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---
library_name: transformers
tags: []
---
# Model Card for Model bpavlsh/bart-crypto-summary
### Model Description
Fine-tuned model for analysing 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 crypto coins.
## 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 approach, ideas and results for academic purpose only,
not any financial advice or recommendations for real business or investment.
## Model Card Contact
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
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