instance_id int64 1 102 | domain stringclasses 4
values | subtask_categories stringlengths 13 82 | github_name stringlengths 9 47 | task_inst stringlengths 150 1.08k | domain_knowledge stringlengths 86 1.75k ⌀ | dataset_folder_tree stringlengths 29 2.6k | dataset_preview stringlengths 175 25k ⌀ | src_file_or_path stringlengths 5 89 ⌀ | gold_program_name stringlengths 5 45 | output_fname stringlengths 21 69 | eval_script_name stringlengths 12 55 |
|---|---|---|---|---|---|---|---|---|---|---|---|
101 | Computational Chemistry | Deep Learning | ppdebreuck/modnet | Train a MODNet model for predicting experimental band gap using the examples in the matbench_expt_gap_train dataset. The model could use 150 input features and has 256, 128, 16, 16 neurons in each layer. Use 'elu' as the activation function. Your target attribute is 'gap_expt_eV'. Predict experimental band gap with the... | MODNetModel takes a 4-tuple of lists of integers for the num_neurons argument: ([], [], [], []). | |-- experimental_band_gap/
|---- matbench_expt_gap_train
|---- matbench_expt_gap_test | null | example_notebooks/composition_example.ipynb | experimental_band_gap_prediction.py | pred_results/experimental_band_gap_prediction_pred.csv | eval_experimental_band_gap_prediction.py |
102 | Computational Chemistry | Deep Learning | ppdebreuck/modnet | Train a MODNet model for predicting the refractive index for materials using examples in the md_ref_index_train file. The MODNet model should use 300 input features and 128, 64, 32 neurons in each layer. Use 'elu' as the activation function. Predict refractive index for the materials in the MP_2018.6 dataset and save t... | MODNetModel takes a 4-tuple of lists of integers for the num_neurons argument: ([], [], [], []). In this task, an empty list should be appended to make the number of tuple equal to 4, even though there are only three layers of input neurons (128, 64, and 32). | |-- ref_index/
|---- MP_2018.6
|---- md_ref_index_train | null |
example_notebooks/training_ref_index.ipynb
example_notebooks/predicting_ref_index.ipynb
| refractive_index_prediction.py | pred_results/ref_index_predictions_pred.csv | eval_refractive_index_prediction.py |
Subsets and Splits
Notebook Validation Entries
Retrieves a limited number of records from the validation dataset that are related to a specific notebook, providing basic filtering but minimal analytical insight.
SQL Console for osunlp/ScienceAgentBench
The query performs a basic filter to extract all records related to the domain 'Geographical Information Science' but doesn't provide significant analytical insights or exploratory patterns.