Renamed parquet files and updated readme.

README.md

@@ -1,4 +1,12 @@
 ---
+pretty_name: SciEntsBank
+license: cc-by-4.0
+language:
+- en
+task_categories:
+- text-classification
+size_categories:
+- 10K<n<100K
 dataset_info:
   features:
   - name: id
@@ -20,28 +28,179 @@ dataset_info:
           '4': non_domain
   splits:
   - name: train
-    num_bytes: 2179970
+    num_bytes: 232655
     num_examples: 4969
   - name: test_ua
-    num_bytes: 236393
+    num_bytes: 52730
     num_examples: 540
   - name: test_uq
-    num_bytes: 304225
+    num_bytes: 35716
     num_examples: 733
   - name: test_ud
-    num_bytes: 1745607
+    num_bytes: 177307
     num_examples: 4562
-  download_size: 498408
-  dataset_size: 4466195
+  dataset_size: 498408
 configs:
 - config_name: default
   data_files:
   - split: train
     path: data/train-*
   - split: test_ua
-    path: data/test_ua-*
+    path: data/test-ua-*
   - split: test_uq
-    path: data/test_uq-*
+    path: data/test-uq-*
   - split: test_ud
-    path: data/test_ud-*
+    path: data/test-ud-*
 ---
+
+# Dataset Card for "SciEntsBank"
+
+SciEntsBank is one of the two distinct subsets within the Student Response Analysis (SRA) corpus, the other subset being the
+[Beetle](https://huggingface.co/datasets/nkazi/Beetle) dataset. Derived from student answers gathered by Nielsen et al. [1],
+this dataset comprises nearly 11K responses to 197 assessment questions spanning 15 diverse science domains. The dataset
+features three labeling schemes: (a) 5-way, (b) 3-way, and (c) 2-way. The dataset includes a training set and three distinct
+test sets: (a) Unseen Answers (`test_ua`), (b) Unseen Questions (`test_uq`), and (c) Unseen Domains (`test_ud`).
+
+- **Authors:** Myroslava Dzikovska, Rodney Nielsen, Chris Brew, Claudia Leacock, Danilo Giampiccolo, Luisa Bentivogli, Peter Clark, Ido Dagan, Hoa Trang Dang
+- **Paper:** [SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge](https://aclanthology.org/S13-2045)
+
+## Loading Dataset
+
+```python
+from datasets import load_dataset
+dataset = load_dataset('nkazi/SciEntsBank')
+```
+
+## Labeling Schemes
+
+The authors released the dataset with annotations using five labels (i.e., 5-way labeling scheme) for Automated Short-Answer Grading (ASAG).
+Additionally, the authors have introduced two alternative labeling schemes, namely the 3-way and 2-way schemes, both derived from the 5-way
+labeling scheme designed for Recognizing Textual Entailment (RTE). In the 3-way labeling scheme, the categories "partially correct but
+incomplete", "irrelevant", and "non-domain" are consolidated into a unified category labeled as "incorrect". On the other hand, the 2-way
+labeling scheme simplifies the classification into a binary system where all labels except "correct" are merged under the "incorrect" category.
+
+The `label` column in this dataset presents the 5-way labels. For 3-way and 2-way labels, use the code provided below to derive it
+from the 5-way labels. After converting the labels, please verify the label distribution. A code to print the label distribution is
+also given below.
+
+### 5-way to 3-way
+
+```python
+from datasets import ClassLabel
+
+dataset = dataset.align_labels_with_mapping({'correct': 0, 'contradictory': 1, 'partially_correct_incomplete': 2, 'irrelevant': 2, 'non_domain': 2}, 'label')
+dataset = dataset.cast_column('label', ClassLabel(names=['correct', 'contradictory', 'incorrect']))
+```
+
+Using `align_labels_with_mapping()`, we are mapping "partially correct but incomplete", "irrelevant", and "non-domain" to the same id. Subsequently,
+we are using `cast_column()` to redefine the class labels (i.e., the label feature) where the id 2 corresponds to the "incorrect" label.
