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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: transformers |
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metrics: |
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- accuracy |
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tags: |
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- crypto |
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- bitcoin |
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- news |
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- eth |
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- transformers |
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widget: |
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- text: >- |
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Bitcoin Vault (BTCV) traded 5.6% higher against the <mask> during the |
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twenty-four hour period ending at 14:00 PM Eastern on October 7th. In the |
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last week, Bitcoin Vault has traded down 2.7% against the dollar. One |
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Bitcoin Vault coin can now be bought for approximately $2.48 or 0.00012763 |
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BTC on major cryptocurrency exchanges. Bitcoin Vault has a total market cap |
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of $5.20 million and approximately $63,451.00 worth of Bitcoin Vault was |
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traded on exchanges in the last day. Here's how other cryptocurrencies have |
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performed in the last day: Bitcoin (BTC) |
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example_title: MLM 1 |
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- text: >- |
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Good morning. Here's what's <mask>:Prices: Bitcoin started what has |
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historically been a strong month about where it ended a dismal September, |
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holding over $19K.Insights: USDC's stablecoin-fueled model of money, in |
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which the dollar functions as an open 'protocol,' could allow innovation to |
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flourish. But healthy competition is a prerequisite.Catch the latest |
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episodes of CoinDesk TV for insightful interviews with crypto industry |
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leaders and analysis. And sign up for First Mover, our daily newsletter |
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putting the latest moves in crypto markets in context. |
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example_title: MLM 2 |
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pipeline_tag: fill-mask |
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--- |
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CryptoBERT is a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model fine-tuned on a dataset of crypto-related news articles. It is designed to analyze and understand crypto news, providing valuable insights into the rapidly evolving world of cryptocurrencies. |
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## Features |
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- **Domain-Specific Knowledge**: Trained on a diverse dataset of crypto news, CryptoBERT captures domain-specific information, enabling it to understand the unique language and context of the cryptocurrency space. |
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- **Sentiment Analysis**: CryptoBERT is capable of sentiment analysis, helping you gauge the overall sentiment expressed in crypto news articles, whether it's positive, negative, or neutral. |
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- **Named Entity Recognition (NER)**: The model excels in identifying key entities such as cryptocurrency names, organizations, and important figures, enhancing its ability to extract relevant information. |
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- **Fine-tuned for Crypto Jargon**: CryptoBERT is fine-tuned to recognize and understand the specialized jargon commonly used in the crypto industry, ensuring accurate interpretation of news articles. |
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## Usage |
