Seq2Seq Model bpavlsh/bart-crypto-summary

Model Description

Fine-tuned Seq2Seq model is developed for analysing and summarization of cryptocurrency news for the following crypto coins: Bitcoin, Ethereum, Tether, Solana, Binance Coin. Max input size for texts is 1024 tokens that is about 3.5K chars of texts. Model is created by fine-tuning facebook/bart-large transformer model. Model outputs short text summary and uptrend/downtrend lists of specified above crypto coins if their trends are considered in the news text.

How to Get Started with the Model

Use the code below to get started with the model:

summarizer = pipeline("summarization", model = "bpavlsh/bart-crypto-summary")
txt="""
Crypto market shows mixed signals. Bitcoin (BTC) and Ethereum (ETH) is experiencing a slight downturn, weighed down by bearish 
investor sentiment, while Solana (SOL) see sharp uptrends driven by increased on-chain activity. 
"""
result=summarizer(txt, early_stopping=True)[0]['summary_text']
print(result)

Result:
"""
Bitcoin and Ethereum are experiencing a slight downturn with bearish investor sentiment, while Solana shows a strong uptrend driven by increased on-chain activity. 
Uptrend: Solana.
Downtrend: Bitcoin, Ethereum.
"""

Disclaimer

We are sharing a considered model and results for academic purpose only, not any financial advice or recommendations for real business or investment.

Contacts

B. Pavlyshenko https://www.linkedin.com/in/bpavlyshenko

References

Pavlyshenko B.M. Financial News Analytics Using Fine-Tuned Llama 2 GPT Model. arXiv preprint arXiv:2308.13032. 2023. Download PDF: https://arxiv.org/pdf/2308.13032.pdf

Pavlyshenko B.M. Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model. arXiv preprint arXiv:2309.04704. 2023. Download PDF: https://arxiv.org/pdf/2309.04704.pdf

Pavlyshenko, B.M. Bitcoin Price Predictive Modeling Using Expert Correction. 2019 XIth International Scientific and Practical Conference on Electronics and Information Technologies (ELIT), September 16 – 18, 2019 Lviv, Ukraine, pages: 163-167. Download PDF: https://arxiv.org/pdf/2201.02729