+
+### 5-way to 2-way
+
+```python
+from datasets import ClassLabel
+
+dataset = dataset.align_labels_with_mapping({'correct': 0, 'contradictory': 1, 'partially_correct_incomplete': 1, 'irrelevant': 1, 'non_domain': 1}, 'label')
+dataset = dataset.cast_column('label', ClassLabel(names=['correct', 'incorrect']))
+```
+
+In the above code, the label "correct" is mapped to 0 to maintain consistency with both the 5-way and 3-way labeling schemes. If the preference is to
+represent "correct" with id 1 and "incorrect" with id 0, either adjust the label map accordingly or run the following to switch the ids:
+
+```python
+dataset = dataset.align_labels_with_mapping({'incorrect': 0, 'correct': 1}, 'label')
+```
+
+### Saving and loading 3-way and 2-way datasets
+
+Use the following code to store the dataset with the 3-way (or 2-way) labeling scheme locally to eliminate the need to convert labels each time the dataset is loaded:
+
+```python
+dataset.save_to_disk('SciEntsBank_3way')
+```
+
+Here, `SciEntsBank_3way` depicts the path/directory where the dataset will be stored. Use the following code to load the dataset from the same local directory/path:
+
+```python
+from datasets import DatasetDict
+dataset = DatasetDict.load_from_disk('SciEntsBank_3way')
+```
+
+### Printing Label Distribution
+
+Use the following code to print the label distribution:
+
+```python
+def print_label_dist(dataset):
+    for split_name in dataset:
+        print(split_name, ':')
+        num_examples = 0
+        for label in dataset[split_name].features['label'].names:
+            count = dataset[split_name]['label'].count(dataset[split_name].features['label'].str2int(label))
+            print(' ', label, ':', count)
+            num_examples += count
+        print('  total :', num_examples)
+
+print_label_dist(dataset)
+```
+
+## Label Distribution
+
+<style>
+.label-dist th:not(:first-child), .label-dist td:not(:first-child) {
+  width: 15%;
+}
+</style>
+
+<div class="label-dist">
+
+### 5-way
+
+Label | Train | Test UA | Test UQ | Test UD
+--- | --: | --: | --: | --:
+Correct | 2,008 | 233 | 301 | 1,917
+Contradictory | 499 | 58 | 64 | 417
+Partially correct but incomplete | 1,324 | 113 | 175 | 986
+Irrelevant | 1,115 | 133 | 193 | 1,222
+Non-domain | 23 | 3 | - | 20
+Total | 4,969 | 540 | 733 | 4,562
+
+### 3-way
+
+Label | Train | Test UA | Test UQ | Test UD
+--- | --: | --: | --: | --:
+Correct | 2,008 | 233 | 301 | 1,917
+Contradictory | 499 | 58 | 64 | 417
+Incorrect | 2,462 | 249 | 368 | 2,228
+Total | 4,969 | 540 | 733 | 4,562
+
+### 2-way
+
+Label | Train | Test UA | Test UQ | Test UD
+--- | --: | --: | --: | --:
+Correct | 2,008 | 233 | 301 | 1,917
+Incorrect | 2,961 | 307 | 432 | 2,645
+Total | 4,969 | 540 | 733 | 4,562
+
+</div>
+
+## Citation
+
+```tex
+@inproceedings{dzikovska2013semeval,
+  title = {{S}em{E}val-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge},
+  author = {Dzikovska, Myroslava  and Nielsen, Rodney  and Brew, Chris  and Leacock, Claudia  and Giampiccolo, Danilo  and Bentivogli, Luisa  and Clark, Peter  and Dagan, Ido  and Dang, Hoa Trang},
+  year = 2013,
+  month = jun,
+  booktitle = {Second Joint Conference on Lexical and Computational Semantics ({SEM}), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation ({S}em{E}val 2013)},
+  editor = {Manandhar, Suresh  and Yuret, Deniz}
+  publisher = {Association for Computational Linguistics},
+  address = {Atlanta, Georgia, USA},
+  pages = {263--274},
+  url = {https://aclanthology.org/S13-2045},
+}
+```
+
+## References
+1. Rodney D. Nielsen, Wayne Ward, James H. Martin, and Martha Palmer. 2008. Annotating students' understanding of science
+concepts. In *Proceedings of the Sixth International Language Resources and Evaluation Conference*, Marrakech, Morocco.