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FinBERT is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.

  • Corporate Reports 10-K & 10-Q: 2.5B tokens
  • Earnings Call Transcripts: 1.3B tokens
  • Analyst Reports: 1.1B tokens

More details on FinBERT's pre-training process can be found at: https://arxiv.org/abs/2006.08097

FinBERT can be further fine-tuned on downstream tasks. Specifically, we have fine-tuned FinBERT on an analyst sentiment classification task, and the fine-tuned model is shared at https://huggingface.co/yiyanghkust/finbert-tone

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