Update README.md

README.md

@@ -1,31 +1,141 @@
----
-license: mit
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: question
-    dtype: string
-  - name: context
-    dtype: string
-  - name: choices
-    sequence: string
-  - name: label
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 63920351
-    num_examples: 2523
-  - name: validation
-    num_bytes: 52064930
-    num_examples: 2086
-  download_size: 5955070
-  dataset_size: 115985281
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
----
+---
+license: mit
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: question
+    dtype: string
+  - name: context
+    dtype: string
+  - name: choices
+    sequence: string
+  - name: label
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 63920351
+    num_examples: 2523
+  - name: validation
+    num_bytes: 52064930
+    num_examples: 2086
+  download_size: 5955070
+  dataset_size: 115985281
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+---
+
+
+This dataset is derived from `tau/scrolls` [dataset](tau/scrolls) by running the following script:
+
+```python
+import re
+
+from datasets import load_dataset
+
+
+def _normalize_answer(text):
+    return " ".join(text.split()).strip()
+
+
+def _drop_duplicates_in_input(untokenized_dataset):
+    # from scrolls/evaluator/dataset_evaluator.py
+
+    indices_to_keep = []
+    id_to_idx = {}
+    outputs = []
+    for i, (id_, output) in enumerate(
+        zip(untokenized_dataset["id"], untokenized_dataset["output"])
+    ):
+        if id_ in id_to_idx:
+            outputs[id_to_idx[id_]].append(output)
+            continue
+        indices_to_keep.append(i)
+        id_to_idx[id_] = len(outputs)
+        outputs.append([output])
+    untokenized_dataset = untokenized_dataset.select(indices_to_keep).flatten_indices()
+    untokenized_dataset = untokenized_dataset.remove_columns("output")
+    untokenized_dataset = untokenized_dataset.add_column("outputs", outputs)
+    return untokenized_dataset
+
+
+def _process_doc_prepended_question(doc):
+    input = doc["input"]
+    split = input.find("\n\n")
+    return {
+        "id": doc["id"],
+        "pid": doc["pid"],
+        "input": input,
+        "outputs": doc["outputs"],
+        "question": input[0:split],
+        "text": input[split + 2 :],
+    }
+
+
+def process_doc(doc):
+    quality_multiple_choice_pattern = re.compile(r" *\([A-D]\) *")
+    doc = _process_doc_prepended_question(doc)
+
+    split = doc["text"].find("\n\n", doc["text"].find("(D)"))
+    choices_text = doc["text"][:split]
+
+    doc["text"] = doc["text"][split:].strip()
+    doc["choices"] = [
+        _normalize_answer(choice)
+        for choice in re.split(quality_multiple_choice_pattern, choices_text)[1:]
+    ]
+    doc["gold"] = doc["choices"].index(_normalize_answer(doc["outputs"][0]))
+    return doc
+
+
+def get_quality_dataset():
+    """
+    download and processes the quality dataset following the lm-evaluation-harness scrolls_quality task
+
+    The processed dataset has the following train & validation splits with 2523 & 2086 examples respectively.
+    fields to be used during evaluation:
+    - question: the question prompt
+    - text: the context
+    - choices: list of choices (4 in total)
+    - gold: index of the correct choice
+    """
+    quality_dataset = load_dataset("tau/scrolls", "quality")
+    del quality_dataset["test"]  # drop test split -> no ground truths
+    for split in quality_dataset:
+        quality_dataset[split] = _drop_duplicates_in_input(quality_dataset[split])
+    quality_dataset = quality_dataset.map(process_doc)
+    return quality_dataset
+
+
+quality_dataset = get_quality_dataset()
+quality_dataset = quality_dataset.rename_columns({"text": "context", "gold": "label"})
+quality_dataset = quality_dataset.remove_columns(["pid", "input", "outputs"])
+train_ds = quality_dataset["train"]
+validation_ds = quality_dataset["validation"]
+```
+
+The processing code is adapted from [lm-evaluation-harness scrolls task](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/scrolls/task.py)
+
+---
+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.
+```