---
library_name: transformers
tags:
- news analytics
- cryptocurrency
- crypto
- Bitcoin
- Ethereum
- Seq2Seq
language:
- en
base_model:
- facebook/bart-large
---

# 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:
```python
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