Merge branch 'master' into task_doc

lm_eval/tasks/headqa.py

@@ -8,7 +8,8 @@ even for highly specialized humans.
 
 Homepage: https://aghie.github.io/head-qa/
 """
-from . common import HFTask
+import inspect
+import lm_eval.datasets.headqa.headqa
 from lm_eval.base import MultipleChoiceTask
 
 
@@ -24,9 +25,9 @@ _CITATION = """
 """
 
 
-class HeadQABase(HFTask, MultipleChoiceTask):
+class HeadQABase(MultipleChoiceTask):
     VERSION = 0
-    DATASET_PATH = "head_qa"
+    DATASET_PATH = inspect.getfile(lm_eval.datasets.headqa.headqa)
 
     def has_training_docs(self):
         return True
@@ -37,7 +38,18 @@ class HeadQABase(HFTask, MultipleChoiceTask):
     def has_test_docs(self):
         return True
 
-    def _convert_standard(self, doc):
+    def training_docs(self):
+        if self._training_docs is None:
+            self._training_docs = list(map(self._process_doc, self.dataset["train"]))
+        return self._training_docs
+
+    def validation_docs(self):
+        return map(self._process_doc, self.dataset["validation"])
+
+    def test_docs(self):
+        return map(self._process_doc, self.dataset["test"])
+
+    def _process_doc(self, doc):
         out_doc = {
             "id": doc["qid"],
             "query": "Question: " + doc["qtext"] + "\nAnswer:",
@@ -49,12 +61,21 @@ class HeadQABase(HFTask, MultipleChoiceTask):
     def doc_to_text(self, doc):
         return doc["query"]
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["query"]
+
+
 class HeadQAEn(HeadQABase):
     DATASET_NAME = "en"
 
+
 class HeadQAEs(HeadQABase):
     DATASET_NAME = "es"
 
+
 # for backwards compatibility
 class HeadQAEsDeprecated(HeadQABase):
     DATASET_NAME = "es"

lm_eval/tasks/hellaswag.py

@@ -15,7 +15,6 @@ Homepage: https://rowanzellers.com/hellaswag/
 """
 import re
 from lm_eval.base import MultipleChoiceTask
-from . common import HFTask
 
 
 _CITATION = """
@@ -28,7 +27,7 @@ _CITATION = """
 """
 
 
-class HellaSwag(HFTask, MultipleChoiceTask):
+class HellaSwag(MultipleChoiceTask):
     VERSION = 0
     DATASET_PATH = "hellaswag"
     DATASET_NAME = None
@@ -42,16 +41,15 @@ class HellaSwag(HFTask, MultipleChoiceTask):
     def has_test_docs(self):
         return False
 
-    @classmethod
-    def preprocess(cls, text):
-        text = text.strip()
-        # NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag.
-        text = text.replace(" [title]", ". ")
-        text = re.sub('\\[.*?\\]', '', text)
-        text = text.replace("  ", " ")
-        return text
+    def training_docs(self):
+        if self._training_docs is None:
+            self._training_docs = list(map(self._process_doc, self.dataset["train"]))
+        return self._training_docs
+
+    def validation_docs(self):
+        return map(self._process_doc, self.dataset["validation"])
 
-    def _convert_standard(self, doc):
+    def _process_doc(self, doc):
         ctx = doc["ctx_a"] + " " + doc["ctx_b"].capitalize()
         out_doc = {
             "query": self.preprocess(doc['activity_label'] + ': ' + ctx),
@@ -60,5 +58,20 @@ class HellaSwag(HFTask, MultipleChoiceTask):
         }
         return out_doc
 
+    @classmethod
+    def preprocess(cls, text):
+        text = text.strip()
+        # NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag.
+        text = text.replace(" [title]", ". ")
+        text = re.sub('\\[.*?\\]', '', text)
+        text = text.replace("  ", " ")
+        return text
+
     def doc_to_text(self, doc):
         return doc["query"]
+
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["query"]

lm_eval/tasks/hendrycks_ethics.py

@@ -16,15 +16,12 @@ of the paper.
 Homepage: https://github.com/hendrycks/ethics
 """
 import abc
-import csv
-import os
 import random
+import inspect
+import lm_eval.datasets.hendrycks_ethics.hendrycks_ethics
 import numpy as np
 from lm_eval.base import Task, rf
-from lm_eval.metrics import mean
-from lm_eval.utils import sh
-from .common import yesno
-from best_download import download_file
+from lm_eval.metrics import mean, yesno
 
 
 _CITATION = """
@@ -38,15 +35,8 @@ _CITATION = """
 
 
 class Ethics(Task):
-    def download(self):
-        if not os.path.exists('data/ethics/done'):
-            sh("mkdir -p data")
-            download_file("https://people.eecs.berkeley.edu/~hendrycks/ethics.tar", local_file="data/ethics.tar", expected_checksum="40acbf1ac0da79a2aabef394d58889136b8d38b05be09482006de2453fb06333")
-            sh("""
-            tar -xf data/ethics.tar -C data/
-            rm data/ethics.tar
-            touch data/ethics/done
-            """)
+    DATASET_PATH = inspect.getfile(lm_eval.datasets.hendrycks_ethics.hendrycks_ethics)
+    DATASET_NAME = None
 
     def has_training_docs(self):
         return True
@@ -57,30 +47,16 @@ class Ethics(Task):
     def has_test_docs(self):
         return True
 
-    @abc.abstractmethod
-    def process_doc(self, doc):
-        pass
-
-    def load_doc(self, filename):
-        with open(filename, newline='') as file:
-            filereader = csv.reader(file)
-            return self.process_doc(list(filereader))
-
-    @abc.abstractmethod
-    def get_prefix(self):
-        """returns string corresponding to file prefix"""
-        pass
-
     # TODO: Figure out how to incorporate the Ethics `hard` test sets.
 
     def training_docs(self):
-        return self.load_doc(f"data/ethics/{self.get_prefix()}_train.csv")
+        return self.dataset["train"]
 
     def validation_docs(self):
         raise NotImplementedError
 
     def test_docs(self):
-        return self.load_doc(f"data/ethics/{self.get_prefix()}_test.csv")
+        return self.dataset["test"]
 
     @abc.abstractmethod
     def doc_to_text(self, doc):
@@ -109,18 +85,19 @@ class Ethics(Task):
 
 class EthicsCM(Ethics):
     VERSION = 0
-    # Ignoring "ambiguous" extra dataset for now
-    def get_prefix(self):
-        return "commonsense/cm"
-
-    def process_doc(self, doc):
-        return doc[1:]
+    DATASET_NAME = "commonsense"  # Ignoring "ambiguous" extra dataset for now
 
     def doc_to_text(self, doc):
-        return "{}\nQuestion: Is this wrong?\nAnswer:".format(doc[1])
+        return "{}\nQuestion: Is this wrong?\nAnswer:".format(doc["input"])
+
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["input"]
 
     def doc_to_target(self, doc):
-        return " {}".format(yesno(int(doc[0])))
+        return " {}".format(yesno(int(doc["label"])))
 
     def construct_requests(self, doc, ctx):
         ll_yes, _ = rf.loglikelihood(ctx, " yes")
@@ -130,7 +107,7 @@ class EthicsCM(Ethics):
     def process_results(self, doc, results):
         ll_yes, ll_no = results
         pred = ll_yes > ll_no
-        gold = bool(int(doc[0]))
+        gold = bool(int(doc["label"]))
         return {
             "acc": pred == gold
         }
@@ -148,19 +125,20 @@ class EthicsCM(Ethics):
 
