Add paper link, GitHub link, and improve dataset description

README.md

@@ -3,120 +3,163 @@ license: mit
 task_categories:
 - text-classification
 tags:
-  - biology
-  - genomics
-  - long-context
+- biology
+- genomics
+- long-context
+papers: 2502.07272
 configs:
 - config_name: drug_resistence_prediction
   data_files:
   - split: train
-    path: "drug_resistence_prediction/train.parquet"
+    path: drug_resistence_prediction/train.parquet
   - split: test
-    path: "drug_resistence_prediction/test.parquet"
+    path: drug_resistence_prediction/test.parquet
 - config_name: fitness_prediction_Ammonium-chloride_N
   data_files:
   - split: train
-    path: "fitness_prediction_Ammonium-chloride_N/train.parquet"
+    path: fitness_prediction_Ammonium-chloride_N/train.parquet
   - split: test
-    path: "fitness_prediction_Ammonium-chloride_N/test.parquet"
+    path: fitness_prediction_Ammonium-chloride_N/test.parquet
 - config_name: fitness_prediction_Cisplatin_stress
   data_files:
   - split: train
-    path: "fitness_prediction_Cisplatin_stress/train.parquet"
+    path: fitness_prediction_Cisplatin_stress/train.parquet
   - split: test
-    path: "fitness_prediction_Cisplatin_stress/test.parquet"
+    path: fitness_prediction_Cisplatin_stress/test.parquet
 - config_name: fitness_prediction_D-Alanine_N
   data_files:
   - split: train
-    path: "fitness_prediction_D-Alanine_N/train.parquet"
+    path: fitness_prediction_D-Alanine_N/train.parquet
   - split: test
-    path: "fitness_prediction_D-Alanine_N/test.parquet"
+    path: fitness_prediction_D-Alanine_N/test.parquet
 - config_name: fitness_prediction_L-Arabinose_C
   data_files:
   - split: train
-    path: "fitness_prediction_L-Arabinose_C/train.parquet"
+    path: fitness_prediction_L-Arabinose_C/train.parquet
   - split: test
-    path: "fitness_prediction_L-Arabinose_C/test.parquet"
+    path: fitness_prediction_L-Arabinose_C/test.parquet
 - config_name: fitness_prediction_L-Histidine_nutrient
   data_files:
   - split: train
-    path: "fitness_prediction_L-Histidine_nutrient/train.parquet"
+    path: fitness_prediction_L-Histidine_nutrient/train.parquet
   - split: test
-    path: "fitness_prediction_L-Histidine_nutrient/test.parquet"
+    path: fitness_prediction_L-Histidine_nutrient/test.parquet
 - config_name: fitness_prediction_LB-10C
   data_files:
   - split: train
-    path: "fitness_prediction_LB-10C/train.parquet"
+    path: fitness_prediction_LB-10C/train.parquet
   - split: test
-    path: "fitness_prediction_LB-10C/test.parquet"
+    path: fitness_prediction_LB-10C/test.parquet
 - config_name: fitness_prediction_LB-20C
   data_files:
   - split: train
-    path: "fitness_prediction_LB-20C/train.parquet"
+    path: fitness_prediction_LB-20C/train.parquet
   - split: test
-    path: "fitness_prediction_LB-20C/test.parquet"
+    path: fitness_prediction_LB-20C/test.parquet
 - config_name: fitness_prediction_LB-30C
   data_files:
   - split: train
-    path: "fitness_prediction_LB-30C/train.parquet"
+    path: fitness_prediction_LB-30C/train.parquet
   - split: test
-    path: "fitness_prediction_LB-30C/test.parquet"
+    path: fitness_prediction_LB-30C/test.parquet
 - config_name: fitness_prediction_LB-pH6
   data_files:
   - split: train
-    path: "fitness_prediction_LB-pH6/train.parquet"
+    path: fitness_prediction_LB-pH6/train.parquet
   - split: test
-    path: "fitness_prediction_LB-pH6/test.parquet"
+    path: fitness_prediction_LB-pH6/test.parquet
 - config_name: fitness_prediction_LB-pH8
   data_files:
   - split: train
-    path: "fitness_prediction_LB-pH8/train.parquet"
+    path: fitness_prediction_LB-pH8/train.parquet
   - split: test
-    path: "fitness_prediction_LB-pH8/test.parquet"
+    path: fitness_prediction_LB-pH8/test.parquet
 - config_name: fitness_prediction_Min-media-glucose_nutrient
   data_files:
   - split: train
-    path: "fitness_prediction_Min-media-glucose_nutrient/train.parquet"
+    path: fitness_prediction_Min-media-glucose_nutrient/train.parquet
   - split: test
-    path: "fitness_prediction_Min-media-glucose_nutrient/test.parquet"
+    path: fitness_prediction_Min-media-glucose_nutrient/test.parquet
 - config_name: fitness_prediction_Pyruvate_C
   data_files:
   - split: train
-    path: "fitness_prediction_Pyruvate_C/train.parquet"
+    path: fitness_prediction_Pyruvate_C/train.parquet
   - split: test
-    path: "fitness_prediction_Pyruvate_C/test.parquet"
+    path: fitness_prediction_Pyruvate_C/test.parquet
 - config_name: fitness_prediction_perchlorate_stress
   data_files:
   - split: train
-    path: "fitness_prediction_perchlorate_stress/train.parquet"
+    path: fitness_prediction_perchlorate_stress/train.parquet
   - split: test
-    path: "fitness_prediction_perchlorate_stress/test.parquet"
+    path: fitness_prediction_perchlorate_stress/test.parquet
 - config_name: gene_classification_bacteria
   data_files:
   - split: train
-    path: "gene_classification_bacteria/train.parquet"
+    path: gene_classification_bacteria/train.parquet
   - split: test
-    path: "gene_classification_bacteria/test.parquet"
+    path: gene_classification_bacteria/test.parquet
 - config_name: taxonomic_classification_mixed_marker
   data_files:
   - split: train
-    path: "taxonomic_classification_mixed_marker/train.parquet"
+    path: taxonomic_classification_mixed_marker/train.parquet
   - split: test
-    path: "taxonomic_classification_mixed_marker/test.parquet"
+    path: taxonomic_classification_mixed_marker/test.parquet
 - config_name: taxonomic_classification_random_fragment
   data_files:
   - split: train
-    path: "taxonomic_classification_random_fragment/train.parquet"
+    path: taxonomic_classification_random_fragment/train.parquet
   - split: test
-    path: "taxonomic_classification_random_fragment/test.parquet"
+    path: taxonomic_classification_random_fragment/test.parquet
 - config_name: taxonomic_classification_ssu
   data_files:
   - split: train
-    path: "taxonomic_classification_ssu/train.parquet"
+    path: taxonomic_classification_ssu/train.parquet
   - split: test
-    path: "taxonomic_classification_ssu/test.parquet"
+    path: taxonomic_classification_ssu/test.parquet
 ---
 
