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@@ -35,7 +35,9 @@ The model might not perform well in identifying and classifying entities that we
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  ## Training and evaluation data
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- The model was trained using a diverse dataset, including artificially generated tweets and ERC20 token metadata fetched through the [Covalent API](https://www.covalenthq.com/docs/unified-api/). GPT was employed to generate 500 synthetic tweets tailored for the cryptocurrency domain. The Covalent API was instrumental in obtaining a rich set of 20K+ unique ERC20 token metadata entries, enhancing the model's understanding and recognition of cryptocurrency entities.## Training procedure
 
 
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  ### Training hyperparameters
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  ## Training and evaluation data
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+ The model was trained using a diverse dataset, including artificially generated tweets and ERC20 token metadata fetched through the [Covalent API](https://www.covalenthq.com/docs/unified-api/). GPT was employed to generate 500 synthetic tweets tailored for the cryptocurrency domain. The Covalent API was instrumental in obtaining a rich set of 20K+ unique ERC20 token metadata entries, enhancing the model's understanding and recognition of cryptocurrency entities.
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+ ## Training procedure
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  ### Training hyperparameters
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