Merge branch 'big-refactor' into big-refactor

lm_eval/models/gpt3.py → lm_eval/models/openai_completions.py

@@ -6,7 +6,8 @@ import numpy as np
 
 from tqdm import tqdm
 from lm_eval import utils
-from lm_eval.api.model import LM, register_model
+from lm_eval.api.model import LM
+from lm_eval.api.registry import register_model
 
 
 def get_result(response, ctxlen):
@@ -56,7 +57,7 @@ def oa_completion(**kwargs):
             backoff_time *= 1.5
 
 
-@register_model("openai")
+@register_model("openai", "gooseai")
 class GPT3LM(LM):
     REQ_CHUNK_SIZE = 20
 

lm_eval/models/textsynth.py

@@ -16,7 +16,8 @@ import os
 import requests as _requests
 import time
 from tqdm import tqdm
-from lm_eval.api.model import LM, register_model
+from lm_eval.api.model import LM
+from lm_eval.api.registry import register_model
 
 
 logger = logging.getLogger(__name__)

lm_eval/tasks/CHECKLIST.md

-# v1.0 Tasks
-This list keeps track of which tasks' implementations have been ported to YAML / v2.0 of the Eval Harness.
-
-Boxes should be checked iff tasks are implemented in v2.0 and tested for regression. Tasks should be struck through if checked *against original introducing paper* implementation or popularizing implementation.
-
-- [ ] Glue
-- [ ] SuperGlue
-- [ ] CoQA
-- [ ] DROP
-- [x] ~~Lambada~~
-- [x] Lambada (Cloze variants)
-- [ ] Lambada (Multilingual)
-- [x] Wikitext
-- [x] PiQA
-- [ ] PROST
-- [ ] MCTACO
-- [ ] Pubmed QA
-- [x] SciQ
-- [ ] QASPER
-- [ ] QA4MRE
-- [ ] TriviaQA
-- [x] AI2 ARC
-- [ ] LogiQA
-- [ ] HellaSwag
-- [ ] SWAG
-- [ ] OpenBookQA
-- [ ] SQuADv2
-- [ ] RACE
-- [ ] HeadQA
-- [ ] MathQA
-- [ ] WebQs
-- [ ] WSC273
-- [ ] Winogrande
-- [ ] ANLI
-- [ ] Hendrycks Ethics
-- [ ] TruthfulQA
-- [ ] MuTual
-- [ ] Hendrycks Math
-- [ ] Asdiv
-- [ ] GSM8k
-- [ ] Arithmetic
-- [ ] MMMLU
-- [ ] Translation (WMT) suite
-- [ ] Unscramble
-- [x] ~~Pile (perplexity)~~
-- [ ] BLiMP
-- [ ] ToxiGen
-- [ ] CrowS-Pairs
-- [ ] XCopa
-- [ ] BIG-Bench
-- [ ] XStoryCloze
-- [ ] XWinograd
-- [ ] PAWS-X
-- [ ] XNLI
-- [ ] MGSM
-
-# Novel Tasks
-Tasks added in the revamped harness that were not previously available. Again, a strikethrough denotes checking performed *against the original task's implementation or published results introducing the task*.
-
-# Task Wishlist
-
-- [ ] TheoremQA
-- [ ] Theorem Proving evaluations
-- [ ] Chain of Thought
-- [ ] Self-consistency ; Least-to-Most prompting, etc.
-- [ ] Summarization Tasks
-- [ ] Anthropic Model-Written Evals 
\ No newline at end of file

lm_eval/tasks/README.md

@@ -64,4 +64,4 @@ Tasks added in the revamped harness that were not previously available. Again, a
 - [ ] Chain of Thought
 - [ ] Self-consistency ; Least-to-Most prompting, etc.
 - [ ] Summarization Tasks
-- [ ] Anthropic Model-Written Evals 
+- [ ] Anthropic Model-Written Evals
\ No newline at end of file

lm_eval/tasks/__init__.py

 import os
 from typing import List, Union
 
+from .gsm8k import *
+from .triviaqa import *
 
 from lm_eval import utils
 from lm_eval.logger import eval_logger
 from lm_eval.api.task import TaskConfig, Task, ConfigurableTask
-from lm_eval.api.register import (
+from lm_eval.api.registry import (
     register_task,
     register_group,
-    task_registry,
-    group_registry,
+    TASK_REGISTRY,
+    GROUP_REGISTRY,
 )
 
