language: en
tags:
- Financial Language Modelling
widget:
- text: Stocks rallied and the British pound <mask>.
Dataset Summary
- Homepage: https://salt-nlp.github.io/FLANG/
- Models: https://huggingface.co/SALT-NLP/FLANG-BERT
- Repository: https://github.com/SALT-NLP/FLANG
FLANG
FLANG is a set of large language models for Financial LANGuage tasks. These models use domain specific pre-training with preferential masking to build more robust representations for the domain. The models in the set are:
FLANG-BERT
FLANG-SpanBERT
FLANG-DistilBERT
FLANG-Roberta
FLANG-ELECTRA
FLANG-ELECTRA
FLANG-ELECTRA is a pre-trained language model which uses financial keywords and phrases for preferential masking of domain specific terms. It is built by further training the ELECTRA language model in the finance domain with improved performance over previous models due to the use of domain knowledge and vocabulary.
FLUE
FLUE (Financial Language Understanding Evaluation) is a comprehensive and heterogeneous benchmark that has been built from 5 diverse financial domain specific datasets.
Sentiment Classification: Financial PhraseBank
Sentiment Analysis, Question Answering: FiQA 2018
New Headlines Classification: Headlines
Named Entity Recognition: NER
Structure Boundary Detection: FinSBD3
Citation
Please cite the model with the following citation:
@INPROCEEDINGS{shah-etal-2022-flang,
author = {Shah, Raj Sanjay and
Chawla, Kunal and
Eidnani, Dheeraj and
Shah, Agam and
Du, Wendi and
Chava, Sudheer and
Raman, Natraj and
Smiley, Charese and
Chen, Jiaao and
Yang, Diyi },
title = {When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain},
booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
year = {2022},
publisher = {Association for Computational Linguistics}
}
Contact information
Please contact Raj Sanjay Shah (rajsanjayshah[at]gatech[dot]edu) or Sudheer Chava (schava6[at]gatech[dot]edu) or Diyi Yang (diyiy[at]stanford[dot]edu) about any FLANG-ELECTRA related issues and questions.