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{
"name": "50_Math_Problem_Solving_Transformer_DeepMindMath_DL",
"query": "Hi! I need help with a project that uses a Transformer model to solve math problems from the DeepMind Mathematics dataset. Please load the dataset and preprocessing it in `src/data_loader.py`. The preprocessing should parse and standardize the math expressions in a syntactically consistent way so the model can easily process them. Implement the Transformer in `src/model.py`. Also, tune the hyperparameters such as the learning rate and the batch size in `src/train.py`, and save the training loss curve to `results/figures/training_loss_curve.png`. Sample and save some Transformer generated solutions in `results/sample_solutions.txt`. Using your model, create a simple interactive tool with Gradio or Streamlit in `src/interface.py` that can solve various user given math problems. Lastly, generate a report on how the model performs with different types of problems, including model accuracy, error analysis, and future improvement suggestions. Save it as `results/metrics/model_report.md`. Thanks in advance!",
"tags": [
"Natural Language Processing"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "DeepMind Mathematics dataset is loaded in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "Data preprocessing is performed including parsing and standardizing mathematical expressions in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [],
"criteria": "A \"Transformer\" model is implemented in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [
0,
1,
2
],
"criteria": "Hyperparameters such as learning rate and batch size are tuned in `src/train.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
0,
1,
2,
3
],
"criteria": "Model training loss curve is saved as `results/figures/training_loss_curve.png`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
0,
1,
2,
3
],
"criteria": "Some Transformer generated solutions are saved in `results/sample_solutions.txt`.",
"category": "Other",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
0,
1,
2,
3
],
"criteria": "An interactive tool is created allowing users to input mathematical problems and receive solutions using \"Gradio\" or \"Streamlit\" in `src/interface.py`.",
"category": "Human Computer Interaction",
"satisfied": null
},
{
"requirement_id": 7,
"prerequisites": [
0,
1,
2,
3,
4
],
"criteria": "A report is generated containing model accuracy, error analysis, and future improvement suggestions, and saved as `results/metrics/model_report.md`.",
"category": "Other",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The preprocessing step should ensure that the mathematical expressions are standardized in a way that makes them easily processed by the model.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The interactive tool should be capable of handling a wide variety of mathematical problem types.",
"satisfied": null
},
{
"preference_id": 2,
"criteria": "The report should provide insights into how the model handles different types of mathematical problems, identifying specific strengths and areas for improvement.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": false
} |