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william0816/
Dual_View_Financial_Encoder

Name: Dual_View_Financial_Encoder
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Financial Risk Identification through Dual-view Adaptation — Encoder

This repository hosts the pretrained encoder from the work “Financial Risk Identification through Dual-view Adaptation.”
The model is designed to uncover inter-firm risk relations from financial text, supporting downstream tasks such as retrieval, relation mining, and stock-signal experiments where relation strength acts as a feature.

Files

pytorch_model.safetensors — model weights

config.json — model configuration

README.md (this file)

✨ What’s special (Dual-view Adaptation)

The model aligns two complementary “views” of firm relations and adapts them during training:

Lexical view (lex) — focuses on token/phrase-level and domain terms common in 10-K and financial news.
Temporal view (time) — encourages stability/consistency of relations across reporting periods and evolving events.

A two-view combination (“Best”) integrates both signals and yields stronger retrieval quality and more stable risk-relation estimates. Ablations (lex, time) are also supported for analysis.

🔧 Intended Use

Feature extraction / sentence embeddings for paragraphs, sections, or documents in financial filings.
Retrieval & ranking: compute similarities between queries (e.g., “supply chain risk for X”) and candidate passages.
Risk-relation estimation: aggregate cross-document similarities to produce pairwise firm relation scores used in downstream analytics.

⚠️ Not a generative LLM. Use it as an encoder (feature extractor).

🚀 Quickstart (Transformers)

python

import torch
from transformers import AutoTokenizer, AutoModel

MODEL_ID = "william0816/Dual_View_Financial_Encoder"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=True)
model = AutoModel.from_pretrained(MODEL_ID)

def mean_pool(last_hidden_state, attention_mask):
    # Mean-pool w.r.t. the attention mask
    mask = attention_mask.unsqueeze(-1).type_as(last_hidden_state)
    summed = (last_hidden_state * mask).sum(dim=1)
    counts = torch.clamp(mask.sum(dim=1), min=1e-9)
    return summed / counts

texts = [
    "The company faces supplier concentration risk due to a single-source vendor.",
    "Management reported foreign exchange exposure impacting Q4 margins."
]

enc = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
    outputs = model(**enc)
embeddings = mean_pool(outputs.last_hidden_state, enc["attention_mask"])

# Cosine similarity for retrieval
emb_norm = torch.nn.functional.normalize(embeddings, p=2, dim=1)
similarity = emb_norm @ emb_norm.T
print(similarity)

🖇️ Citation

If you use this model or the dual-view methodology, please cite:

bibtex

@misc{financial_risk_dualview_2025,
  title        = {Financial Risk Identification through Dual-view Adaptation},
  author       = {Chiu, Wei-Ning and collaborators},
  year         = {2025},
  note         = {Preprint/Project},
  howpublished = {\url{https://huggingface.co/william0816/Dual_View_Financial_Encoder}}
}