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| <p align="center"> | |
| <img src="./assets/simplyfi-banner.png" alt="SimplyFI — AI Agents for Banking" width="100%" /> | |
| </p> | |
| <h1 align="center">SimplyFI — AI Agents for Banking</h1> | |
| <p align="center"> | |
| Intelligent automation for <b>Customer Engagement</b>, <b>Compliance</b>, <b>Lending</b>, and <b>Trade Finance</b>.<br/> | |
| Models, datasets, and Spaces for document understanding, risk, and decisioning — production-first and audit-ready. | |
| </p> | |
| <p align="center"> | |
| <a href="[website]"><img alt="Website" src="https://img.shields.io/badge/Website-SimplyFI-blue.svg"></a> | |
| <a href="[linkedin]"><img alt="LinkedIn" src="https://img.shields.io/badge/LinkedIn-@SimplyFI-informational.svg"></a> | |
| <a href="mailto:[email]"><img alt="Contact" src="https://img.shields.io/badge/Contact-hello%40simplyfi.in-success.svg"></a> | |
| <img alt="Industry" src="https://img.shields.io/badge/Industry-Banking%20%26%20Financial%20Services-8A2BE2.svg"> | |
| </p> | |
| --- | |
| ## What we publish | |
| - **Models**: OCR/IE, invoice understanding, entity linking (KYC/AML), credit signals, dialogue agents (RAG+RLHF). | |
| - **Datasets**: Synthetic & anonymized corpora for invoices, SWIFT/LC patterns, trade compliance, FAQs. | |
| - **Spaces**: Demo apps for **document triage**, **explainable decisions**, and **agentic workflows**. | |
| - **Notebooks**: End-to-end examples — from fine-tuning to human feedback (RLHF) and safe deployment. | |
| > Prefer **bank-grade** controls? We support **gated access**, **EULAs**, and **private org Spaces** for sensitive assets. | |
| --- | |
| ## Highlights | |
| - **SIMBA** — our production platform for **Trade Finance & Banking AI**: OCR ➜ extraction ➜ validation ➜ decisioning | |
| - **Agentic Workflows** — multi-tool agents with grounded retrieval, guardrails, and human-in-the-loop review | |
| - **Compliance by design** — redaction, PII handling, lineage, prompt/trace logging, and model cards with usage guidance | |
| --- | |
| ## Explore | |
| ### Featured models | |
| | Purpose | Model | Tasks | Notes | | |
| |---|---|---|---| | |
| | Invoice Understanding | [`SimplyFI/invoice-extractor`](https://huggingface.co/SimplyFI/invoice-extractor) | token-classification, table-qa | line-item, taxes, totals, vendor | | |
| | Trade Compliance NER | [`SimplyFI/trade-ner-compliance`](https://huggingface.co/SimplyFI/trade-ner-compliance) | token-classification | sanctions terms, ports, HS codes | | |
| | Banking Assistant (SFT) | [`SimplyFI/banking-assistant-sft`](https://huggingface.co/SimplyFI/banking-assistant-sft) | text-generation | safe, concise answers with citations | | |
| | Risk Signals | [`SimplyFI/risk-signal-classifier`](https://huggingface.co/SimplyFI/risk-signal-classifier) | sequence-classification | escalations & review routing | | |
| > Replace links above with your actual repos (or keep as placeholders until published). | |
| ### Example: quick inference | |
| ```python | |
| from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline | |
| repo = "SimplyFI/invoice-extractor" # update if different | |
| tok = AutoTokenizer.from_pretrained(repo) | |
| mdl = AutoModelForTokenClassification.from_pretrained(repo) | |
| ner = pipeline("token-classification", model=mdl, tokenizer=tok, aggregation_strategy="simple") | |
| text = "Invoice 9081 from Alpha Plastics Pvt Ltd, Total ₹1,24,560 due on 2025-11-15" | |
| print(ner(text)) | |