Merge pull request #146 from EleutherAI/translation

Translation

.gitignore

 env
 *.pyc
 data/
+.idea
+lm_cache
\ No newline at end of file

lm_eval/base.py

 import abc
 import random
 import numpy as np
-import sklearn
-import math
+
+from lm_eval.metrics import mean
 
 
 class LM(abc.ABC):
@@ -30,6 +30,7 @@ class LM(abc.ABC):
         """
         pass
 
+    # TODO: Add an optional max length
     @abc.abstractmethod
     def greedy_until(self, requests):
         """Generate greedily until a stopping sequence
@@ -61,6 +62,14 @@ class LM(abc.ABC):
 
 
 class Task(abc.ABC):
+    """A task represents an entire benchmark including its dataset, problems,
+    answers, and evaluation methods. See BoolQ for a simple example implementation
+
+    A `doc` can be any python object which represents one instance of evaluation.
+    This is usually a dictionary e.g.
+        {"question": ..., "answer": ...} or
+        {"question": ..., question, answer)
+    """
     def __init__(self):
         self.download()
         self._training_docs = None
@@ -148,9 +157,9 @@ class Task(abc.ABC):
     @abc.abstractmethod
     def aggregation(self):
         """
-        :returns: {str: [float] -> float}
+        :returns: {str: [metric_score] -> float}
             A dictionary where keys are the names of submetrics and values are 
-            functions that aggregate a list of metrics
+            functions that aggregate a list of metric scores
         """
         pass
 
@@ -213,60 +222,6 @@ class MultipleChoiceTask(Task):
         }
 
 
-def mean(arr):
-    return sum(arr) / len(arr)
-
-
-def median(arr):
-    return arr[len(arr) // 2]
-
-
-def matthews_corrcoef(items):
-    unzipped_list = list(zip(*items))
-    golds = unzipped_list[0]
-    preds = unzipped_list[1]
-    return sklearn.metrics.matthews_corrcoef(golds, preds)
-
-
-def f1_score(items):
-    unzipped_list = list(zip(*items))
-    golds = unzipped_list[0]
-    preds = unzipped_list[1]
-    fscore = sklearn.metrics.f1_score(golds, preds)
-
-    return np.max(fscore)
-
-
-def acc_all(items):
-    # Only count as correct if all answers are labeled correctly for each question
-    question_scoring_dict = {}
-    preds = list(zip(*items))[0]
-    docs = list(zip(*items))[1]
-
-    for doc, pred in zip(docs, preds):
-        question_id = doc["idx"]["question"]
-        if question_id not in question_scoring_dict:
-            question_scoring_dict[question_id] = []
-
-        gold_label = doc["label"] == 1
-        question_scoring_dict[question_id].append(gold_label == pred)
-            
-    acc = np.mean([int(all(x)) for x in question_scoring_dict.values()])
-    return acc
-
-
-def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
-    """Compute max metric between prediction and each ground truth."""
-    scores_for_ground_truths = []
-    for ground_truth in ground_truths:
-        score = metric_fn(prediction, ground_truth)
-        scores_for_ground_truths.append(score)
-    return max(scores_for_ground_truths)
-
-
-def perplexity(items):
-    return math.exp(-mean(items))
-
 req_ret_lens = {
     'loglikelihood': 2,
     'greedy_until': None,

