Merge branch 'master' into webqs

.github/workflows/python-app.yml

+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Python application
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Cache
+      uses: actions/cache@v2.1.3
+      with:
+        # A list of files, directories, and wildcard patterns to cache and restore
+        path: |
+          data
+          ~/.cache
+        # An explicit key for restoring and saving the cache
+        key: evaldata-cache
+    - name: Set up Python 3.9
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.9
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install flake8 pytest pytest-cov
+        pip install -e .
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pytest --cov=lm_eval/ tests/
+    - name: Upload to codecov
+      run: |
+        bash <(curl -s https://codecov.io/bash)
\ No newline at end of file

README.md

 # Evaluation Harness for Large Language Models
 
+![](https://github.com/EleutherAI/lm-evaluation-harness/workflows/Python%20application/badge.svg)
+[![codecov](https://codecov.io/gh/EleutherAI/lm-evaluation-harness/branch/master/graph/badge.svg?token=JSG3O2427J)](https://codecov.io/gh/EleutherAI/lm-evaluation-harness)
+
 ## Overview 
 
 The goal of this project is to build a set of tools for evaluating LMs on typical NLU tasks, based on evaluation of GPT-3 as described in https://arxiv.org/pdf/2005.14165.pdf. Following the initial description, this repo should support 3 functions:

lm_eval/base.py

@@ -58,10 +58,10 @@ class LM(abc.ABC):
         return cls()
 
 
-class Dataset(abc.ABC):
+class Task(abc.ABC):
     def __init__(self):
         self.download()
-        self._traindocs = None
+        self._training_docs = None
 
     def download(self):
         """Downloads the task dataset if necessary"""
@@ -84,23 +84,29 @@ class Dataset(abc.ABC):
 
     def training_docs(self):
         """
-
         :return: Iterable[obj]
             A iterable of any object, that doc_to_text can handle
         """
         return []
 
     def validation_docs(self):
+        """
+        :return: Iterable[obj]
+            A iterable of any object, that doc_to_text can handle
+        """
         return []
 
     def test_docs(self):
+        """
+        :return: Iterable[obj]
+            A iterable of any object, that doc_to_text can handle
+        """
         return []
 
     def fewshot_examples(self, k):
-        if self._traindocs is None:
-            self._traindocs = list(self.training_docs())
-
-        return random.sample(self._traindocs, k)
+        if self._training_docs is None:
+            self._training_docs = list(self.training_docs())
+        return random.sample(self._training_docs, k)
 
     @abc.abstractmethod
     def doc_to_text(self, doc):

lm_eval/tasks/__init__.py

@@ -46,7 +46,7 @@ TASK_REGISTRY = {
     "lambada": lambada.LAMBADA,
     "piqa": piqa.PiQA,
 
-    "triviaqa": triviaqa.TriviaQA,
+    #"triviaqa": triviaqa.TriviaQA,
     # "arc_easy": arc.ARCEasy, # not implemented yet
     # "arc_challenge": arc.ARCChallenge, # not implemented yet
     # "quac": quac.QuAC, # not implemented yet

lm_eval/tasks/arithmetic.py

@@ -2,12 +2,12 @@ import abc
 import json
 import os
 from collections import namedtuple
-from lm_eval.base import Dataset, mean, rf
+from lm_eval.base import Task, mean, rf
 from best_download import download_file
 
 ArithmeticDoc = namedtuple('ArithmeticDoc', ['context', 'completion'])
 
-class Arithmetic(Dataset):
+class Arithmetic(Task):
     directory = 'data/arithmetic/'
 
     def __init__(self):

lm_eval/tasks/common.py

 import datasets
 import numpy as np
-import random
-from ..base import Dataset
+from ..base import Task
 
 
-class HFTask(Dataset):
+class HFTask(Task):
     DATASET_PATH = None
     DATASET_NAME = None
 
     def __init__(self):
+        self.data = None
         super().__init__()
-        self._training_docs = None
 
     def download(self):
         self.data = datasets.load_dataset(path=self.DATASET_PATH, name=self.DATASET_NAME)

lm_eval/tasks/coqa.py

@@ -2,11 +2,11 @@
 
 import json
 import random
-from lm_eval.base import Dataset
+from lm_eval.base import Task
 from ..utils import sh
 