 class EthicsDeontology(Ethics):
     VERSION = 0
-    def get_prefix(self):
-        return "deontology/deontology"
-
-    def process_doc(self, doc):
-        # Append identifiers before shuffling to calculate exact matches lateron & skip the first element of headers
-        return [x + [i] for i, x in enumerate(doc[1:])]
+    DATASET_NAME = "deontology"
 
     def doc_to_text(self, doc):
-        prompt = " ".join([doc[1], doc[2]])
+        prompt = " ".join([doc["scenario"], doc["excuse"]])
         return "Question: Would most people believe this reasonable or unreasonable to say? \"{}\"\nAnswer:".format(prompt)
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return " ".join([doc["scenario"], doc["excuse"]])
+
     def doc_to_target(self, doc):
-        target = ["unreasonable", "reasonable"][int(doc[0])]
+        target = ["unreasonable", "reasonable"][int(doc["label"])]
         return " {}".format(target)
 
     def construct_requests(self, doc, ctx):
@@ -170,14 +148,15 @@ class EthicsDeontology(Ethics):
 
     def process_results(self, doc, results):
         pred = np.argmax(results)
-        gold = bool(int(doc[0]))
+        gold = bool(int(doc["label"]))
         return {
             "acc": pred == gold,
-            "em": [doc[-1], pred == gold]
+            "em": [doc["group_id"], pred == gold]
         }
 
     def calc_em(self, items):
         # Calculate exact matches - i.e. all in a pair of 4 are correct
+        # NOTE: `items` is a tuple of (doc["group_id"], is_correct)
         preds_sort = sorted(items, key=lambda x: x[0])
         em_sums = [int(preds_sort[4*i][1]) + int(preds_sort[4*i+1][1]) + int(preds_sort[4*i+2][1]) + int(preds_sort[4*i+3][1]) for i in range(len(preds_sort) // 4)]
         em_cors = [em_sums[i] == 4 for i in range(len(em_sums))]
@@ -198,18 +177,19 @@ class EthicsDeontology(Ethics):
 
 class EthicsJustice(Ethics):
     VERSION = 0
-    def get_prefix(self):
-        return "justice/justice"
-
-    def process_doc(self, doc):
-        # Append identifiers before shuffling to calculate exact matches later on & skip the first element of headers
-        return [x + [i] for i, x in enumerate(doc[1:])]
+    DATASET_NAME = "justice"
 
     def doc_to_text(self, doc):
-        return "Question: Would most people believe this reasonable or unreasonable to say? \"{}\"\nAnswer:".format(doc[1])
+        return "Question: Would most people believe this reasonable or unreasonable to say? \"{}\"\nAnswer:".format(doc["scenario"])
+
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["scenario"]
 
     def doc_to_target(self, doc):
-        target = ["unreasonable", "reasonable"][int(doc[0])]
+        target = ["unreasonable", "reasonable"][int(doc["label"])]
         return " {}".format(target)
 
     def construct_requests(self, doc, ctx):
@@ -219,14 +199,15 @@ class EthicsJustice(Ethics):
 
     def process_results(self, doc, results):
         pred = np.argmax(results)
-        gold = bool(int(doc[0]))
+        gold = bool(int(doc["label"]))
         return {
             "acc": pred == gold,
-            "em": [doc[-1], pred == gold]
+            "em": [doc["group_id"], pred == gold]
         }
 
     def calc_em(self, items):
         # Calculate exact matches - i.e. all in a pair of 4 are correct
+        # NOTE: `items` is a tuple of (doc["group_id"], is_correct)
         preds_sort = sorted(items, key=lambda x: x[0])
         em_sums = [int(preds_sort[4*i][1]) + int(preds_sort[4*i+1][1]) + int(preds_sort[4*i+2][1]) + int(preds_sort[4*i+3][1]) for i in range(len(preds_sort) // 4)]
         em_cors = [em_sums[i] == 4 for i in range(len(em_sums))]
@@ -247,17 +228,12 @@ class EthicsJustice(Ethics):
 
 class EthicsUtilitarianismOriginal(Ethics):
     VERSION = 0
-    def get_prefix(self):
-        return "utilitarianism/util"
+    DATASET_NAME = "utilitarianism"
 
     def has_training_docs(self):
         # Rely on the fixed and labeled examples of `fewshot_examples` for the few-shot setting.
         return False
 
-    def process_doc(self, docs):
-        for doc in docs:
-            yield {"activity": doc[0], "baseline": doc[1], "rating": ""}
-
     def fewshot_examples(self, k, rnd):
         # Overwriting fewshot examples as k can be max 5
         assert k <= 5, "There are only 5 possible shots for this task. Refer to the V2 for more."
@@ -274,6 +250,12 @@ class EthicsUtilitarianismOriginal(Ethics):
     def doc_to_text(self, doc):
         return 'Activity: "{}"\nRating:'.format(doc["activity"])
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["activity"]
+
     def doc_to_target(self, doc):
         return " " + doc["rating"]
 
@@ -311,24 +293,33 @@ class EthicsUtilitarianismOriginal(Ethics):
 
 
 class EthicsUtilitarianism(Ethics):
-    VERSION = 0
     """
     This is a variation of the original Utilitarianism task used in the paper, where the situations are directly compared.
     This allows scaling to >5 shots.
     """
+    VERSION = 0
+    DATASET_NAME = "utilitarianism"
 
-    def get_prefix(self):
-        return "utilitarianism/util"
+    def training_docs(self):
+        for doc in self.dataset["train"]:
+            yield self._process_doc(doc)
 
-    def process_doc(self, docs):
-        rnd = random.Random()
-        for doc in docs:
-            rnd.seed(doc[0])
+    def validation_docs(self):
+        raise NotImplementedError
+
+    def test_docs(self):
+        for doc in self.dataset["test"]:
+            yield self._process_doc(doc)
+
+    def _process_doc(self, doc):
+        rnd = random.Random(doc["activity"])
+        scenarios = [doc["activity"], doc["baseline"]]
         ordering = [0, 1]
         rnd.shuffle(ordering)
-            yield {
-                "scenarios": [doc[ordering[0]], doc[ordering[1]]],
-                "label": int(ordering.index(0) == 0),  # The correct scenario is always first
+        return {
+            "scenarios": [scenarios[ordering[0]], scenarios[ordering[1]]],
+            # The correct scenario is always first
+            "label": int(ordering.index(0) == 0),
         }
 
     def doc_to_text(self, doc):
@@ -365,23 +356,19 @@ class EthicsUtilitarianism(Ethics):
 
 class EthicsVirtue(Ethics):
     VERSION = 0
-    def get_prefix(self):
-        return "virtue/virtue"
+    DATASET_NAME = "virtue"
 