lm_eval/metrics.py

+import math
+from pprint import pprint
+
+import numpy as np
+import sacrebleu
+import sklearn
+
+
+def mean(arr):
+    return sum(arr) / len(arr)
+
+
+def median(arr):
+    return arr[len(arr) // 2]
+
+
+def matthews_corrcoef(items):
+    unzipped_list = list(zip(*items))
+    golds = unzipped_list[0]
+    preds = unzipped_list[1]
+    return sklearn.metrics.matthews_corrcoef(golds, preds)
+
+
+def f1_score(items):
+    unzipped_list = list(zip(*items))
+    golds = unzipped_list[0]
+    preds = unzipped_list[1]
+    fscore = sklearn.metrics.f1_score(golds, preds)
+
+    return np.max(fscore)
+
+
+def acc_all(items):
+    # Only count as correct if all answers are labeled correctly for each question
+    question_scoring_dict = {}
+    preds = list(zip(*items))[0]
+    docs = list(zip(*items))[1]
+
+    for doc, pred in zip(docs, preds):
+        question_id = doc["idx"]["question"]
+        if question_id not in question_scoring_dict:
+            question_scoring_dict[question_id] = []
+
+        gold_label = doc["label"] == 1
+        question_scoring_dict[question_id].append(gold_label == pred)
+
+    acc = np.mean([int(all(x)) for x in question_scoring_dict.values()])
+    return acc
+
+
+def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
+    """Compute max metric between prediction and each ground truth."""
+    scores_for_ground_truths = []
+    for ground_truth in ground_truths:
+        score = metric_fn(prediction, ground_truth)
+        scores_for_ground_truths.append(score)
+    return max(scores_for_ground_truths)
+
+
+def perplexity(items):
+    return math.exp(-mean(items))
+
+
+def bleu(items):
+    """The Bilingual Evaluation Understudy Score, or BLEU for short, is a metric
+    for evaluating a generated sentence to a reference sentence. It counts matching
+    n-grams in the candidate translation to n-grams in the reference text, where
+    1-gram or unigram would be each token and a bigram comparison would be each
+    word pair. The comparison is made regardless of word order
+    Source: https://machinelearningmastery.com/calculate-bleu-score-for-text-python/
+    Paper: https://www.aclweb.org/anthology/P02-1040/
+
+    Higher is better
+    """
+    refs = list(zip(*items))[0]
+    preds = list(zip(*items))[1]
+    refs, preds = _sacreformat(refs, preds)
+    return sacrebleu.corpus_bleu(preds, refs).score
+
+
+def chrf(items):
+    """chrF++ is a tool for automatic evaluation of machine translation output
+    based on character n-gram precision and recall enhanced with word n-grams.
+    Source: https://github.com/m-popovic/chrF
+    Paper: https://www.aclweb.org/anthology/W15-3049.pdf
+
+    Higher is better  # TODO I think
+    """
+    refs = list(zip(*items))[0]
+    preds = list(zip(*items))[1]
+    refs, preds = _sacreformat(refs, preds)
+    return sacrebleu.corpus_chrf(preds, refs).score
+
+
+def ter(items):
+    """Translation Error Rate is an error metric for machine translation that
+    measures the number of edits required to change a system output into one
+    of the references
+    Source: http://www.cs.umd.edu/~snover/tercom/
+    Paper: http://mt-archive.info/AMTA-2006-Snover.pdf
+
+    Lower is better
+    """
+    refs = list(zip(*items))[0]
+    preds = list(zip(*items))[1]
+    refs, preds = _sacreformat(refs, preds)
+    return sacrebleu.corpus_ter(preds, refs).score
+
+
+def _sacreformat(refs, preds):
+    """Format refs and preds for sacrebleu corpus calculation. It is very particular"""
+    # Sacrebleu expects (List[str], List[List[str])
+    #   e.g. sacrebleu.corpus_bleu([pred_t], [[ref1_stream], [ref2_stream], ...])
+
+    # Note [ref1_stream] is the first reference for each pred.
+    # So lists are size N and (M, N) for N preds and M possible refs for each pred
+    # This is a different order of dimensions that I would expect
+
+    # We expect refs to be List[str] or List[List[str]], the outer list corresponding to preds
+    # Must become List[List[str]] with the inner list corresponding to preds
+    if not isinstance(refs, list):
+        refs = list(refs)
+    if not isinstance(refs[0], list):
+        refs = [[ref] for ref in refs]
+    refs = list(zip(*refs))
+    # Note the number of refs in each ref list much match the number of preds
+
+    # We expect preds to be List[str] or List[List[str]]. Must become List[str]
+    if not isinstance(preds, list):
+        preds = list(preds)
+    if isinstance(preds[0], list):
+        assert len(preds[0]) == 1, f"Pred must be a str, was {preds[0]}"
+        preds = [pred[0] for pred in preds]
+
+    return refs, preds

lm_eval/tasks/__init__.py

+from pprint import pprint
+
 from . import superglue
 from . import glue
 from . import arc
@@ -21,6 +23,7 @@ from . import pubmedqa
 from . import sciq
 from . import webqs
 from . import qa4mre
+from . import translation
 from . import headqa
 from . import mathqa
 
