File size: 2,820 Bytes
a37189a 49d8cee a37189a 0950a80 a37189a 73e1c70 d53f7ed 73e1c70 4b9690b 4b55848 f2162f5 a37189a 4b55848 a37189a 4b9690b f413c0d 101e032 4b9690b 4b55848 4b9690b 4b55848 83573d9 4b55848 a37189a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
---
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](https://huggingface.co/SALT-NLP/FLANG-BERT)\
[FLANG-SpanBERT](https://huggingface.co/SALT-NLP/FLANG-SpanBERT)\
[FLANG-DistilBERT](https://huggingface.co/SALT-NLP/FLANG-DistilBERT)\
[FLANG-Roberta](https://huggingface.co/SALT-NLP/FLANG-Roberta)\
[FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA)
## FLANG-DistilBERT
FLANG-DistilBERT 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 DistilBERT 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](https://huggingface.co/datasets/financial_phrasebank)\
Sentiment Analysis, Question Answering: [FiQA 2018](https://huggingface.co/datasets/SALT-NLP/FLUE-FiQA)\
New Headlines Classification: [Headlines](https://www.kaggle.com/datasets/daittan/gold-commodity-news-and-dimensions)\
Named Entity Recognition: [NER](https://paperswithcode.com/dataset/fin)\
Structure Boundary Detection: [FinSBD3](https://sites.google.com/nlg.csie.ntu.edu.tw/finweb2021/shared-task-finsbd-3)
## Citation
Please cite the work with the following citation:
```bibtex
@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-DistilBERT related issues and questions.
---
license: afl-3.0
---
|