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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.

Pre-training

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

The entire training is done using an NVIDIA DGX-1 machine. The server has 4 Tesla P100 GPUs, providing a total of 128 GB of GPU memory. This machine enables us to train the BERT models using a batch size of 128. We utilize Horovord framework for multi-GPU training. Overall, the total time taken to perform pretraining for one model is approximately 2 days.

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/demo-org/auditor_review_model

Usage

Load the model directly from Transformers:

from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("demo-org/finbert-pretrain", use_auth_token=True)

Questions

Please contact the Data Science COE if you have more questions about this pre-trained model

Demo Model

This model card is for demo purposes. The original model card for this model is https://huggingface.co/yiyanghkust/finbert-pretrain.

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