@@ -88,6 +91,11 @@ TASK_REGISTRY = {
     "arithmetic_2dm": arithmetic.Arithmetic2DMultiplication,
     "arithmetic_1dc": arithmetic.Arithmetic1DComposite,
 
+    # TODO Perhaps make these groups of tasks
+    #   e.g. anli, arithmetic, openai_translations, harness_translations
+
+    # e.g. wmt14-fr-en
+    **translation.create_tasks_from_benchmarks(translation.selected_benchmarks)
 }
 
 
@@ -95,7 +103,12 @@ ALL_TASKS = sorted(list(TASK_REGISTRY))
 
 
 def get_task(task_name):
+    try:
         return TASK_REGISTRY[task_name]
+    except KeyError as e:
+        print("Available tasks:")
+        pprint(TASK_REGISTRY)
+        raise KeyError(f"Missing task {task_name}")
 
 
 def get_task_dict(task_name_list):

lm_eval/tasks/anli.py

 import numpy as np
-from lm_eval.base import rf, mean
+from lm_eval.base import rf
+from ..metrics import mean
 from . common import HFTask
 
 class ANLIBase(HFTask):

lm_eval/tasks/arc.py

+import numpy as np
 from lm_eval.base import MultipleChoiceTask
-from .common import HFTask
+from ..metrics import mean
+from . common import HFTask
 
 
 class ARCEasy(HFTask, MultipleChoiceTask):

lm_eval/tasks/arithmetic.py

@@ -2,7 +2,8 @@ import abc
 import json
 import os
 from collections import namedtuple
-from lm_eval.base import Task, mean, rf
+from lm_eval.base import Task, rf
+from lm_eval.metrics import mean
 from best_download import download_file
 
 ArithmeticDoc = namedtuple('ArithmeticDoc', ['context', 'completion'])

lm_eval/tasks/common.py

 import datasets
 import numpy as np
+
+import lm_eval.metrics
 from ..base import Task
 
 
@@ -44,7 +46,7 @@ class HFTask(Task):
 
 
 def simple_accuracy_metric(preds, golds):
-    acc = float((np.array(preds) == np.array(golds)).mean())
+    acc = float(lm_eval.metrics.mean())
     return {
         "major": acc,
         "minor": {"acc": acc},

lm_eval/tasks/glue.py

 import numpy as np
-from lm_eval.base import rf, mean, f1_score, matthews_corrcoef
+from lm_eval.base import rf
+from ..metrics import mean, matthews_corrcoef, f1_score
 from scipy.stats import pearsonr, spearmanr
 from tqdm import auto as tqdm_lib
 from . common import HFTask, yesno

lm_eval/tasks/lambada.py

-from lm_eval.base import Task, rf, mean, perplexity
+from lm_eval.base import Task, rf
+from lm_eval.metrics import mean, perplexity
 from lm_eval.utils import sh
 import json
 import math

lm_eval/tasks/piqa.py

 import numpy as np
-from lm_eval.base import rf, mean
+from lm_eval.base import rf
+from ..metrics import mean
 from . common import HFTask
 
 

lm_eval/tasks/pubmedqa.py

@@ -2,7 +2,8 @@ import numpy as np
 import json
 import random
 from .common import HFTask 
-from lm_eval.base import rf, mean
+from lm_eval.base import rf
+from ..metrics import mean
 
 
 class Pubmed_QA(HFTask):

lm_eval/tasks/qa4mre.py

 import os
 import numpy as np
 from best_download import download_file
-from lm_eval.base import MultipleChoiceTask, rf, mean
+from lm_eval.base import MultipleChoiceTask, rf
+from lm_eval.metrics import mean
 import xml.etree.ElementTree as ET
 import random
 

lm_eval/tasks/race.py

 import collections
 import datasets
 import numpy as np
-from lm_eval.base import rf, mean
+from lm_eval.base import rf
+from ..metrics import mean
 from . common import HFTask
 
 import os

lm_eval/tasks/sat.py

 import json
 import random
 import os
-from lm_eval.base import MultipleChoiceTask, rf, mean
+from lm_eval.base import MultipleChoiceTask, rf
+from ..metrics import mean
 from tqdm import auto as tqdm_lib
 from . common import simple_accuracy_metric
 import numpy as np

