Merge pull request #112 from jeffhsu3/pubmedqa

pubmedqa and sciq

lm_eval/tasks/__init__.py

@@ -17,6 +17,8 @@ from . import lambada
 from . import race 
 from . import piqa
 from . import triviaqa
+from . import pubmedqa
+from . import sciq
 from . import webqs
 
 
@@ -46,6 +48,9 @@ TASK_REGISTRY = {
     "lambada": lambada.LAMBADA,
     "piqa": piqa.PiQA,
 
+    "pubmedqa" : pubmedqa.Pubmed_QA,
+    "sciq" : sciq.SciQ,
+
     #"triviaqa": triviaqa.TriviaQA,
     "arc_easy": arc.ARCEasy,
     "arc_challenge": arc.ARCChallenge,

lm_eval/tasks/pubmedqa.py

+import numpy as np
+import json
+import random
+from .common import HFTask 
+from lm_eval.base import rf, mean
+
+
+class Pubmed_QA(HFTask):
+    DATASET_PATH = "pubmed_qa"
+    DATASET_NAME = "pqa_labeled"
+
+    def has_training_docs(self):
+        return False
+
+    def has_validation_docs(self):
+        return False
+
+    def has_test_docs(self):
+        return True
+
+    def test_docs(self):
+        if self.has_test_docs():
+            # HF is labelled as train but its really just for testing
+            return self.data["train"]
+
+    def fewshot_description(self):
+        # Average ctx length in labelled dataset is 238.9
+        # 2 few-shot exmamples pushes it beyond context window
+        return ""
+
+    def doc_to_text(self, doc):
+        ctxs = "\n".join(doc["context"]["contexts"])
+        return "abstract: {}\nquestion: {}\nanswer:".format(
+            ctxs,
+            doc["question"],
+            doc["final_decision"]
+        )
+
+    def doc_to_target(self, doc):
+        return " {}".format(doc["final_decision"])
+
+    def fewshot_examples(self, k):
+        # Since only test docs sample from test docs
+        if self._training_docs is None:
+            self._training_docs = list(self.test_docs())
+        return random.sample(self._training_docs, k)
+
+    def construct_requests(self, doc, ctx):
+        """ Uses RequestFactory to construct Requests and returns
+        an iterable of Requests which will be sent to the LM.
+        """
+        ll_yes, _ = rf.loglikelihood(ctx, " yes")
+        ll_no, _ = rf.loglikelihood(ctx, " no")
+        ll_maybe, _ = rf.loglikelihood(ctx, " maybe")
+        return ll_yes, ll_no, ll_maybe
+
+    def process_results(self, doc, results):
+        gold = doc["final_decision"]
+        ll_yes, ll_no, ll_maybe = results
+        pred = np.argmax(results)
+        return {
+            "acc": ["yes", "no", "maybe"][pred] == gold, 
+        }
+
+    def aggregation(self):
+        return {
+            "acc" : mean
+        }
+
+    def higher_is_better(self):
+        return {
+            "acc" : True
+        }

lm_eval/tasks/sciq.py

+import os
+import json
+from ..utils import sh
+from lm_eval.base import MultipleChoiceTask, rf, mean
+import zipfile
+
+
+class SciQ(MultipleChoiceTask):
+    # Multiple languages and multiple years
+    def download(self):
+        if not os.path.exists('data/sciq'):
+            os.mkdir('data/sciq')
+            sh((
+                "wget https://ai2-public-datasets.s3.amazonaws.com/sciq/SciQ.zip -O data/sciq/SciQ.zip"
+            ))
+            with zipfile.ZipFile("data/sciq/SciQ.zip", "r") as zf:
+                zf.extractall("data/sciq/")
+
+    def has_training_docs(self):
+        return True
+
+    def has_validation_docs(self):
+        return True
+
+    def has_test_docs(self):
+        return True
+
+    def _convert_standard(self, doc):
+        choices = [
+            doc["distractor1"], 
+            doc["distractor2"], 
+            doc["distractor3"],
+            doc["correct_answer"],
+        ]
+        src = doc['support']
+        out_doc = {
+            "source" : src,
+            "query" : doc['question'],
+            "choices" : choices,
+            "gold" : 3,
+        }
+        return out_doc
+    
+    def load_docs(self, textfilename):
+        with open(textfilename, 'r') as j:
+            docs = json.loads(j.read()) 
+        for record in docs:
+            yield self._convert_standard(record)
+
+    def fewshot_description(self):
+        # Average ctx length in labelled dataset is 238.9
+        # 2 few-shot exmamples pushes it beyond context window
+        return ""
+
+    def training_docs(self):
+        return self.load_docs("data/sciq/SciQ dataset-2 3/train.json")
+
+    def validation_docs(self):
+        return self.load_docs("data/sciq/SciQ dataset-2 3/valid.json")
+
+    def test_docs(self):
+        return self.load_docs("data/sciq/SciQ dataset-2 3/test.json")
+
+    def doc_to_text(self, doc):
+        return "{}\n{}".format(doc["source"], doc["query"])
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