-    def process_doc(self, doc):
-        # Append identifiers before shuffling to calculate exact matches lateron & skip the first element of headers
-        return [x + [i] for i, x in enumerate(doc[1:])]
-
-    def load_doc(self, filename):
-        with open(filename, newline='') as file:
-            filereader = csv.reader(file)
-            return self.process_doc(list(filereader))
+    def _process_doc(self, doc):
+        return doc
 
     def doc_to_text(self, doc):
-        return "Sentence: {}\nQuestion: Does the character in this sentence exhibit the trait \"{}\"?\nAnswer:".format(*doc[1].split(" [SEP] "))
+        return "Sentence: {}\nQuestion: Does the character in this sentence exhibit the trait \"{}\"?\nAnswer:".format(
+            doc["scenario"],
+            doc["trait"]
+        )
 
     def doc_to_target(self, doc):
-        return " {}".format(yesno(int(doc[0])))
+        return " {}".format(yesno(int(doc["label"])))
 
     def construct_requests(self, doc, ctx):
         ll_yes, _ = rf.loglikelihood(ctx, " yes")
@@ -391,14 +378,15 @@ class EthicsVirtue(Ethics):
     def process_results(self, doc, results):
         ll_yes, ll_no = results
         pred = ll_yes > ll_no
-        gold = bool(int(doc[0]))
+        gold = bool(int(doc["label"]))
         return {
             "acc": pred == gold,
-            "em": [doc[-1], pred == gold]
+            "em": [doc["group_id"], pred == gold]
         }
 
     def calc_em(self, items):
         # Calculate exact matches - i.e. all in a pair of 5 are correct
+        # NOTE: `items` is a tuple of (doc["group_id"], is_correct)
         preds_sort = sorted(items, key=lambda x: x[0])
         em_sums = [int(preds_sort[5*i][1]) + int(preds_sort[5*i+1][1]) + int(preds_sort[5*i+2][1]) + int(preds_sort[5*i+3][1]) + int(preds_sort[5*i+4][1]) for i in range(len(preds_sort) // 5)]
         em_cors = [em_sums[i] == 5 for i in range(len(em_sums))]

lm_eval/tasks/hendrycks_math.py

@@ -8,13 +8,10 @@ models to generate answer derivations and explanations.
 
 Homepage: https://github.com/hendrycks/math
 """
-import abc
-import json
-from lm_eval.utils import sh
+import inspect
+import lm_eval.datasets.hendrycks_math.hendrycks_math
 from lm_eval.metrics import mean
 from lm_eval.base import Task, rf
-from pathlib import Path
-from best_download import download_file
 
 
 _CITATION = """
@@ -28,21 +25,8 @@ _CITATION = """
 
 
 class Math(Task):
-    DATASET_PATH = Path('data/MATH')
-
-    def download(self):
-        if not (self.DATASET_PATH / 'test').exists() or not (self.DATASET_PATH / 'done').exists():
-            sh(f"mkdir -p {self.DATASET_PATH}")
-            download_file("https://people.eecs.berkeley.edu/~hendrycks/MATH.tar", local_file=f"{self.DATASET_PATH}.tar", expected_checksum="0fbe4fad0df66942db6c221cdcc95b298cc7f4595a2f0f518360cce84e90d9ac")
-            sh(f"""
-            tar -xf {self.DATASET_PATH}.tar -C data/ && touch {self.DATASET_PATH / 'done'}
-            rm {self.DATASET_PATH}.tar
-            """)
-
-    @abc.abstractmethod
-    def get_file_info(self):
-        """returns directory name"""
-        pass
+    DATASET_PATH = inspect.getfile(lm_eval.datasets.hendrycks_math.hendrycks_math)
+    DATASET_NAME = None
 
     def has_training_docs(self):
         return True
@@ -53,28 +37,31 @@ class Math(Task):
     def has_test_docs(self):
         return True
 
-    def _load_docs(self, path):
-        for file in sorted(path.iterdir()):
-            with open(file) as f:
-                doc = json.load(f)
-                doc["answer"] = self.remove_boxed(
-                    self.last_boxed_only_string(doc["solution"]))
-                yield doc
-
     def training_docs(self):
-        return self._load_docs(self.DATASET_PATH / "train" / self.get_file_info())
+        return map(self._process_doc, self.dataset["train"])
 
     def validation_docs(self):
         return NotImplemented
 
     def test_docs(self):
-        return self._load_docs(self.DATASET_PATH / "test" / self.get_file_info())
+        return map(self._process_doc, self.dataset["test"])
+
+    def _process_doc(self, doc):
+        doc["answer"] = self.remove_boxed(
+            self.last_boxed_only_string(doc["solution"]))
+        return doc
 
     def doc_to_text(self, doc):
         return "Problem: " + doc["problem"] + "\nAnswer:"
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["problem"]
+
     def doc_to_target(self, doc):
-        return " " + doc["answer"]
+        return " " + doc["solution"]
 
     def construct_requests(self, doc, ctx):
         return rf.greedy_until(ctx, ["\n"])
@@ -301,41 +288,34 @@ class Math(Task):
 
 class MathAlgebra(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'algebra'
+    DATASET_NAME = 'algebra'
 
 
 class MathCountingAndProbability(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'counting_and_probability'
+    DATASET_NAME = 'counting_and_probability'
 
 
 class MathGeometry(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'geometry'
+    DATASET_NAME = 'geometry'
 
 
 class MathIntermediateAlgebra(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'intermediate_algebra'
+    DATASET_NAME = 'intermediate_algebra'
 
 
 class MathNumberTheory(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'number_theory'
+    DATASET_NAME = 'number_theory'
 
 
 class MathPrealgebra(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'prealgebra'
+    DATASET_NAME = 'prealgebra'
 
 
 class MathPrecalculus(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'precalculus'
+    DATASET_NAME = 'precalculus'

lm_eval/tasks/hendrycks_test.py

@@ -12,12 +12,7 @@ important shortcomings.
 
 Homepage: https://github.com/hendrycks/test
 """
-import csv
-import random
 from lm_eval.base import MultipleChoiceTask
-from ..utils import sh
-from pathlib import Path
-from best_download import download_file
 
 
 _CITATION = """
@@ -61,25 +56,15 @@ def create_task(subject):
 
 class GeneralHendrycksTest(MultipleChoiceTask):
     VERSION = 0
-    DATASET_PATH = Path("data/hendrycksTest/")
+    DATASET_PATH = "hendrycks_test"
+    DATASET_NAME = None
 
     def __init__(self, subject):
-        self.subject = subject
+        self.DATASET_NAME = subject
         super().__init__()
 
-    def download(self):
-        if not (self.DATASET_PATH / 'done').exists():
-            sh("mkdir -p data")
-            download_file("https://people.eecs.berkeley.edu/~hendrycks/data.tar", local_file="data/data.tar", expected_checksum="78a804365a59028188fb19bd1adcadc5e0c260b220a9d8b2e33a5ea7d5fbe3b4")
-            sh("""
-            tar -xf data/data.tar -C data/
-            rm data/data.tar
-            mv data/data data/hendrycksTest
-            touch data/hendrycksTest/done
-            """)
-
     def has_training_docs(self):
-        return True
+        return False
 
     def has_validation_docs(self):
         return True
@@ -87,8 +72,14 @@ class GeneralHendrycksTest(MultipleChoiceTask):
     def has_test_docs(self):
         return True
 
-    def _convert_standard(self, doc):
-        def format_example(doc, choices):
+    def validation_docs(self):
+        return map(self._process_doc, self.dataset["validation"])
+
+    def test_docs(self):
+        return map(self._process_doc, self.dataset["test"])
+
+    def _process_doc(self, doc):
+        def format_example(doc, keys):
             """
                 Question: <prompt>
                 Choices:
@@ -98,46 +89,31 @@ class GeneralHendrycksTest(MultipleChoiceTask):
                 D. <choice4>
                 Answer:
             """
-            prompt = "Question: " + doc[0] + "\nChoices:\n"
-            prompt += "".join([f"{choices[j]}. {doc[j+1]}\n" for j in range(4)])
+            prompt = "Question: " + doc["question"] + "\nChoices:\n"
+            prompt += "".join([f"{key}. {choice}\n" for key, choice in zip(keys, doc["choices"])])
             prompt += "Answer:"
             return prompt
-        choices = ['A', 'B', 'C', 'D']
+        keys = ['A', 'B', 'C', 'D']
         return {
-            "query": format_example(doc, choices),
-            "choices": doc[1:5],
-            "gold": choices.index(doc[5])
+            "query": format_example(doc, keys),
+            "choices": doc["choices"],
+            "gold": keys.index(doc["answer"]) if isinstance(doc["answer"], str) else doc["answer"]
         }
 