lm_eval/tasks/sciq.py

 import os
 import json
 from ..utils import sh
-from lm_eval.base import MultipleChoiceTask, rf, mean
+from lm_eval.base import MultipleChoiceTask, rf
+from ..metrics import mean
 import zipfile
 from best_download import download_file
 

lm_eval/tasks/superglue.py

@@ -5,7 +5,8 @@ To-do:
 """
 import numpy as np
 from . common import HFTask, yesno
-from lm_eval.base import rf, mean, acc_all, metric_max_over_ground_truths
+from lm_eval.base import rf
+from ..metrics import mean, acc_all, metric_max_over_ground_truths
 import sklearn
 import transformers.data.metrics.squad_metrics as squad_metrics
 from ..utils import general_detokenize

lm_eval/tasks/translation.py

+import abc
+import json
+import random
+import os
+from pprint import pprint
+
+import pycountry
+from sacrebleu import sacrebleu
+import logging
+
+from lm_eval import metrics
+from lm_eval.base import Task, rf
+
+"""
+This file implements translation tasks using datasets from WMT conferences, provided by sacrebleu.
+Traditionally they are evaluated with BLEU scores. TER and CHRF are other options.
+
+See sacrebleu.DATASETS for all available datasets. There are a lot!
+"""
+sacrebleu_datasets = sacrebleu.DATASETS
+
+
+########################################
+# Benchmarks one might want to run
+########################################
+
+# 6 total
+gpt3_benchmarks = {
+    "wmt14": ['en-fr', 'fr-en'],  # French
+    "wmt16": ['en-ro', 'ro-en', 'de-en', 'en-de'],  # German, Romanian
+}
+# 14 total
+selected_benchmarks = {
+    **gpt3_benchmarks,
+    "wmt20": ['fr-de', 'de-fr', 'en-ru', 'ru-en', 'en-iu', 'iu-en'],  # French, German, Russian, Inuit
+    "iwslt17": ['en-ar', 'ar-en']  # Arabic
+}
+# 319 total
+all_benchmarks = {
+    ts: sacrebleu.get_langpairs_for_testset(ts)
+    for ts in sacrebleu.get_available_testsets()
+}
+
+available_tests = {
+    "gpt3_tests": gpt3_benchmarks,
+    "selected_tests": selected_benchmarks,
+    "all_tests": all_benchmarks
+}
+
+def create_tasks_from_benchmarks(benchmark_dict):
+    """Creates a dictionary of tasks from a dict
+    :param benchmark_dict: { dataset: [lang_pair, ...] }
+    :return: {task_name: task}
+        e.g. {wmt14-fr-en: Task, wmt16-de-en: Task}
+    """
+    return {
+        f"{dataset}-{language_pair}": create_translation_task(dataset, language_pair)
+        for dataset, language_pairs in benchmark_dict.items()
+        for language_pair in language_pairs
+    }
+
+########################################
+# Tasks
+########################################
+
+def create_translation_task(dataset, language_pair):
+    class TranslationTask(GeneralTranslationTask):
+        def __init__(self):
+            super().__init__(dataset, language_pair)
+    return TranslationTask
+
+class GeneralTranslationTask(Task):
+
+    # e.g. ("wmt14", "fr-en")
+    def __init__(self, sacrebleu_dataset, sacrebleu_language_pair=None):
+        self.sacrebleu_dataset = sacrebleu_dataset
+        self.sacrebleu_language_pair = sacrebleu_language_pair
+        self.src_file = self.ref_file = self.src_data = self.ref_data = None
+
+        super().__init__()
+
+    def download(self):
+        # This caches in the users home dir automatically
+        self.src_file, self.ref_file = \
+            sacrebleu.download_test_set(self.sacrebleu_dataset, self.sacrebleu_language_pair)
+        self.src_data, self.ref_data = [
+            [line.rstrip() for line in sacrebleu.smart_open(file)]
+            for file in (self.src_file, self.ref_file)
+        ]
+
+    def has_training_docs(self):
