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---
language: en
license: other
tags:
- finance
- risk-relation
- retrieval
- encoder
- feature-extraction
- stock-prediction
pipeline_tag: feature-extraction
---
# 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}}
}
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