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---
license: llama3.1
datasets:
- BAAI/OPI
language:
- en
pipeline_tag: text-generation
tags:
- Life Science
- AI4Science
- Biology
- Protein
- LLM
- Instruction
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
---
![OPI_logo](demo_figures/OPI_logo.png)
# Github:
https://github.com/baaihealth/opi
# Paper:
[OPI: An Open Instruction Dataset for Adapting Large Language Models to Protein-Related Tasks](https://neurips.cc/virtual/2024/105921) has been accepted by [NeurIPS 2024 Workshop: Foundation Models for Science: Progress, Opportunities, and Challenges](https://neurips.cc/virtual/2024/workshop/84714).
# Model Card of OPI-Llama-3.1-8B-Instruct
OPI-Llama-3.1-8B-Instruct was fine-tuned from the Meta-Llama-3.1-8B-Instruct model using the complete OPI training set (i.e.,[OPI_full_1.61M_train.json](https://huggingface.co/datasets/BAAI/OPI/blob/main/OPI_DATA/OPI_full_1.61M_train.json)).
For more details of training and testing, please visit [https://github.com/baaihealth/opi](https://github.com/baaihealth/opi).
![Overview](demo_figures/OPI_experiment_outline.png)
# Evaluation of OPI-Llama-3.1-8B-Instruct on 9 tasks
Each testing result is derived from the Meta-Llama-3.1-8B-Instruct model that has been fine-tuned using [OPI_full_1.61M.json](https://huggingface.co/datasets/BAAI/OPI/blob/main/OPI_DATA/OPI_full_1.61M_train.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>CLEAN_EC_number_new_test</td>
<td>-</td>
<td>0.3724</td>
<td>0.3374</td>
<td>0.3468</td>
<td>-</td>
</tr>
<tr>
<td>CLEAN_EC_number_price_test</td>
<td>-</td>
<td>0.0738</td>
<td>0.0738</td>
<td>0.0738</td>
<td>-</td>
</tr>
<tr>
<td rowspan="3">Fold Type Prediction</td>
<td>fold_type_test_Fold_Holdout</td>
<td>0.1045</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
</tr>
<tr>
<td>fold_type_test_Superfamily_Holdout</td>
<td>0.1507</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
</tr>
<tr>
<td>fold_type_test_Family_Holdout</td>
<td>0.6145</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
</tr>
<tr>
<td>Subcellular Localization Prediction</td>
<td>subcell_loc_test</td>
<td>0.4214</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
</tr>
<tr>
<td rowspan="9">Annotation Prediction</td>
<td rowspan="3">Function Keywords Prediction</td>
<td>CASPSimilarSeq_keywords_test</td>
<td>-</td>
<td>0.4202</td>
<td>0.5057</td>
<td>0.4385</td>
<td>-</td>
</tr>
<tr>
<td>IDFilterSeq_keywords_test</td>
<td>-</td>
<td>0.6762</td>
<td>0.6905</td>
<td>0.6650</td>
<td>-</td>
</tr>
<tr>
<td>UniProtSeq_keywords_test</td>
<td>-</td>
<td>0.7606</td>
<td>0.7489</td>
<td>0.7374</td>
<td>-</td>
</tr>
<tr>
<td rowspan="3">Gene Ontology(GO) Terms Prediction</td>
<td>CASPSimilarSeq_go_terms_test</td>
<td>-</td>
<td>0.1113</td>
<td>0.0936</td>
<td>0.099</td>
<td>-</td>
</tr>
<tr>
<td>IDFilterSeq_go_terms_test</td>
<td>-</td>
<td>0.6686</td>
<td>0.6287</td>
<td>0.6304</td>
<td>-</td>
</tr>
<tr>
<td>UniProtSeq_go_terms_test</td>
<td>-</td>
<td>0.7150</td>
<td>0.6897</td>
<td>0.6849</td>
<td>-</td>
</tr>
<tr>
<td rowspan="3">Function Description Prediction</td>
<td>CASPSimilarSeq_function_test</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>0.7524</td>
</tr>
<tr>
<td>IDFilterSeq_function_test</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>0.4786</td>
</tr>
<tr>
<td>UniProtSeq_function_test</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>0.5144</td>
</tr>
<tr>
<td rowspan="3">Knowledge Mining</td>
<td>Tissue Location Prediction from Gene Symbol</td>
<td>gene_symbol_to_tissue_test</td>
<td>-</td>
<td>0.4002</td>
<td>0.9356</td>
<td>0.5466</td>
<td>-</td>
</tr>
<tr>
<td>Cancer Prediction from Gene Symbol</td>
<td>gene_symbol_to_cancer_test</td>
<td>-</td>
<td>0.2890</td>
<td>0.2701</td>
<td>0.2664</td>
<td>-</td>
</tr>
<tr>
<td>Cancer Prediction from Gene Name</td>
<td>gene_name_to_cancer_test</td>
<td>-</td>
<td>0.2786</td>
<td>0.2707</td>
<td>0.2659</td>
<td>-</td>
</tr>
</tbody>
</table>
# Prediction comparison with SOTA mdoels
![model_compare](model_compare/task1_EC_number.png)
![model_compare](model_compare/task2_fold_type.png)
![model_compare](model_compare/task3_subcell_loc.png)
![model_compare](model_compare/task4_keywords.png)
![model_compare](model_compare/task5_GO.png)
![model_compare](model_compare/task6_function.png)
![model_compare](model_compare/task7_gsymbol2tissue.png)
![model_compare](model_compare/task8_gsymbol2cancer.png)
![model_compare](model_compare/task9_gname2cancer.png)