+        """Whether the task has a training set"""
+        # TODO In the future we could be more discerning. Some more recent tests have train and dev sets
+        return False
+
+    def has_validation_docs(self):
+        """Whether the task has a validation set"""
+        return False
+
+    def has_test_docs(self):
+        """Whether the task has a test set"""
+        return True
+
+    def test_docs(self):
+        """
+        :return: Iterable[obj]
+            A iterable of any object, that doc_to_text can handle
+        """
+        return [{
+            "src": src,
+            "ref": ref
+        } for src, ref in zip(self.src_data, self.ref_data)]
+
+    def doc_to_text(self, doc):
+        return doc["src"]
+
+    def doc_to_target(self, doc):
+        # TODO Note that some exotic tests have multiple ref lines.
+        #  How does sacrebleu handle opening these files?
+        return doc["ref"]
+
+    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`.
+        """
+        return rf.greedy_until(ctx, ["\n"])
+
+    def process_results(self, doc, results):
+        # These metrics are corpus-level not sentence level, so we'll hide the
+        # results in this dict and compute the corpus score in the aggregate method
+        ref_pred = (doc["ref"], results)
+        return {
+            "bleu": ref_pred,
+            "chrf": ref_pred,
+            "ter": ref_pred,
+        }
+
+    def aggregation(self):
+        """
+        :returns: {str: [float] -> float}
+            A dictionary where keys are the names of submetrics and values are
+            functions that aggregate a list of metrics
+        """
+        return {
+            "bleu": metrics.bleu,
+            "chrf": metrics.chrf,
+            "ter": metrics.ter,
+        }
+
+    def higher_is_better(self):
+        """
+        :returns: {str: bool}
+            A dictionary where keys are the names of submetrics and values are
+            whether a higher value of the submetric is better
+        """
+        return {
+            "bleu": True,
+            "chrf": True,
+            "ter": False,
+        }
+
+    def fewshot_description(self):
+        language_codes = self.sacrebleu_language_pair.split("-")
+        src_lang = code_to_language(language_codes[0])
+        tar_lang = code_to_language(language_codes[1])
+        return f"Translate these {src_lang} phrases to {tar_lang}."
+
+    # TODO This should be something like
+    #   French: {src_line}
+    #   English: {ref_line}
+    def fewshot_context(self, doc, num_fewshot, provide_description):
+        return ""
+
+    def __str__(self):
+        language_codes = self.sacrebleu_language_pair.split("-")
+        src_lang = code_to_language(language_codes[0])
+        tar_lang = code_to_language(language_codes[1])
+        return f"{self.sacrebleu_dataset.upper()} {src_lang} to {tar_lang} Task"
+
+
+########################################
+# Util
+########################################
+
+
+def code_to_language(code):
+    # key is alpha_2 or alpha_3 depending on the code length
+    language_tuple = pycountry.languages.get(**{f"alpha_{len(code)}": code})
+    return language_tuple.name
+
+def print_available_tests():
+    pprint({ts: sacrebleu.get_langpairs_for_testset(ts) for ts in sacrebleu.get_available_testsets()})
+
+
+def main():
+    # print(sacrebleu.download_test_set("wmt14", "en-fr"))
+    # print_available_tests()
+    # sacrebleu.print_test_set("wmt14", "fr-en", "src")
+
+    # # Print number of benchmarks
+    # print(sum([
+    #     len(sacrebleu.get_langpairs_for_testset(ts))