 
-class CoQA(Dataset):
+class CoQA(Task):
     def __init__(self):
         self.download()
     def download(self):

lm_eval/tasks/drop.py

@@ -5,9 +5,9 @@ from sklearn.metrics import f1_score, matthews_corrcoef
 from tqdm import auto as tqdm_lib
 from . common import HFTask, simple_accuracy_metric, yesno
 from pathlib import Path
-from ..base import Dataset
+from ..base import Task
 
-class DROP(Dataset):
+class DROP(Task):
     DATAFOLDER = Path(__file__).parent / "../../data/drop"
     
     def __init__(self):

lm_eval/tasks/lambada.py

-from lm_eval.base import Dataset, rf, mean
+from lm_eval.base import Task, rf, mean
 from lm_eval.utils import sh
 import json
 import math
 from best_download import download_file
 
 
-class LAMBADA(Dataset):
+class LAMBADA(Task):
     def download(self):
         sh("mkdir -p data/lambada")
         download_file(

lm_eval/tasks/naturalqs.py

 from . common import HFTask
 from itertools import islice
+import random
 
 class NaturalQs(HFTask):
+    # TODO: naturalqs has a *really* large train set that huggingface just
+    # automatically downloads even if you dont use it. we should try and only 
+    # download the val set and not even bother with the train set. 
+
     DATASET_PATH = "natural_questions"
     DATASET_NAME = None
 
@@ -25,10 +30,10 @@ class NaturalQs(HFTask):
 
     def fewshot_examples(self, k):
         # Data is too large to fit in memory. We just sample from the first bit.
-        if self._traindocs is None:
-            self._traindocs = list(islice(self.training_docs(), 0, 100000))
+        if self._training_docs is None:
+            self._training_docs = list(islice(self.training_docs(), 0, 100000))
 
-        return random.sample(self._traindocs, k)
+        return random.sample(self._training_docs, k)
 
     def doc_to_text(self, doc):
         return 'Q: ' + doc['question']['text'] + '\n\n' + 'A: '

lm_eval/tasks/piqa.py

 import json
 import random
-from lm_eval.base import Dataset, rf, mean
+from lm_eval.base import Task, rf, mean
 from ..utils import sh
 import os
 
-class PiQA(Dataset):
+class PiQA(Task):
     def download(self):
         if not os.path.exists('data/piqa'):
             #TODO: use best_download

lm_eval/tasks/quac.py

 import json
 import random
 import os
-from lm_eval.base import Dataset
+from lm_eval.base import Task
 from ..utils import sh
 
 
-class QuAC(Dataset):    
+class QuAC(Task):    
     def __init__(self):
         super().__init__()
 

lm_eval/tasks/race.py

@@ -23,7 +23,8 @@ class RACE(HFTask):
         return True
 
     def _collate_data(self, set):
-        if set in self.cache: return self.cache[set]
+        if set in self.cache:
+            return self.cache[set]
         # One big issue with HF's implementation of this dataset: it makes a
         # separate document for each question; meanwhile, in the GPT3 paper it
         # is shown that one document is made per passage.

lm_eval/tasks/sat.py

 import json
 import random
 import os
-from lm_eval.base import Dataset, rf, mean
+from lm_eval.base import Task, rf, mean
 from tqdm import auto as tqdm_lib
 from . common import simple_accuracy_metric
 import numpy as np
 from ..utils import sh
 
 
-class SATAnalogies(Dataset):    
+class SATAnalogies(Task):    
     NEEDS_MANUAL_DL = True
     
     def __init__(self):

lm_eval/tasks/storycloze.py

 import json
 import random
-from lm_eval.base import Dataset
+from lm_eval.base import Task
 from ..utils import sh
 import csv
 
-class StoryCloze(Dataset):
+class StoryCloze(Task):
     NEEDS_MANUAL_DL = True
 
     def download(self):