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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+# OccCANINE: I-CeM Occupational Classification (OCCICEM)
+
+## Overview
+
+OccCANINE_OCCICEM is a version of [OccCANINE](https://github.com/christianvedels/OccCANINE) fine-tuned to automatically convert English occupational descriptions into [I-CeM](https://www.essex.ac.uk/research-projects/integrated-census-microdata) (Integrated Census Microdata) occupational codes. It uses a CANINE encoder with a sequential decoder trained using a mixed loss, fine-tuned from the [OccCANINE_s2s_mix](https://huggingface.co/Christianvedel/OccCANINE_s2s_mix) base model on IPUMS UK census data.
+
+See more on: [GitHub.com/christianvedels/OccCANINE](https://github.com/christianvedels/OccCANINE)
+
+Read the paper on arXiv: [https://arxiv.org/abs/2402.13604](https://arxiv.org/abs/2402.13604)
+
+## Key Features
+
+- **English**: Trained and evaluated on English occupational descriptions.
+- **Sequential decoding**: Outputs I-CeM codes digit-by-digit.
+- **Mixed loss training**: Combines sequence-level and flat classification losses.
+- **Fine-tuned**: Initialized from OccCANINE_s2s_mix and fine-tuned on IPUMS UK I-CeM data.
+
+## Usage
+
+```python
+from histocc import OccCANINE
+
+model = OccCANINE(name="OccCANINE_OCCICEM", system="OCCICEM", hf=True)
+
+result = model.predict("blacksmith", lang="en")
+```
+
+## Contribution and Support
+
+Developed at the University of Southern Denmark by Christian Møller Dahl, Torben Johansen and Christian Vedel.
+
+---
+
+**Model Details:**
+- **Task**: Text Classification / Sequence Generation
+- **Base Model**: CANINE (fine-tuned from OccCANINE_s2s_mix)
+- **Target system**: I-CeM (OCCICEM)
+- **Language**: English
+- **Framework**: Transformers / PyTorch
+- **License**: Apache 2.0
+- **Paper**: arXiv 2402.13604