+    #     for ts in sacrebleu.get_available_testsets()
+    # ]))
+
+    # Test task dictionary
+    # for task, task_class in create_tasks_from_benchmarks(selected_benchmarks).items():
+    #     print(task, task_class())
+    pass
+
+
+if __name__ == "__main__":
+    main()
+
+
+
+########################################
+# Don't mind me...!
+########################################
+
+# Available tests as of 2020/02/11
+"""
+{'iwslt17': ['en-fr',
+             'fr-en',
+             'en-de',
+             'de-en',
+             'en-zh',
+             'zh-en',
+             'en-ar',
+             'ar-en',
+             'en-ja',
+             'ja-en',
+             'en-ko',
+             'ko-en'],
+ 'iwslt17/dev2010': ['en-fr', 'fr-en', 'en-de', 'de-en', 'en-zh', 'zh-en'],
+ 'iwslt17/tst2010': ['en-fr', 'fr-en', 'en-de', 'de-en', 'en-zh', 'zh-en'],
+ 'iwslt17/tst2011': ['en-fr', 'fr-en', 'en-de', 'de-en', 'en-zh', 'zh-en'],
+ 'iwslt17/tst2012': ['en-fr', 'fr-en', 'en-de', 'de-en', 'en-zh', 'zh-en'],
+ 'iwslt17/tst2013': ['en-fr', 'fr-en', 'en-de', 'de-en', 'en-zh', 'zh-en'],
+ 'iwslt17/tst2014': ['en-fr', 'fr-en', 'en-de', 'de-en', 'en-zh', 'zh-en'],
+ 'iwslt17/tst2015': ['en-fr', 'fr-en', 'en-de', 'de-en', 'en-zh', 'zh-en'],
+ 'iwslt17/tst2016': ['en-fr', 'fr-en', 'en-de', 'de-en', 'en-zh', 'zh-en'],
+ 'mtnt1.1/test': ['en-fr', 'fr-en', 'en-ja', 'ja-en'],
+ 'mtnt1.1/train': ['en-fr', 'fr-en', 'en-ja', 'ja-en'],
+ 'mtnt1.1/valid': ['en-fr', 'fr-en', 'en-ja', 'ja-en'],
+ 'mtnt2019': ['en-fr', 'fr-en', 'en-ja', 'ja-en'],
+ 'multi30k/2016': ['en-fr', 'en-de', 'en-cs'],
+ 'multi30k/2017': ['en-fr', 'en-de'],
+ 'multi30k/2018': ['en-fr', 'en-de'],
+ 'wmt08': ['cs-en',
+           'en-cs',
+           'de-en',
+           'en-de',
+           'es-en',
+           'en-es',
+           'fr-en',
+           'en-fr',
+           'hu-en',
+           'en-hu'],
+ 'wmt08/europarl': ['de-en', 'en-de', 'es-en', 'en-es', 'fr-en', 'en-fr'],
+ 'wmt08/nc': ['cs-en', 'en-cs'],
+ 'wmt09': ['cs-en',
+           'en-cs',
+           'de-en',
+           'en-de',
+           'es-en',
+           'en-es',
+           'fr-en',
+           'en-fr',
+           'hu-en',
+           'en-hu',
+           'it-en',
+           'en-it'],
+ 'wmt10': ['cs-en',
+           'en-cs',
+           'de-en',
+           'en-de',
+           'es-en',
+           'en-es',
+           'fr-en',
+           'en-fr'],
+ 'wmt11': ['cs-en',
+           'en-cs',
+           'de-en',
+           'en-de',
+           'fr-en',
+           'en-fr',
+           'es-en',
+           'en-es'],
+ 'wmt12': ['cs-en',
+           'en-cs',
+           'de-en',
+           'en-de',
+           'es-en',
+           'en-es',
+           'fr-en',
+           'en-fr'],
+ 'wmt13': ['cs-en',
+           'en-cs',
+           'de-en',
+           'en-de',
+           'es-en',
+           'en-es',
+           'fr-en',
+           'en-fr',
+           'ru-en',
+           'en-ru'],
+ 'wmt14': ['cs-en',
+           'en-cs',
+           'de-en',
+           'en-de',
+           'en-fr',
+           'fr-en',
+           'en-hi',
+           'hi-en',
+           'en-ru',
+           'ru-en'],
+ 'wmt14/full': ['cs-en',
+                'en-cs',
+                'de-en',
+                'en-de',
+                'en-fr',
+                'fr-en',
+                'en-hi',
+                'hi-en',
+                'en-ru',
+                'ru-en'],
+ 'wmt15': ['en-fr',
+           'fr-en',
+           'cs-en',
+           'de-en',
+           'en-cs',
+           'en-de',
+           'en-fi',
+           'en-ru',