-    def _load_docs(self, filename):
-        reader = csv.reader(open(filename, 'r'), quotechar='"', delimiter=',')
-        return (self._convert_standard(doc) for doc in reader)
-
-    def training_docs(self):
-        docs = []
-        for train_dir in ["auxiliary_train", "dev"]:
-            for f in (self.DATASET_PATH / train_dir).iterdir():
-                docs.extend(self._load_docs(f))
-        return docs
-
-    def validation_docs(self):
-        filename = self.DATASET_PATH / "val" / f"{self.subject}_val.csv"
-        return self._load_docs(filename)
-
-    def test_docs(self):
-        filename = self.DATASET_PATH / "test" / f"{self.subject}_test.csv"
-        return self._load_docs(filename)
-
     def fewshot_examples(self, k, rnd):
         # fewshot_examples is not just sampling from train_docs because dev is 
         # in the same distribution as val/test but auxiliary_train isn't
 
-        filename = self.DATASET_PATH / "dev" / f"{self.subject}_dev.csv"
-
         if self._fewshot_docs is None:
-            self._fewshot_docs = list(self._load_docs(filename))
+            self._fewshot_docs = list(map(self._process_doc, self.dataset["dev"]))
 
         return rnd.sample(list(self._fewshot_docs), k)
 
     def doc_to_text(self, doc):
         return doc["query"]
+
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["query"]

lm_eval/tasks/lambada.py

@@ -12,12 +12,10 @@ in the broader discourse.
 
 Homepage: https://zenodo.org/record/2630551#.X4Xzn5NKjUI
 """
-import json
+import inspect
+import lm_eval.datasets.lambada.lambada
 from lm_eval.base import Task, rf
 from lm_eval.metrics import mean, perplexity
-from lm_eval.utils import sh
-from best_download import download_file
-import os
 
 
 _CITATION = """
@@ -34,19 +32,7 @@ _CITATION = """
 
 class LAMBADA(Task):
     VERSION = 0
-    def download(self):
-        sh("mkdir -p data/lambada")
-        try:
-            if not os.path.exists("data/lambada/lambada_test.jsonl"):
-                download_file(
-                    "http://eaidata.bmk.sh/data/lambada_test.jsonl", 
-                    local_file="data/lambada/lambada_test.jsonl",
-                    expected_checksum="4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226"
-                )
-        except:
-            # fallback - for some reason best_download doesnt work all the time here
-            sh("wget http://eaidata.bmk.sh/data/lambada_test.jsonl -O data/lambada/lambada_test.jsonl")
-            sh('echo "4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226  data/lambada/lambada_test.jsonl" | sha256sum --check')
+    DATASET_PATH = inspect.getfile(lm_eval.datasets.lambada.lambada)
 
     def has_training_docs(self):
         return False
@@ -61,9 +47,7 @@ class LAMBADA(Task):
         pass
 
     def validation_docs(self):
-        with open("data/lambada/lambada_test.jsonl") as fh:
-            for line in fh:
-                yield json.loads(line)
+        return self.dataset["validation"]
 
     def test_docs(self):
         pass
@@ -71,6 +55,12 @@ class LAMBADA(Task):
     def doc_to_text(self, doc):
         return doc['text'].rsplit(' ', 1)[0]
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc['text']
+
     def doc_to_target(self, doc):
         return " " + doc['text'].rsplit(' ', 1)[1]
 

lm_eval/tasks/lambada_cloze.py

@@ -13,12 +13,7 @@ in the broader discourse.
 
 Homepage: https://zenodo.org/record/2630551#.X4Xzn5NKjUI
 """
-import json
-from lm_eval.base import Task, rf
-from lm_eval.metrics import mean, perplexity
-from lm_eval.utils import sh
 from lm_eval.tasks.lambada import LAMBADA
-from best_download import download_file
 
 
 _CITATION = """
@@ -35,8 +30,15 @@ _CITATION = """
 
 class LAMBADA_cloze(LAMBADA):
     VERSION = 0
+
     def doc_to_text(self, doc):
         return doc['text'].rsplit(' ', 1)[0] + " ____. ->"
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc['text']
+
     def doc_to_target(self, doc):
         return " " + doc['text'].rsplit(' ', 1)[1]

lm_eval/tasks/lambada_multilingual.py

@@ -14,13 +14,6 @@ in the broader discourse.
 Homepage: https://zenodo.org/record/2630551#.X4Xzn5NKjUI
 """
 from . import lambada
-from lm_eval.base import Task, rf
-from lm_eval.metrics import mean, perplexity
-from lm_eval.utils import sh
-from best_download import download_file
-import json
-from functools import partial
-import os 
 
 
 _CITATION = """
@@ -35,68 +28,37 @@ _CITATION = """
 """
 
 
-LANGS = ["en", "fr", "de", "it", "es"]
-CHECKSUMS = {"en": "4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226", 
-             "fr": "941ec6a73dba7dc91c860bf493eb66a527cd430148827a4753a4535a046bf362", 
-             "de": "51c6c1795894c46e88e4c104b5667f488efe79081fb34d746b82b8caa663865e", 
-             "it": "86654237716702ab74f42855ae5a78455c1b0e50054a4593fb9c6fcf7fad0850", 
-             "es": "ffd760026c647fb43c67ce1bc56fd527937304b348712dce33190ea6caba6f9c"
-            }
-
 class MultilingualLAMBADA(lambada.LAMBADA):
     VERSION = 0
 
-    def __init__(self, lang=None):
-      self.LANG = lang
-      super().__init__()
-    
-    def download(self):
-      sh("mkdir -p data/lambada")
-      f = f"data/lambada/lambada_test_{self.LANG}.jsonl"
-      url = f"http://eaidata.bmk.sh/data/lambada_test_{self.LANG}.jsonl"
-      try:
-        if not os.path.exists(f):
-          download_file(
-              url, 
-              local_file=f,
-              expected_checksum=CHECKSUMS[self.LANG]
-          )
-      except:
-        # fallback - for some reason best_download doesnt work all the time here
-        sh(f"wget {url} -O {f}")
-        sh(f'echo "{CHECKSUMS[self.LANG]}  {f}" | sha256sum --check')
-
-
-    def validation_docs(self):
-      with open(f"data/lambada/lambada_test_{self.LANG}.jsonl") as fh:
-        for line in fh:
-          yield json.loads(line)
 
 class MultilingualLAMBADAEN(MultilingualLAMBADA):
-  def __init__(self):
-    super().__init__('en')
+    DATASET_NAME = 'en'
+
 
 class MultilingualLAMBADAFR(MultilingualLAMBADA):
-  def __init__(self):
-    super().__init__('fr')
+    DATASET_NAME = 'fr'
+
 
 class MultilingualLAMBADADE(MultilingualLAMBADA):
-  def __init__(self):
-    super().__init__('de')
+    DATASET_NAME = 'de'
+
 
 class MultilingualLAMBADAIT(MultilingualLAMBADA):
-  def __init__(self):
-    super().__init__('it')
+    DATASET_NAME = 'it'
+
 
 class MultilingualLAMBADAES(MultilingualLAMBADA):
-  def __init__(self):
-    super().__init__('es')
+    DATASET_NAME = 'es'
+
+
+LANG_CLASSES = [MultilingualLAMBADAEN, MultilingualLAMBADAFR,
+                MultilingualLAMBADADE, MultilingualLAMBADAIT,
+                MultilingualLAMBADAES]
 
-LANG_CLASSES = [MultilingualLAMBADAEN, MultilingualLAMBADAFR, MultilingualLAMBADADE, MultilingualLAMBADAIT, MultilingualLAMBADAES]
 
 def construct_tasks():
     tasks = {}
-    for lang, lang_class in zip(LANGS, LANG_CLASSES):
-        tasks[f"lambada_mt_{lang}"] = lang_class
+    for lang_class in LANG_CLASSES:
+        tasks[f"lambada_mt_{lang_class.DATASET_NAME}"] = lang_class
     return tasks
-

lm_eval/tasks/logiqa.py

@@ -10,9 +10,9 @@ NLP setting.
 