-## Abouts
+# Prokaryotic Gener Tasks
 
-In this repository, we present Prokaryotic Gener Tasks, a suite of biologically meaningful benchmark tasks in the prokaryotic domain. 
+[**Paper**](https://arxiv.org/abs/2502.07272) | [**GitHub**](https://github.com/generteam/generator)
+
+Prokaryotic Gener Tasks is a suite of biologically meaningful benchmark tasks in the prokaryotic domain, introduced as part of the **GENERator** project. GENERator is a generative genomic foundation model for long-context DNA modeling, pre-trained on expansive DNA datasets derived from the RefSeq database.
+
+## Dataset Summary
+
+This collection includes various downstream tasks designed to evaluate the sequence understanding (classification and regression) capabilities of genomic foundation models in the prokaryotic domain. The benchmark includes:
+
+- **Drug Resistance Prediction**: Predicting the resistance profiles of prokaryotic sequences to various drugs.
+- **Fitness Prediction**: Estimating the biological fitness of organisms under diverse environmental conditions, including temperature variations (LB-10C, LB-20C, LB-30C), pH levels (pH6, pH8), and nutrient stresses (Ammonium-chloride, Pyruvate, etc.).
+- **Gene Classification**: Classification of genes within the bacterial domain.
+- **Taxonomic Classification**: Classifying sequences into phylogenetic categories using various markers such as Small Subunit (SSU) sequences, random fragments, or mixed markers.
+
+## Usage
+
+To evaluate a model on these tasks using the official [GENERator implementation](https://github.com/generteam/generator), you can use the following command structure:
+
+```shell
+# Using single GPU for sequence understanding
+python src/tasks/downstream/sequence_understanding.py \
+    --model_name GenerTeam/GENERator-eukaryote-1.2b-base \
+    --dataset_name GenerTeam/prokaryotic-gener-tasks \
+    --subset_name gene_classification_bacteria \
+    --batch_size 32 \
+    --problem_type classification \
+    --main_metrics accuracy
+```
+
+Replace `--subset_name` with one of the configurations (e.g., `taxonomic_classification_ssu` or `fitness_prediction_LB-10C`) and adjust `--problem_type` (classification or regression) as appropriate.
+
+## Citation
+
+```bibtex
+@misc{wu2025generator,
+      title={GENERator: A Long-Context Generative Genomic Foundation Model}, 
+      author={Wei Wu and Qiuyi Li and Mingyang Li and Kun Fu and Fuli Feng and Jieping Ye and Hui Xiong and Zheng Wang},
+      year={2025},
+      eprint={2502.07272},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.07272}, 
+}
+```