+           'fi-en',
+           'ru-en'],
+ 'wmt16': ['cs-en',
+           'de-en',
+           'en-cs',
+           'en-de',
+           'en-fi',
+           'en-ro',
+           'en-ru',
+           'en-tr',
+           'fi-en',
+           'ro-en',
+           'ru-en',
+           'tr-en'],
+ 'wmt16/B': ['en-fi'],
+ 'wmt16/dev': ['en-ro', 'en-tr', 'ro-en', 'tr-en'],
+ 'wmt16/tworefs': ['en-fi'],
+ 'wmt17': ['cs-en',
+           'de-en',
+           'en-cs',
+           'en-de',
+           'en-fi',
+           'en-lv',
+           'en-ru',
+           'en-tr',
+           'en-zh',
+           'fi-en',
+           'lv-en',
+           'ru-en',
+           'tr-en',
+           'zh-en'],
+ 'wmt17/B': ['en-fi'],
+ 'wmt17/dev': ['en-lv', 'en-zh', 'lv-en', 'zh-en'],
+ 'wmt17/improved': ['en-zh', 'zh-en'],
+ 'wmt17/ms': ['zh-en'],
+ 'wmt17/tworefs': ['en-fi'],
+ 'wmt18': ['cs-en',
+           'de-en',
+           'en-cs',
+           'en-de',
+           'en-et',
+           'en-fi',
+           'en-ru',
+           'et-en',
+           'fi-en',
+           'ru-en',
+           'en-tr',
+           'tr-en',
+           'en-zh',
+           'zh-en'],
+ 'wmt18/dev': ['et-en', 'en-et'],
+ 'wmt18/test-ts': ['cs-en',
+                   'de-en',
+                   'en-cs',
+                   'en-de',
+                   'en-et',
+                   'en-fi',
+                   'en-ru',
+                   'et-en',
+                   'fi-en',
+                   'ru-en',
+                   'en-tr',
+                   'tr-en',
+                   'en-zh',
+                   'zh-en'],
+ 'wmt19': ['cs-de',
+           'de-cs',
+           'de-en',
+           'de-fr',
+           'en-cs',
+           'en-de',
+           'en-fi',
+           'en-gu',
+           'en-kk',
+           'en-lt',
+           'en-ru',
+           'en-zh',
+           'fi-en',
+           'fr-de',
+           'gu-en',
+           'kk-en',
+           'lt-en',
+           'ru-en',
+           'zh-en'],
+ 'wmt19/dev': ['lt-en', 'en-lt', 'gu-en', 'en-gu', 'kk-en', 'en-kk'],
+ 'wmt19/google/ar': ['en-de'],
+ 'wmt19/google/arp': ['en-de'],
+ 'wmt19/google/hqall': ['en-de'],
+ 'wmt19/google/hqp': ['en-de'],
+ 'wmt19/google/hqr': ['en-de'],
+ 'wmt19/google/wmtp': ['en-de'],
+ 'wmt20': ['cs-en',
+           'de-en',
+           'de-fr',
+           'en-cs',
+           'en-de',
+           'en-iu',
+           'en-ja',
+           'en-km',
+           'en-pl',
+           'en-ps',
+           'en-ru',
+           'en-ta',
+           'en-zh',
+           'fr-de',
+           'iu-en',
+           'ja-en',
+           'km-en',
+           'pl-en',
+           'ps-en',
+           'ru-en',
+           'ta-en',
+           'zh-en'],
+ 'wmt20/dev': ['iu-en',
+               'en-iu',
+               'ja-en',
+               'en-ja',
+               'pl-en',
+               'en-pl',
+               'ta-en',
+               'en-ta'],
+ 'wmt20/robust/set1': ['en-ja', 'en-de'],
+ 'wmt20/robust/set2': ['en-ja', 'ja-en'],
+ 'wmt20/robust/set3': ['de-en'],
+ 'wmt20/tworefs': ['de-en', 'en-de', 'en-zh', 'ru-en', 'zh-en']}
+"""
\ No newline at end of file

lm_eval/tasks/triviaqa.py

 import os
 import json
 import random
-from lm_eval.base import Task, mean, rf
+from lm_eval.base import Task, rf
+from ..metrics import mean
 from ..utils import sh
 
 class TriviaQA(Task):

lm_eval/tasks/webqs.py

 from . common import HFTask
-from lm_eval.base import mean, rf
+from lm_eval.base import rf
+from ..metrics import mean
+
 
 class WebQs(HFTask):
     DATASET_PATH = "web_questions"