 Homepage: https://github.com/lgw863/LogiQA-dataset
 """
+import inspect
+import lm_eval.datasets.logiqa.logiqa
 from lm_eval.base import MultipleChoiceTask
-from best_download import download_file
-from pathlib import Path
 
 
 _CITATION = """
@@ -29,21 +29,8 @@ _CITATION = """
 
 class LogiQA(MultipleChoiceTask):
     VERSION = 0
-    DATASET_PATH = Path("data/logiqa")
-
-    def download(self):
-        if self.DATASET_PATH.exists():
-            return
-        Path.mkdir(self.DATASET_PATH, parents=True)
-        base_url = "https://raw.githubusercontent.com/lgw863/LogiQA-dataset/master"
-        splits = [
-            {"name": "Train", "checksum": "7d5bb1f58278e33b395744cd2ad8d7600faa0b3c4d615c659a44ec1181d759fa"},
-            {"name": "Eval", "checksum": "4c49e6753b7262c001506b9151135abf722247035ab075dad93acdea5789c01f"},
-            {"name": "Test", "checksum": "359acb78c37802208f7fde9e2f6574b8526527c63d6a336f90a53f1932cb4701"}
-        ]
-        for split in splits:
-            file = self.DATASET_PATH / f"{split['name']}.txt"
-            download_file(f"{base_url}/{split['name']}.txt", local_file=str(file), expected_checksum=split["checksum"])
+    DATASET_PATH = inspect.getfile(lm_eval.datasets.logiqa.logiqa)
+    DATASET_NAME = None
 
     def has_training_docs(self):
         return True
@@ -54,7 +41,18 @@ class LogiQA(MultipleChoiceTask):
     def has_test_docs(self):
         return True
 
-    def _convert_standard(self, doc):
+    def training_docs(self):
+        if self._training_docs is None:
+            self._training_docs = list(map(self._process_doc, self.dataset["train"]))
+        return self._training_docs
+
+    def validation_docs(self):
+        return map(self._process_doc, self.dataset["validation"])
+
+    def test_docs(self):
+        return map(self._process_doc, self.dataset["test"])
+
+    def _process_doc(self, doc):
         def format_example(doc, choices):
             """
                 Passage: <passage>
@@ -66,7 +64,7 @@ class LogiQA(MultipleChoiceTask):
                 D. <choice4>
                 Answer:
             """
-            prompt = "Passage: " + doc["passage"] + "\n"
+            prompt = "Passage: " + doc["context"] + "\n"
             prompt += "Question: " + doc["question"] + "\nChoices:\n"
             for choice, option in zip(choices, doc["options"]):
                 prompt += f"{choice.upper()}. {option}\n"
@@ -74,35 +72,17 @@ class LogiQA(MultipleChoiceTask):
             return prompt
         choices = ['a', 'b', 'c', 'd']
         return {
+            "passage": doc["context"], # Used for decontamination
             "query": format_example(doc, choices),
             "choices": doc["options"],
-            "gold": choices.index(doc["answerKey"])
-        }
-
-    def _load_docs(self, filename):
-        def normalize(text):
-            return text.replace(".", ". ").strip()
-
-        with open(filename, 'r') as f:
-            docs = f.read().strip().split("\n\n")
-        for rawdoc in docs:
-            rawdoc = rawdoc.split("\n")
-            doc = {
-                "answerKey": rawdoc[0].strip(),
-                "passage": normalize(rawdoc[1]),
-                "question": normalize(rawdoc[2]),
-                "options": [normalize(option[2:]) for option in rawdoc[3:]]
+            "gold": choices.index(doc["label"])
         }
-            yield self._convert_standard(doc)
-
-    def training_docs(self):
-        return self._load_docs(self.DATASET_PATH / "Train.txt")
-
-    def validation_docs(self):
-        return self._load_docs(self.DATASET_PATH / "Eval.txt")
-
-    def test_docs(self):
-        return self._load_docs(self.DATASET_PATH / "Test.txt")
 
     def doc_to_text(self, doc):
         return doc["query"]
+
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["passage"]

lm_eval/tasks/mathqa.py

@@ -10,7 +10,6 @@ Homepage: https://math-qa.github.io/math-QA/
 """
 import re
 from lm_eval.base import MultipleChoiceTask
-from . common import HFTask
 
 
 _CITATION = """
@@ -25,7 +24,7 @@ _CITATION = """
 """
 
 
-class MathQA(HFTask, MultipleChoiceTask):
+class MathQA(MultipleChoiceTask):
     VERSION = 0
     DATASET_PATH = "math_qa"
     DATASET_NAME = None
@@ -39,13 +38,23 @@ class MathQA(HFTask, MultipleChoiceTask):
     def has_test_docs(self):
         return True
 
-    def _convert_standard(self, doc):
+    def training_docs(self):
+        if self._training_docs is None:
+            self._training_docs = list(map(self._process_doc, self.dataset["train"]))
+        return self._training_docs
 
+    def validation_docs(self):
+        return map(self._process_doc, self.dataset["validation"])
+
+    def test_docs(self):
+        return map(self._process_doc, self.dataset["test"])
+
+    def _process_doc(self, doc):
         answer_idx = ['a', 'b', 'c', 'd', 'e'].index(doc['correct'])
         choices = [c[4:].rstrip(" ,") for c in re.findall(r"[abcd] \) .*?, |e \) .*?$", doc['options'])]
 
         out_doc = {
-            "query": "Question: " + doc['Problem'] +"\nAnswer:",
+            "query": "Question: " + doc['Problem'] + "\nAnswer:",
             "choices": choices,
             "gold": answer_idx,
         }
@@ -53,3 +62,9 @@ class MathQA(HFTask, MultipleChoiceTask):
 
     def doc_to_text(self, doc):
         return doc["query"]
+
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["query"]

lm_eval/tasks/mc_taco.py

@@ -20,9 +20,8 @@ of a question's options. See section 4 of the paper for details.
 Homepage: https://leaderboard.allenai.org/mctaco/submissions/public
 """
 import numpy as np
-from lm_eval.base import rf
 from collections import defaultdict
-from . common import HFTask
+from lm_eval.base import rf, Task
 
 
 _CITATION = """
@@ -35,7 +34,7 @@ _CITATION = """
 """
 
 
-class MCTACO(HFTask):
+class MCTACO(Task):
     VERSION = 0
     DATASET_PATH = "mc_taco"
     DATASET_NAME = None
@@ -49,10 +48,22 @@ class MCTACO(HFTask):
     def has_test_docs(self):
         return True
 
+    def validation_docs(self):
+        return self.dataset["validation"]
+
+    def test_docs(self):
+        return self.dataset["test"]
+
     def doc_to_text(self, doc):
         return f"{doc['sentence']}\nQuestion: {doc['question']}\n"\
             f"Answer: {doc['answer']}\nPlausible:"
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc['question'] + " " + doc['sentence']
+
     def doc_to_target(self, doc):
         return " " + ["no", "yes"][doc['label']]
 

lm_eval/tasks/mutual.py

@@ -7,14 +7,11 @@ modified from Chinese high school English listening comprehension test data.
 
