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README.md
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---
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license:
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---
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---
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license: mit
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language:
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- en
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tags:
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- biology
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- rna
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- rna-language-model
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- rna-secondary-structure
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- shifu
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library_name: pytorch
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pipeline_tag: fill-mask
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---
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<!-- Copy this file into each HuggingFace repo as README.md and fill the LMR-v0/289M fields. -->
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# LMR-v0 (SHIFU / LMR foundational backbone)
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`LMR-v0` is a foundational RNA language-model backbone (289M parameters) from
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**SHIFU: an integrated framework for deep learning of RNA secondary structure**
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(Galvez and Vicens, Vicens Lab, University of Houston).
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It is pretrained by masked language modeling on RNA sequence and serves as the
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backbone for downstream RNA structure tasks. The three released backbones are:
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| Model | Params | Context | Repo |
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|---|---|---|---|
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| LMR-v0 | 289M | 512 | GaboG7/LMR-v0 |
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| LMR-G | 228M | 512 | GaboG7/LMR-G |
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| LMR-nano | 65M | 512 | GaboG7/LMR-mini |
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| LMR-Long | 290M | 4,096 | GaboG7/LMR-Long |
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## Usage
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```python
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from huggingface_hub import snapshot_download
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path = snapshot_download("GaboG7/LMR-v0") # LMR-v0 / LMR-G / LMR-mini
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# load with the LMR code: https://github.com/Vicens-Lab/LMR
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```
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Training code, configs, and the finetuning recipe are in the GitHub repo:
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**https://github.com/Vicens-Lab/LMR**
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## Training data
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Pretrained on RNA sequence data; the downstream 2D structure benchmark
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(**SHIFU-Corpus**, 254,123 sequences, leakage-audited, family-aware splits) is a
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separate release.
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## Intended use and limitations
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A general-purpose RNA sequence backbone for finetuning (e.g. secondary-structure
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prediction). It is a sequence model, not a standalone structure predictor. Inputs
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cap at the context length in the config.
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## License
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MIT (code and weights). SHIFU-Corpus retains the licenses of its six source databases.
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## Citation
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This work is not yet published; a citation will be added when available.
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```bibtex
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@misc{lmr_shifu,
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title = {SHIFU: an integrated framework for deep learning of RNA secondary structure},
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author = {Galvez, Gabriel and Vicens, Quentin},
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note = {Manuscript in preparation. Citation to be updated.},
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year = {TODO}
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}
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```
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