Create coqa.py

coqa.py

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+"""
+CoQA: A Conversational Question Answering Challenge
+https://arxiv.org/pdf/1808.07042.pdf
+
+CoQA is a large-scale dataset for building Conversational Question Answering
+systems. The goal of the CoQA challenge is to measure the ability of machines to
+understand a text passage and answer a series of interconnected questions that
+appear in a conversation.
+
+Homepage: https://stanfordnlp.github.io/coqa/
+"""
+import inspect
+import transformers.data.metrics.squad_metrics as squad_metrics
+import lm_eval.datasets.coqa.coqa
+from lm_eval.base import Task, rf, mean
+from itertools import zip_longest
+
+
+_CITATION = """
+@misc{reddy2018coqa,
+    title={CoQA: A Conversational Question Answering Challenge},
+    author={Siva Reddy and Danqi Chen and Christopher D. Manning},
+    year={2018},
+    eprint={1808.07042},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+
+class CoQA(Task):
+    VERSION = 1
+    DATASET_PATH = inspect.getfile(lm_eval.datasets.coqa.coqa)
+    DATASET_NAME = None
+
+    def has_training_docs(self):
+        return True
+
+    def has_validation_docs(self):
+        return True
+
+    def has_test_docs(self):
+        return False
+
+    def training_docs(self):
+        return self.dataset["train"]
+
+    def validation_docs(self):
+        return self.dataset["validation"]
+
+    def test_docs(self):
+        pass
+
+    def doc_to_text(self, doc):
+        # Given a passage p, the conversation history {q1, a1, . . . qi−1, ai−1}
+        # and a question qi, the task is to predict the answer ai
+        doc_text = doc["story"] + "\n\n"
+        for (q, a) in zip_longest(
+            doc["questions"]["input_text"], doc["answers"]["input_text"][:-1]
+        ):  # omit target answer ai
+            question = f"Q: {q}\n\n"
+            answer = f"A: {a}\n\n" if a is not None else "A:"
+            doc_text += question + answer
+        return doc_text
+
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["story"] + " " + "\n".join(doc["questions"]["input_text"])
+
+    @classmethod
+    def get_answers(cls, doc, turn_id):
+        # Returns unique answers and valid alternatives (Some questions in CoQA have multiple valid answers).
+        answers = []
+        answer_forturn = doc["answers"]["input_text"][turn_id - 1]
+        answers.append(answer_forturn)
+
+        additional_answers = doc.get("additional_answers")
+        if additional_answers:
+            for key in additional_answers:
+                additional_answer_for_turn = additional_answers[key]["input_text"][
+                    turn_id - 1
+                ]
+                if additional_answer_for_turn.lower() not in map(str.lower, answers):
+                    answers.append(additional_answer_for_turn)
+        return answers
+
+    @classmethod
+    def get_answer_choice(self, raw_text):
+        # Function maps answers to CoQA answer categories
+        # ~ 1/5 of the CoQA answers are Yes/No
+        # ~ 2/3 of the CoQA answers are span-based
+        # (answers overlap with the passage ignoring punctuation and case mismatch)
+        if raw_text == "unknown":
+            return "0"
+        if squad_metrics.normalize_answer(raw_text) == "yes":
+            return "1"
+        if squad_metrics.normalize_answer(raw_text) == "no":
+            return "2"
+        return "3"  # Not a yes/no question
+
+    @staticmethod
+    def compute_scores(gold_list, pred):
+        # tests for exact match and on the normalised answer (compute_exact)
+        # test for overlap (compute_f1)
+        f1_sum = 0.0
+        em_sum = 0.0
+        if len(gold_list) > 1:
+            for i in range(len(gold_list)):
+                gold_answers = gold_list[0:i] + gold_list[i + 1 :]
+                # predictions compared against (n) golds and take maximum
+                em_sum += max(
+                    squad_metrics.compute_exact(a, pred) for a in gold_answers
+                )
+                f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_answers)
+        else:
+            em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_list)
+            f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_list)
+
+        return {
+            "em": em_sum / max(1, len(gold_list)),
+            "f1": f1_sum / max(1, len(gold_list)),
+        }
+
+    def doc_to_target(self, doc, turnid=None):
+        # Default to prediction of last turn.
+        if turnid is None:
+            turnid = len(doc["questions"]["input_text"])
+        raw_text = doc["answers"]["input_text"][turnid - 1]
+        return " " + raw_text
+
+    def construct_requests(self, doc, ctx):
+        """Uses RequestFactory to construct Requests and returns an iterable of
+        Requests which will be sent to the LM.
+
+        :param doc:
+            The document as returned from training_docs, validation_docs, or test_docs.
+        :param ctx: str
+            The context string, generated by fewshot_context. This includes the natural
+            language description, as well as the few shot examples, and the question
+            part of the document for `doc`.
+        """
+        cont_request = rf.greedy_until(ctx, {"until": ["\nQ:"]})
+        return cont_request
+
+    def process_results(self, doc, results):
+        """Take a single document and the LM results and evaluates, returning a
+        dict where keys are the names of submetrics and values are the values of
+        the metric for that one document
+
+        :param doc:
+            The document as returned from training_docs, validation_docs, or test_docs.
+        :param results:
+            The results of the requests created in construct_requests.
+        """
+        turn_id = len(doc["questions"]["input_text"])
+        gold_list = self.get_answers(doc, turn_id)
+        pred = results[0].strip().split("\n")[0]
+
+        scores = self.compute_scores(gold_list, pred)
+
+        return {
+            "f1": scores["f1"],
+            "em": scores["em"],
+        }
+
+    def higher_is_better(self):
+        return {
+            "f1": True,
+            "em": True,
+        }
+
+    def aggregation(self):
+        return {
+            "f1": mean,
+            "em": mean,
+        }