 was used for about four hours.

Join our Discord if you have questions about this model: https://discord.gg/MdZyqSxKbe

DistilBERT is a smaller, faster and lighter version of BERT. It was trained by distilling BERT base and has 40% less parameters than bert-base-uncased. It runs 60% faster while preserving over 95% of BERT’s performances as measured on the GLUE language understanding benchmark. DistilBERT does not have token-type embeddings, pooler and retains only half of the layers from Google’s BERT.

✨ Features

The model can handle various text classification tasks, especially when it comes to stock and finance news sentiment classification.
The output of the model are the three classes "positive", "neutral" and "negative" plus the models respective confidence score of the class.
The model was fine-tuned on a custom datasets that was curated by Kern AI and labeled in our tool refinery.
The model is currently supported by the PyTorch framework and can be easily deployed on various platforms using the HuggingFace Pipeline API.

📦 Installation

To use the model, you need to install the HuggingFace Transformers library:

pip install transformers

💻 Usage Examples

Basic Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("KernAI/stock-news-distilbert")
tokenizer = AutoTokenizer.from_pretrained("KernAI/stock-news-distilbert")

Advanced Usage

from transformers import pipeline

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = classifier("This is a positive sentence.")
print(result)
# [{'label': 'POSITIVE', 'score': 0.9998656511306763}]

📄 License

This project is licensed under the MIT license.

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