Update README.md

README.md

@@ -1,37 +1,108 @@
----
-license: mit
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: question
-    dtype: string
-  - name: context
-    dtype: string
-  - name: A
-    dtype: string
-  - name: B
-    dtype: string
-  - name: C
-    dtype: string
-  - name: D
-    dtype: string
-  - name: label
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 63759933.69322235
-    num_examples: 2517
-  - name: test
-    num_bytes: 52057383.0
-    num_examples: 2086
-  download_size: 19849080
-  dataset_size: 115817316.69322234
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
----
+---
+license: mit
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    dtype: string
+  - name: context
+    dtype: string
+  - name: A
+    dtype: string
+  - name: B
+    dtype: string
+  - name: C
+    dtype: string
+  - name: D
+    dtype: string
+  - name: label
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 63759933.69322235
+    num_examples: 2517
+  - name: test
+    num_bytes: 52057383.0
+    num_examples: 2086
+  download_size: 19849080
+  dataset_size: 115817316.69322234
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+---
+
+This dataset is derived from `tau/scrolls` [dataset](tau/scrolls) by running the following script:
+
+```python
+import re
+
+from datasets import load_dataset
+
+quality_dataset = load_dataset("tau/scrolls", "quality")
+
+
+def parse_example(example):
+    text = example["input"]
+    options = dict(re.findall(r"\((A|B|C|D)\) ([^\n]+)", text))
+
+    question_part, context = re.split(r"\(D\) [^\n]+\n", text, maxsplit=1)
+    question = re.sub(r"\([A-D]\) [^\n]+\n?", "", question_part).strip()
+
+    result = {"question": question, "context": context.strip(), **options}
+
+    if not all(key in result for key in ["A", "B", "C", "D"]):
+        raise ValueError("One or more options (A, B, C, D) are missing!")
+
+    # get label
+    label = -1
+    answer = example["output"]
+    if answer is None:
+        answer = ""
+
+    for idx, option in enumerate([options["A"], options["B"], options["C"], options["D"]]):
+        if answer.strip() == option.strip():
+            label = idx
+
+    result["label"] = label
+    return result
+
+
+quality_dataset = quality_dataset.map(parse_example)
+quality_dataset = quality_dataset.filter(lambda x: x["label"] >= 0)
+
+train_ds = quality_dataset["train"].remove_columns(["pid", "input", "output"])
+test_ds = quality_dataset["validation"].remove_columns(["pid", "input", "output"])
+```
+
+Specifically, only `quality` subset is kept and processed into MCQ format. The `test` split from original dataset is removed since it doesn't have ground truth labels. 
+Instead, validation split is assigned as test.
+
+Number of examples in train: ~2.5k
+Number of examples in test: ~2.1k
+
+This dataset can be used to test performance of a model focusing on long contexts. 
+Input Tokens as per [llama2](bclavie/bert24_32k_tok_llama2) tokenizer: Mean -> 7.4k, SD: 2.3k, Max -> 11.6k
+
+---
+Relevant sections from the [SCROLLS: Standardized CompaRison Over Long Language Sequences paper](https://arxiv.org/pdf/2201.03533)
+```
+QuALITY (Pang et al., 2021): A multiplechoice question answering dataset over stories
+and articles sourced from Project Gutenberg,10 the
+Open American National Corpus (Fillmore et al.,
+1998; Ide and Suderman, 2004), and more. Experienced writers wrote questions and distractors, and
+were incentivized to write answerable, unambiguous questions such that in order to correctly answer
+them, human annotators must read large portions
+of the given document. To measure the difficulty
+of their questions, Pang et al. conducted a speed
+validation process, where another set of annotators
+were asked to answer questions given only a short
+period of time to skim through the document. As
+a result, 50% of the questions in QuALITY are
+labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong
+answer.
+```