BAAI/OPI-Galactica-6.7B

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Github:

https://github.com/baaihealth/opi

Paper:

OPI: An Open Instruction Dataset for Adapting Large Language Models to Protein-Related Tasks has been accepted by NeurIPS 2024 Workshop: Foundation Models for Science: Progress, Opportunities, and Challenges.

Model Card of OPI-Galactica-6.7B

OPI-Galactica-6.7B was fine-tuned from the Galactica-6.7B model using the complete OPI training set (i.e.,OPI_full_1.61M_train.json). For more details of training and testing, please visit https://github.com/baaihealth/opi.

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Evaluation of OPI-Galactica-6.7B model on 9 tasks

Each testing result is derived from the Galactica-6.7B model that has been fine-tuned using OPI_full_1.61M.json and subsequently evaluated on the respective testing set for each specific task.

<table border="1" style="text-align:center; border-collapse:collapse; width: 100%;"> <thead> <tr> <th style="text-align:center;">Task Type</th> <th style="text-align:center;">Task Name</th> <th style="text-align:center;">Testing file</th> <th style="text-align:center;">Accuracy</th> <th style="text-align:center;">Precision</th> <th style="text-align:center;">Recall</th> <th style="text-align:center;">F1</th> <th style="text-align:center;">Rouge-L</th> </tr> </thead> <tbody> <tr> <td rowspan="6">Sequence Understanding</td> <td rowspan="2">EC Number Prediction (split100)</td> <td>CLEANECnumbernewtest</td> <td>-</td> <td>0.2700</td> <td>0.2663</td> <td>0.2596</td> <td>-</td> </tr> <tr> <td>CLEANECnumberpricetest</td> <td>-</td> <td>0.0268</td> <td>0.0268</td> <td>0.0268</td> <td>-</td> </tr> <tr> <td rowspan="3">Fold Type Prediction</td> <td>foldtypetestFoldHoldout</td> <td>0.0808</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>foldtypetestSuperfamilyHoldout</td> <td>0.1348</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>foldtypetestFamilyHoldout</td> <td>0.4854</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td>Subcellular Localization Prediction</td> <td>subcellloctest</td> <td>0.7771</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> </tr> <tr> <td rowspan="9">Annotation Prediction</td> <td>Function Keywords Prediction</td> <td>CASPSimilarSeqkeywordstest</td> <td>-</td> <td>0.8120</td> <td>0.7360</td> <td>0.7643</td> <td>-</td> </tr> <tr> <td>Function Keywords Prediction</td> <td>IDFilterSeqkeywordstest</td> <td>-</td> <td>0.8377</td> <td>0.8019</td> <td>0.8070</td> <td>-</td> </tr> <tr> <td>Function Keywords Prediction</td> <td>UniProtSeqkeywordstest</td> <td>-</td> <td>0.8596</td> <td>0.8196</td> <td>0.8276</td> <td>-</td> </tr> <tr> <td>Gene Ontology (GO) Terms Prediction</td> <td>CASPSimilarSeqgotermstest</td> <td>-</td> <td>0.7613</td> <td>0.7492</td> <td>0.7476</td> <td>-</td> </tr> <tr> <td>Gene Ontology (GO) Terms Prediction</td> <td>IDFilterSeqgotermstest</td> <td>-</td> <td>0.7404</td> <td>0.7274</td> <td>0.7207</td> <td>-</td> </tr> <tr> <td>Gene Ontology (GO) Terms Prediction</td> <td>UniProtSeqgotermstest</td> <td>-</td> <td>0.7638</td> <td>0.7373</td> <td>0.7358</td> <td>-</td> </tr> <tr> <td>Function Description Prediction</td> <td>CASPSimilarSeqfunctiontest</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>0.7430</td> </tr> <tr> <td>Function Description Prediction</td> <td>IDFilterSeqfunctiontest</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>0.7014</td> </tr> <tr> <td>Function Description Prediction</td> <td>UniProtSeqfunctiontest</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>0.7133</td> </tr> <tr> <td rowspan="3">Knowledge Mining</td> <td>Tissue Location Prediction from Gene Symbol</td> <td>genesymboltotissuetest</td> <td>-</td> <td>0.3917</td> <td>0.9077</td> <td>0.5303</td> <td>-</td> </tr> <tr> <td>Cancer Prediction from Gene Symbol</td> <td>genesymboltocancertest</td> <td>-</td> <td>0.3555</td> <td>0.3189</td> <td>0.3229</td> <td>-</td> </tr> <tr> <td>Cancer Prediction from Gene Name</td> <td>genenametocancer_test</td> <td>-</td> <td>0.2728</td> <td>0.2554</td> <td>0.2533</td> <td>-</td> </tr> </tbody> </table>

Prediction comparison with SOTA mdoels

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Demo

We use the FastChat platform to visually demonstrate the ability of OPI-Galactica-6.7B model on various evaluation tasks.

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