 Homepage: https://github.com/Nealcly/MuTual
 """
-import json
-import zipfile
-import shutil
 import numpy as np
-from pathlib import Path
+import inspect
+import lm_eval.datasets.mutual.mutual
 from lm_eval.base import Task, rf
 from lm_eval.metrics import mean
-from best_download import download_file
 
 
 _CITATION = """
@@ -30,29 +27,10 @@ _CITATION = """
 
 class MuTualBase(Task):
     VERSION = 1
-    BASE_PATH = Path("data/mutual")
+    DATASET_PATH = inspect.getfile(lm_eval.datasets.mutual.mutual)
     DATASET_NAME = None
     CHOICES = ['A', 'B', 'C', 'D']
 
-    def __init__(self):
-        super().__init__()
-
-    def download(self):
-        if self.BASE_PATH.exists():
-            return
-        Path.mkdir(self.BASE_PATH, parents=True)
-        master_zip = Path("data/master.zip")
-        download_file(
-            "https://github.com/Nealcly/MuTual/archive/master.zip",
-            local_file=str(master_zip),
-            expected_checksum="bb325cf6c672f0f02699993a37138b0fa0af6fcfc77ec81dfbe46add4d7b29f9")
-        with zipfile.ZipFile(master_zip, 'r') as zip:
-            zip.extractall("data")
-        Path("data/MuTual-master/data").rename(str(self.BASE_PATH))
-        # Remove left over files and directories.
-        master_zip.unlink()
-        shutil.rmtree("data/MuTual-master")
-
     def has_training_docs(self):
         return True
 
@@ -62,18 +40,11 @@ class MuTualBase(Task):
     def has_test_docs(self):
         return False
 
-    def _load_docs(self, path):
-        for file in sorted(path.iterdir()):
-            if file.suffix != ".txt":
-                continue
-            with open(file, 'r', encoding='utf-8') as f:
-                yield json.load(f)
-
     def training_docs(self):
-        return self._load_docs(self.BASE_PATH / self.DATASET_NAME / "train")
+        return self.dataset["train"]
 
     def validation_docs(self):
-        return self._load_docs(self.BASE_PATH / self.DATASET_NAME / "dev")
+        return self.dataset["validation"]
 
     def test_docs(self):
         return NotImplemented
@@ -81,6 +52,12 @@ class MuTualBase(Task):
     def doc_to_text(self, doc):
         return self.detokenize(doc["article"])
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["article"]
+
     def doc_to_target(self, doc):
         return " " + self.detokenize(doc["options"][self.CHOICES.index(doc["answers"])])
 
@@ -134,8 +111,8 @@ class MuTualBase(Task):
 
 
 class MuTual(MuTualBase):
-    DATASET_NAME = Path("mutual")
+    DATASET_NAME = "mutual"
 
 
 class MuTualPlus(MuTualBase):
-    DATASET_NAME = Path("mutual_plus")
+    DATASET_NAME = "mutual_plus"

lm_eval/tasks/naturalqs.py

@@ -15,8 +15,7 @@ not even bother with the train set.
 
 Homepage: https://ai.google.com/research/NaturalQuestions
 """
-import random
-from . common import HFTask
+from lm_eval.base import Task
 from itertools import islice
 
 
@@ -30,7 +29,7 @@ _CITATION = """
 """
 
 
-class NaturalQs(HFTask):
+class NaturalQs(Task):
     VERSION = 0
     DATASET_PATH = "natural_questions"
     DATASET_NAME = None
@@ -47,7 +46,12 @@ class NaturalQs(HFTask):
     def training_docs(self):
         # Cache training for faster few-shot.
         # Data is too large to fit in memory.
-        return self.data["train"]
+        if self._training_docs is None:
+            self._training_docs = list(self.dataset["train"])
+        return self._training_docs
+
+    def validation_docs(self):
+        return self.dataset["validation"]
 
     def fewshot_examples(self, k, rnd):
         # Data is too large to fit in memory. We just sample from the first bit.
@@ -59,6 +63,12 @@ class NaturalQs(HFTask):
     def doc_to_text(self, doc):
         return 'Q: ' + doc['question']['text'] + '\n\n' + 'A:'
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc['question']['text']
+
     def doc_to_target(self, doc):
         # There's a short answer and a long answer. Based on the paper, I'm using the long answer.
         short_answer = doc['annotations']['short_answers'][0]['text']

lm_eval/tasks/openbookqa.py

@@ -15,7 +15,6 @@ based algorithm and a word co-occurrence algorithm.
 Homepage: https://allenai.org/data/open-book-qa
 """
 from lm_eval.base import MultipleChoiceTask
-from .common import HFTask
 
 
 _CITATION = """
@@ -28,7 +27,7 @@ _CITATION = """
 """
 
 
-class OpenBookQA(HFTask, MultipleChoiceTask):
+class OpenBookQA(MultipleChoiceTask):
     VERSION = 0
     DATASET_PATH = "openbookqa"
     DATASET_NAME = "main"
@@ -42,7 +41,18 @@ class OpenBookQA(HFTask, MultipleChoiceTask):
     def has_test_docs(self):
         return True
 
-    def _convert_standard(self, doc):
+    def training_docs(self):
+        if self._training_docs is None:
+            self._training_docs = list(map(self._process_doc, self.dataset["train"]))
+        return self._training_docs
+
+    def validation_docs(self):
+        return map(self._process_doc, self.dataset["validation"])
+
+    def test_docs(self):
+        return map(self._process_doc, self.dataset["test"])
+
+    def _process_doc(self, doc):
         out_doc = {
             "id": doc["id"],
             "query": doc["question_stem"],
@@ -53,3 +63,9 @@ class OpenBookQA(HFTask, MultipleChoiceTask):
 
     def doc_to_text(self, doc):
         return doc["query"]
+
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["query"]

lm_eval/tasks/pile.py

@@ -10,15 +10,9 @@ math, computer science, and philosophy papers.
 
 Homepage: https://pile.eleuther.ai/
 """
-import os
-
-import lm_dataformat
-import abc
-import numpy as np
-from lm_eval.base import rf, PerplexityTask
-from ..metrics import mean, matthews_corrcoef, f1_score
-from ..utils import general_detokenize
-from best_download import download_file
+import inspect
+import lm_eval.datasets.pile.pile
+from lm_eval.base import PerplexityTask
 
 
 _CITATION = """
@@ -31,32 +25,10 @@ _CITATION = """
 """
 
