psnonis / Finbert
BERT for Finance : UC Berkeley MIDS w266 Final Project
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FinBERT: Pre-Trained on SEC Filings for Financial NLP Tasks
Vinicio DeSola, Kevin Hanna, Pri Nonis
MODEL WEIGHTS
PUBLICATION
MOTIVATIONS
Goal 1 FinBERT-Prime_128MSL-500K+512MSL-10K vs BERT
- Compare mask LM prediction accurracy on technical financial sentences
- Compare analogy on financial relationships
Goal 2 FinBERT-Prime_128MSL-500K vs FinBERT-Pre2K_128MSL-500K
- Compare mask LM prediction accuracy on financial news from 2019
- Compare analogy on financial relationship, measure shift in understanding : risk vs climate in 1999 vs 2019
Goal 3 FinBERT-Prime_128MSL-500K vs FinBERT-Prime_128MSK-500K+512MSL-10K
- Compare mask LM prediction accuracy on long financial sentences
Goal 4 FinBERT-Combo_128MSL-250K vs FinBERT-Prime_128MSL-500K+512MSL-10K
- Compare mask LM prediction accuracy on financial sentences : can we get same accuracy with less training by building on original BERT weights.
TERMINOLOGY
-
PrimePre-trained from scratch on 2017, 2018, 2019 SEC 10K dataset -
Pre2KPre-traind from scratch on 1998, 1999 SEC 10K dataset -
ComboPre-trained continued from original BERT on 2017, 2018, 2019 SEC 10K dataset
ANALYSIS
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