readme: ---
  language: en
  tags:
  - bert
  - business
  - finance
  license: cc-by-4.0
  datasets:
  - CompanyWeb
  - MD&A Disclosures
  - S2ORC
  ---
  
  # BusinessBERT
  
  An industry-sensitive language model for business applications pretrained on business communication corpora. The model incorporates industry classification (IC) as a pretraining objective besides masked langauge modeling (MLM).
  
  It was introduced in
  [this paper]() and released in
  [this repository]().
  
  ## Model description
  
  We introduce BusinessBERT, an industry-sensitive language model for business applications. The advantage of the model is the training approach focused on incorporating industry information relevant for business related natural language processing (NLP) tasks. 
  We compile three large-scale textual corpora consisting of annual disclosures, company website content and scientific literature representing business communication. In total, the corpora include 2.23 billion token. 
  BusinessBERT builds upon the bidirectional encoder representations from transformer architecture (BERT) and embeds industry information during pretraining in two ways: (1) The business communication corpora contain a variety of industry-specific terminology; (2) We employ industry classification (IC) as an additional pretraining objective for text documents originating from companies.
  BusinessBERT is evaluated on business related NLP tasks including text classification, named entity recognition, sentiment analysis and question answering. We find that BusinessBERT substantially improves downstream performance for business related NLP tasks.
  
  ## Intended uses & limitations
  
  The model is intended to be fine-tuned on business related NLP tasks, i.e. sequence classification, named entity recognition, sentiment analysis or question answering.
  
  ### How to use
  
  [More Information Needed]
  
  ### Limitations and bias
  
  [More Information Needed]
  
  ## Training data
  
  - [CompanyWeb](https://huggingface.co/datasets/anonymous20221014/submissiondata2022): 0.77 billion token, 3.5 GB raw text file
  - [MD&A Disclosures](https://data.caltech.edu/records/1249): 1.06 billion token, 5.1 GB raw text file
  - [Semantic Scholar Open Research Corpus](https://api.semanticscholar.org/corpus): 0.40 billion token, 1.9 GB raw text file
  
  ## Evaluation results
  
  [More Information Needed]
  
  <!-- When fine-tuned on downstream tasks, this model achieves the following results:
  
  Glue test results:
  
  | Task | MNLI-(m/mm) | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  | Average |
  |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
  |      | 84.6/83.4   | 71.2 | 90.5 | 93.5  | 52.1 | 85.8  | 88.9 | 66.4 | 79.6    | -->
  
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @misc{title_year,
        title={TITLE},
        author={AUTHORS},
        year={2022},
  }
  ```