financial NLP | BestHub

DataFunTalk

Jun 10, 2023 · Artificial Intelligence

Financial Event Analysis and Applications Based on Pre-trained Models

This article introduces the tasks, techniques, and frameworks for financial event analysis using pre‑trained language models, covering unstructured data parsing, event semantics, graph construction, detection, extraction, and prediction, and presents the TDE‑GTEE model that achieves state‑of‑the‑art performance even in few‑shot scenarios.

AIEvent Extractionevent graph

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Financial Event Analysis and Applications Based on Pre-trained Models

DataFunTalk

Jun 16, 2022 · Artificial Intelligence

BigBang Transformer (BBT): A 1‑Billion‑Parameter Financial Pre‑trained Language Model with Time‑Series‑Text Cross‑Modal Architecture

The BigBang Transformer (BBT) is a 1‑billion‑parameter financial pre‑trained language model that combines text and time‑series data in a cross‑modal Transformer architecture, achieving up to 10% higher downstream accuracy than T5‑scale models and demonstrating strong performance on financial NLP tasks, time‑series forecasting, and multi‑factor investment strategies.

Big Dataartificial intelligencecross‑modal

0 likes · 19 min read

BigBang Transformer (BBT): A 1‑Billion‑Parameter Financial Pre‑trained Language Model with Time‑Series‑Text Cross‑Modal Architecture

DataFunTalk

Oct 24, 2020 · Artificial Intelligence

FinBERT 1.0: An Open‑Source Chinese Financial Domain Pre‑trained BERT Model and Its Evaluation

FinBERT 1.0 is an open‑source Chinese BERT model pre‑trained on large‑scale financial corpora that achieves 2‑5 % F1 improvements across multiple downstream fintech tasks without additional tuning, demonstrating the value of domain‑specific pre‑training for natural language processing.

BERTFinBERTchinese

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FinBERT 1.0: An Open‑Source Chinese Financial Domain Pre‑trained BERT Model and Its Evaluation

DataFunTalk

Jun 28, 2020 · Artificial Intelligence

Applying UDA Semi‑Supervised Learning to Financial Text Classification: Experiments and Insights

This article investigates the practical performance of Google’s 2019 Unsupervised Data Augmentation (UDA) framework on real‑world financial text classification tasks, detailing experiments with limited labeled data, domain‑out‑of‑distribution samples, noisy labels, and comparisons between BERT and lightweight TextCNN models.

BERTSemi-supervised LearningText Classification

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Applying UDA Semi‑Supervised Learning to Financial Text Classification: Experiments and Insights