Update README.md

README.md

@@ -121,32 +121,54 @@ This dataset presents in-context scenarios where models can outsource the comput
 
 ## Construction Process
 
-We took the original math_qa dataset, parsed the nested formulas, linearized them into a sequence (chain) of operations, and replace all advanced
+We took the original math_qa dataset, parsed the nested formulas, linearized them into a sequence (chain) of operations, and replaced all advanced
 function calls (such as `circle_area`) with explicit elementary operations. We evaluate all the steps in each example and filter out examples if their
-evaluation does not match the answer selected as correct in the data with a 5% tolerance. The sequence of steps is then saved in HTML-like language
-in the `chain` column. We keep the original columns in the dataset for convenience.
+evaluation does not match the answer selected as correct in the data with a 5% tolerance, with about 26k examples remaining. The sequence of steps is then saved in HTML-like language
+in the `chain` column.
 
-You can read more information about this process in our [technical report](https://arxiv.org/abs/2305.15017).
+We also perform in-dataset and cross-dataset data-leak detection within [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
+Specifically for MathQA, we found that majority of validation and test examples are near-duplicates of some example in the train set, and that all validation and test
+examples likely originate from the Aqua-RAT train split. We do not recommend to original validation and test splits of the MathQA dataset.
 
+You can read more information about this process in our [Calc-X paper](https://arxiv.org/abs/2305.15017).
 
-## Content and Data splits
 
-Content and splits correspond to the original math_qa dataset.
-See [mathqa HF dataset](https://huggingface.co/datasets/math_qa) and [official website](https://math-qa.github.io/) for more info.
+## Data splits
 
-Columns:
+In our default configuration, test and validation splits are removed and we recommend using MathQA for training only. You can load it using:
 
-- `question` - th description of a mathematical problem in natural language
-- `options` - dictionary with choices 'A' to 'E' as possible solutions
+```python3
+datasets.load_dataset("MU-NLPC/calc-math_qa")
+```
+
+If you want to use the original dataset splits, you can load it using:
+
+```python3
+datasets.load_dataset("MU-NLPC/calc-math_qa", "original-splits")
+```
+
+
+## Atributes
+
+- `id` - id of the example
+- `question` - the description of a mathematical problem in natural language, and includes the options to be selected from
 - `chain` - solution in the form of step-by-step calculations encoded in simple html-like language. computed from `annotated_formula` column
 - `result` - the correct option
 - `result_float` - the result converted to a float
+- `question_without_options` - same as `question`, but does not contain the options
+- `options` - dictionary of options to choose from, one is correct, keys are "A".."E" 
 - `annotated_formula` - human-annotated nested expression that (approximately) evaluates to the selected correct answer
 - `linear_formula` - same as `annotated_formula`, but linearized by original math_qa authors
 - `rationale` - human-annotated free-text reasoning that leads to the correct answer
-- `index` - index of the example in the original math_qa dataset
+- `category` - category of the math problem
+
+Attributes `id`, `question`, `chain`, and `result` are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
+
 
+## Sources
 
+- [mathqa HF dataset](https://huggingface.co/datasets/math_qa)
+- [official website](https://math-qa.github.io/)
 
 
 ## Licence
@@ -156,14 +178,14 @@ Apache 2.0, consistently with the original dataset.
 
 ## Cite
 
-If you use this version of dataset in research, please cite the [original MathQA paper](https://arxiv.org/abs/1905.13319), and [Calc-X collection](https://arxiv.org/abs/2305.15017) as follows:
+If you use this version of dataset in research, please cite the [original MathQA paper](https://arxiv.org/abs/1905.13319), and [Calc-X paper](https://arxiv.org/abs/2305.15017) as follows:
 
 ```bibtex
 @inproceedings{kadlcik-etal-2023-soft,
     title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems",
     author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek",
     booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track",
-    month = december,
+    month = dec,
     year = "2023",
     address = "Singapore, Singapore",
     publisher = "Association for Computational Linguistics",