 
-class PilePerplexityTask(PerplexityTask, abc.ABC):
+class PilePerplexityTask(PerplexityTask):
     VERSION = 1
-
-    PILE_SET_NAME = None
-    VAL_PATH = 'data/pile/val.jsonl.zst'
-    TEST_PATH = 'data/pile/test.jsonl.zst'
-
-    def download(self):
-        # TODO: separate pile val/test out by component so we don't have to scan the entire file once per set
-        if not os.path.exists("data/pile/test.jsonl.zst"):
-            # todo use new best_download fallback api
-            os.makedirs("data/pile/", exist_ok=True)
-            download_file("http://eaidata.bmk.sh/data/pile/val.jsonl.zst", local_file=self.VAL_PATH, expected_checksum="264c875d8bbd355d8daa9d032b75fd8fb91606218bb84dd1155b203fcd5fab92")
-            download_file("http://eaidata.bmk.sh/data/pile/test.jsonl.zst", local_file=self.TEST_PATH, expected_checksum="0bb28c52d0b5596d389bf179ce2d43bf7f7ffae76b0d2d20b180c97f62e0975e")
-
-    def validation_docs(self):
-        rdr = lm_dataformat.Reader(self.VAL_PATH)
-        for doc, metadata in rdr.stream_data(get_meta=True):
-            if metadata["pile_set_name"] == self.PILE_SET_NAME:
-                yield doc
-
-    def test_docs(self):
-        rdr = lm_dataformat.Reader(self.TEST_PATH)
-        for doc, metadata in rdr.stream_data(get_meta=True):
-            if metadata["pile_set_name"] == self.PILE_SET_NAME:
-                yield doc
+    DATASET_PATH = inspect.getfile(lm_eval.datasets.pile.pile)
+    DATASET_NAME = None
 
     def has_validation_docs(self):
         return True
@@ -64,90 +36,98 @@ class PilePerplexityTask(PerplexityTask, abc.ABC):
     def has_test_docs(self):
         return True
 
+    def validation_docs(self):
+        for doc in self.dataset["validation"]:
+            yield doc["text"]
+
+    def test_docs(self):
+        for doc in self.dataset["test"]:
+            yield doc["text"]
+
 
 class PileArxiv(PilePerplexityTask):
-    PILE_SET_NAME = "ArXiv"
+    DATASET_NAME = "pile_arxiv"
 
 
 class PileBooks3(PilePerplexityTask):
-    PILE_SET_NAME = "Books3"
+    DATASET_NAME = "pile_books3"
 
 
 class PileBookCorpus2(PilePerplexityTask):
-    PILE_SET_NAME = "BookCorpus2"
+    DATASET_NAME = "pile_bookcorpus2"
 
 
 class PileDmMathematics(PilePerplexityTask):
-    PILE_SET_NAME = "DM Mathematics"
+    DATASET_NAME = "pile_dm-mathematics"
 
 
 class PileEnron(PilePerplexityTask):
-    PILE_SET_NAME = "Enron Emails"
+    DATASET_NAME = "pile_enron"
 
 
 class PileEuroparl(PilePerplexityTask):
-    PILE_SET_NAME = "EuroParl"
+    DATASET_NAME = "pile_europarl"
 
 
 class PileFreeLaw(PilePerplexityTask):
-    PILE_SET_NAME = "FreeLaw"
+    DATASET_NAME = "pile_freelaw"
 
 
 class PileGithub(PilePerplexityTask):
-    PILE_SET_NAME = "Github"
+    DATASET_NAME = "pile_github"
 
 
 class PileGutenberg(PilePerplexityTask):
-    PILE_SET_NAME = "Gutenberg (PG-19)"
+    DATASET_NAME = "pile_gutenberg"
 
 
 class PileHackernews(PilePerplexityTask):
-    PILE_SET_NAME = "HackerNews"
+    DATASET_NAME = "pile_hackernews"
 
 
 class PileNIHExporter(PilePerplexityTask):
-    PILE_SET_NAME = "NIH ExPorter"
+    DATASET_NAME = "pile_nih-exporter"
 
 
 class PileOpenSubtitles(PilePerplexityTask):
-    PILE_SET_NAME = "OpenSubtitles"
+    DATASET_NAME = "pile_opensubtitles"
 
 
 class PileOpenWebText2(PilePerplexityTask):
-    PILE_SET_NAME = "OpenWebText2"
+    DATASET_NAME = "pile_openwebtext2"
 
 
 class PilePhilPapers(PilePerplexityTask):
-    PILE_SET_NAME = "PhilPapers"
+    DATASET_NAME = "pile_philpapers"
 
 
 class PilePileCc(PilePerplexityTask):
-    PILE_SET_NAME = "Pile-CC"
+    DATASET_NAME = "pile_pile-cc"
 
 
 class PilePubmedAbstracts(PilePerplexityTask):
-    PILE_SET_NAME = "PubMed Abstracts"
+    DATASET_NAME = "pile_pubmed-abstracts"
 
 
 class PilePubmedCentral(PilePerplexityTask):
-    PILE_SET_NAME = "PubMed Central"
+    DATASET_NAME = "pile_pubmed-central"
 
 
 class PileStackExchange(PilePerplexityTask):
-    PILE_SET_NAME = "StackExchange"
+    DATASET_NAME = "pile_stackexchange"
 
 
 class PileUspto(PilePerplexityTask):
-    PILE_SET_NAME = "USPTO Backgrounds"
+    DATASET_NAME = "pile_upsto"
 
 
 class PileUbuntuIrc(PilePerplexityTask):
-    PILE_SET_NAME = "Ubuntu IRC"
+    DATASET_NAME = "pile_ubuntu-irc"
 
 
 class PileWikipedia(PilePerplexityTask):
-    PILE_SET_NAME = "Wikipedia (en)"
+    DATASET_NAME = "pile_wikipedia"
 
 
 class PileYoutubeSubtitles(PilePerplexityTask):
-    PILE_SET_NAME = "YoutubeSubtitles"
+    DATASET_NAME = "pile_youtubesubtitles"

lm_eval/tasks/piqa.py

@@ -9,10 +9,7 @@ actually learning about the world?
 
 Homepage: https://yonatanbisk.com/piqa/
 """
-import numpy as np
-from lm_eval.base import MultipleChoiceTask, rf
-from ..metrics import mean
-from . common import HFTask
+from lm_eval.base import MultipleChoiceTask
 
 
 _CITATION = """
@@ -29,7 +26,7 @@ _CITATION = """
 """
 
 
-class PiQA(HFTask, MultipleChoiceTask):
+class PiQA(MultipleChoiceTask):
     VERSION = 0
     DATASET_PATH = "piqa"
     DATASET_NAME = None
@@ -43,7 +40,15 @@ class PiQA(HFTask, MultipleChoiceTask):
     def has_test_docs(self):
         return False
 
-    def _convert_standard(self, doc):
+    def training_docs(self):
+        if self._training_docs is None:
+            self._training_docs = list(map(self._process_doc, self.dataset["train"]))
+        return self._training_docs
+
+    def validation_docs(self):
+        return map(self._process_doc, self.dataset["validation"])
+
+    def _process_doc(self, doc):
         out_doc = {
             "goal": doc["goal"],
             "choices": [doc["sol1"], doc["sol2"]],
@@ -53,3 +58,9 @@ class PiQA(HFTask, MultipleChoiceTask):
 
     def doc_to_text(self, doc):
         return "Question: " + doc["goal"] + "\nAnswer:"
+
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["goal"]

lm_eval/tasks/prost.py

@@ -15,7 +15,6 @@ have been trained on data not specifically collected to succeed on PROST."
 Homepage: https://github.com/nala-cub/prost
 """
 from lm_eval.base import MultipleChoiceTask
-from . common import HFTask
 
 
 _CITATION = """
@@ -36,7 +35,7 @@ _CITATION = """
 """
 
 
-class PROST(HFTask, MultipleChoiceTask):
+class PROST(MultipleChoiceTask):
     VERSION = 0
     DATASET_PATH = "corypaik/prost"
     DATASET_NAME = None
@@ -50,6 +49,9 @@ class PROST(HFTask, MultipleChoiceTask):
     def has_test_docs(self):
         return True
 
+    def test_docs(self):
+        return map(self._process_doc, self.dataset["test"])
+
     def fewshot_context(self, doc, num_fewshot, provide_description=None, rnd=None, description=None):
         assert num_fewshot == 0, 'PROST is designed to probe models in a zero-shot fashion only.'
         return super().fewshot_context(
@@ -59,7 +61,7 @@ class PROST(HFTask, MultipleChoiceTask):
             description=description
         )
 
