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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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  ### Model Description
 
 
 
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 
 
 
 
 
 
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
 
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  [More Information Needed]
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  ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
 
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- [More Information Needed]
 
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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+ # Model Card for Model bpavlsh/bart-crypto-summary
 
 
 
 
 
 
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  ### Model Description
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+ Fine-tuned model for analysing cryptocurrency news for the following crypto coins:
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+ Bitcoin, Ethereum, Tether, Solana, Binance Coin. Max input size for texts is 1024 tokens that is about
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+ 3.5K chars of texts. Model outputs short text summary and list coins for uptrend and downtrend.
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+ ## How to Get Started with the Model
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ Use the code below to get started with the model:
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+ summarizer = pipeline("summarization", model = "bpavlsh/bart-crypto-summary")
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+ summarizer = pipeline("summarization", model = "bpavlsh/bart-crypto-summary")
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+ txt="""
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+ Crypto market shows mixed signals. Bitcoin (BTC) and Ethereum (ETH) is experiencing a slight downturn, weighed down by bearish
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+ investor sentiment, while Solana (SOL) see sharp uptrends driven by increased on-chain activity.
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+ """
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+ result=summarizer(txt, early_stopping=True)[0]['summary_text']
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+ print(result)
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+ Result:
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+ Bitcoin and Ethereum are experiencing a slight downturn with bearish investor sentiment, while Solana shows a strong uptrend driven by increased on-chain activity.
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+ Uptrend: Solana.
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+ Downtrend: Bitcoin, Ethereum.
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  [More Information Needed]
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  ## Glossary [optional]
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+ ## Disclaimer
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+ We are sharing a considered approach, ideas and results for academic purpose only,
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+ not any financial advice or recommendations for real business or investment.
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+ ## Model Card Contact
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+ B. Pavlyshenko https://www.linkedin.com/in/bpavlyshenko
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+ ## References
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+ 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
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+ 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
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+ 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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