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README.md

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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  
+> - *(optional but recommended)* `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"  # replace with your repo id
+
+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}}
+}