-    def _convert_standard(self, doc):
+    def _process_doc(self, doc):
         out_doc = {
             "query": f"{doc['context']}\nQuestion: {doc['ex_question']}\nAnswer:",
             "choices": [doc['A'], doc['B'], doc['C'], doc['D']],
@@ -69,3 +71,9 @@ class PROST(HFTask, MultipleChoiceTask):
 
     def doc_to_text(self, doc):
         return doc["query"]
+
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["query"]

lm_eval/tasks/pubmedqa.py

@@ -16,9 +16,8 @@ and (4) a yes/no/maybe answer which summarizes the conclusion.
 Homepage: https://pubmedqa.github.io/
 """
 import numpy as np
-from .common import HFTask
-from lm_eval.base import rf
-from ..metrics import mean
+from lm_eval.base import rf, Task
+from lm_eval.metrics import mean
 
 
 _CITATION = """
@@ -32,7 +31,7 @@ _CITATION = """
 """
 
 
-class Pubmed_QA(HFTask):
+class Pubmed_QA(Task):
     VERSION = 0
     DATASET_PATH = "pubmed_qa"
     DATASET_NAME = "pqa_labeled"
@@ -49,7 +48,7 @@ class Pubmed_QA(HFTask):
     def test_docs(self):
         if self.has_test_docs():
             # HF is labelled as train but its really just for testing
-            return self.data["train"]
+            return self.dataset["train"]
 
     def doc_to_text(self, doc):
         ctxs = "\n".join(doc["context"]["contexts"])
@@ -59,6 +58,12 @@ class Pubmed_QA(HFTask):
             doc["final_decision"]
         )
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["question"] + " " + "\n".join(doc["context"]["contexts"])
+
     def doc_to_target(self, doc):
         return " {}".format(doc["final_decision"])
 

lm_eval/tasks/qa4mre.py

@@ -13,9 +13,6 @@ and Entrance Exam.
 
 Homepage: http://nlp.uned.es/clef-qa/repository/qa4mre.php
 """
-import os
-import xml.etree.ElementTree as ET
-from best_download import download_file
 from lm_eval.base import MultipleChoiceTask
 
 
@@ -31,35 +28,8 @@ _CITATION = """
 
 class QA4MRE(MultipleChoiceTask):
     VERSION = 0
-    YEAR = None
-    def download(self):
-        year = self.YEAR
-        lang = "EN"
-        base_path = (
-            "http://nlp.uned.es/clef-qa/repository/js/scripts/downloadFile.php?"
-            "file=/var/www/html/nlp/clef-qa/repository/resources/QA4MRE/"
-        )
-        # TODO: add side tasks?
-        variable_year_path = {
-            2011: '2011/Training_Data/Goldstandard/',
-            2012: '2012/Main_Task/Training_Data/Goldstandard/Used_in_Evaluation/',
-            2013: '2013/Main_Task/Training_Data/Goldstandard/'
-        }
-        sha256sums = {
-            2011 : "6d2524952a3a015f2a82df785b85b5578681e3602ec276b4e72c01f4ebc50034",
-            2012 : "f9edaf408f8ac93f89a643a0d0b19263a1bb5ce64f19b2af10df279a656dfb24",
-            2013 : "c60e5aa4ec77e0493ef0b11d46bd1d74d58a499a3a2f871b8cf3af9536f0f094", 
-        }
-        vpath = variable_year_path[year]
-        url_path = f"{base_path}{vpath}QA4MRE-{year}-{lang}_GS.xml"
-        if not os.path.exists("data/qa4mre"):
-            os.makedirs("data/qa4mre", exist_ok=True)
-        if not os.path.isfile(f"data/qa4mre/QA4MRE-{year}-{lang}"):
-            download_file(
-                url_path,
-                local_file=f"data/qa4mre/QA4MRE-{year}-{lang}_GS.xml",
-                expected_checksum=sha256sums[year],
-                )
+    DATASET_PATH = "qa4mre"
+    DATASET_NAME = None
 
     def has_training_docs(self):
         return False
@@ -70,39 +40,37 @@ class QA4MRE(MultipleChoiceTask):
     def has_test_docs(self):
         return True
 
-    def _convert_standard(self, question):
-        choices = [i.text for i in question.iter('answer')]
+    def test_docs(self):
+        # `qa4mre` only has train data so we use it for the test docs.
+        return map(self._process_doc, self.dataset["train"])
+
+    def _process_doc(self, doc):
+        choices = doc["answer_options"]["answer_str"]
         out_doc = {
-            "query" : question.find('q_str').text,
+            "source": doc["document_str"].strip().replace("\'", "'"),
+            "query": doc["question_str"],
             "choices": choices,
-            "gold" : int(question.find("./answer[@correct='Yes']").attrib["a_id"]) - 1,
+            "gold": int(doc["correct_answer_id"]) - 1,
         }
         return out_doc
 
-    def load_docs(self, textfilename, tfds=False):
-        tree = ET.parse(textfilename)
-        root = tree.getroot()
-        # TODO: context is much larger than the context sometimes
-        # at the moment, it just gets left-truncated by LM automatically, and maybe that's good enough?
-        for reading_test in root.iter('reading-test'):
-            src = reading_test[0].text
-            src = src.strip().replace("\'", "'")
-            for qid, question in enumerate(reading_test.iter('q')):
-                out_doc = self._convert_standard(question)
-                out_doc['source'] = src
-                yield out_doc
-
-    def test_docs(self):
-        return self.load_docs(f"data/qa4mre/QA4MRE-{self.YEAR}-EN_GS.xml")
-
     def doc_to_text(self, doc):
         return "{}\nQuestion: {}\nAnswer:".format(doc["source"], doc["query"])
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["source"] + " " + doc["query"]
+
+
 class QA4MRE_2011(QA4MRE):
-    YEAR = 2011
+    DATASET_NAME = "2011.main.EN"
+
 
 class QA4MRE_2012(QA4MRE):
-    YEAR = 2012
+    DATASET_NAME = "2012.main.EN"
+
 
 class QA4MRE_2013(QA4MRE):
-    YEAR = 2013
+    DATASET_NAME = "2013.main.EN"

lm_eval/tasks/qasper.py

@@ -11,13 +11,10 @@ provide supporting evidence to answers.
 Homepage: https://allenai.org/data/qasper
 """
 from collections import Counter
-from math import exp
-import random
 import re
 import string
-from lm_eval.base import rf
+from lm_eval.base import rf, Task
 from lm_eval.metrics import f1_score, mean
-from .common import HFTask
 
 
 _CITATION = """
@@ -104,11 +101,20 @@ def token_f1_score(prediction, ground_truth):
     return f1
 
 
-class QASPER(HFTask):
+class QASPER(Task):
     VERSION = 0
     DATASET_PATH = "qasper"
     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 doc_to_text(self, doc):
         return (
             "TITLE: "
@@ -130,14 +136,14 @@ class QASPER(HFTask):
         return " " + answer
 
     def training_docs(self):
-        for doc in self.data["train"]:
-            yield from self.process_doc(doc)
+        for doc in self.dataset["train"]:
+            yield from self._process_doc(doc)
 
     def validation_docs(self):
-        for doc in self.data["train"]:
-            yield from self.process_doc(doc)
+        for doc in self.dataset["validation"]:
+            yield from self._process_doc(doc)
 
-    def process_doc(self, doc):
+    def _process_doc(self, doc):
         """Given a `doc`, flatten it out so that each JSON blob
         contains exactly one question and one answer. Logic taken from
         the reference implementation available at