 
 def get_task_name_from_config(task_config):
-    return "configurable_{dataset_path}_{dataset_name}".format(**task_config)
+    return "{dataset_path}_{dataset_name}".format(**task_config)
+
+
+def include_task_folder(task_dir):
+    """
+    Calling this function
+    """
+    for root, subdirs, file_list in os.walk(task_dir):
+        if (subdirs == []) and (len(file_list) > 0):
+            for f in file_list:
+                if f.endswith(".yaml"):
+                    yaml_path = os.path.join(root, f)
+                    try:
+                        config = utils.load_yaml_config(yaml_path)
+
+                        SubClass = type(
+                            config["task"] + "ConfigurableTask",
+                            (ConfigurableTask,),
+                            {"CONFIG": TaskConfig(**config)},
+                        )
+
+                        if "task" in config:
+                            task_name = "{}".format(config["task"])
+                            register_task(task_name)(SubClass)
+
+                        if "group" in config:
+                            for group in config["group"]:
+                                register_group(group)(SubClass)
+                    except Exception as error:
+                        eval_logger.warning(
+                            "Failed to load config in\n"
+                            f"                                 {yaml_path}\n"
+                            "                                 Config will not be added to registry\n"
+                            f"                                 Error: {error}"
+                        )
 
 
 task_dir = os.path.dirname(os.path.abspath(__file__)) + "/"
-for root, subdirs, file_list in os.walk(task_dir):
-    if (subdirs == []) and (len(file_list) > 0):
-        for file in file_list:
-            if "yaml" in file:
-                yaml_path = os.path.join(root, file)
-                try:
-                    config = utils.load_yaml_config(yaml_path)
-
-                    SubClass = type(
-                        config["task"] + "ConfigurableTask",
-                        (ConfigurableTask,),
-                        {"CONFIG": TaskConfig(**config)},
-                    )
-
-                    if "task" in config:
-                        task_name = "{}:{}".format(
-                            get_task_name_from_config(config), config["task"]
-                        )
-                        register_task(task_name)(SubClass)
-
-                    if "group" in config:
-                        for group in config["group"]:
-                            register_group(group)(SubClass)
-                except Exception:
-                    eval_logger.warning(
-                        "Failed to load config at in\n"
-                        f"                                 {yaml_path}\n"
-                        "                                 Config will not be added to registry"
-                    )
-
-TASK_REGISTRY = task_registry
-GROUP_REGISTRY = group_registry
-ALL_TASKS = sorted(list(TASK_REGISTRY.keys()) + list(GROUP_REGISTRY.keys()))
+include_task_folder(task_dir)
 
 
 def get_task(task_name, config):

lm_eval/tasks/arc.py

-"""
-Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
-https://arxiv.org/pdf/1803.05457.pdf
-
-The ARC dataset consists of 7,787 science exam questions drawn from a variety
-of sources, including science questions provided under license by a research
-partner affiliated with AI2. These are text-only, English language exam questions
-that span several grade levels as indicated in the files. Each question has a
-multiple choice structure (typically 4 answer options). The questions are sorted
-into a Challenge Set of 2,590 “hard” questions (those that both a retrieval and
-a co-occurrence method fail to answer correctly) and an Easy Set of 5,197 questions.
-
-Homepage: https://allenai.org/data/arc
-"""
-from lm_eval import utils
-from lm_eval.prompts import get_prompt
-from lm_eval.api.task import MultipleChoiceTask
-
-from lm_eval.api.register import register_task, register_group
-
-_CITATION = """
-@article{Clark2018ThinkYH,
-  title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
-  author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
-  journal={ArXiv},
-  year={2018},
-  volume={abs/1803.05457}
-}
-"""
-
-
-@register_group("arc")
-@register_task("arc_easy")
-class ARCEasy(MultipleChoiceTask):
-    VERSION = "2.0"
-    DATASET_PATH = "ai2_arc"
-    DATASET_NAME = "ARC-Easy"
-
-    OUTPUT_TYPE = "loglikelihood"
-
-    def has_training_docs(self):
-        return True
-
-    def has_validation_docs(self):
-        return True
-
-    def has_test_docs(self):
-        return True
-
-    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):
-        # NOTE: Some `doc["answerKey"]`s are in numeric string format being one
-        # of {'1', '2', '3', '4', '5'}. We map them back to letters.
-        num_to_letter = {"1": "A", "2": "B", "3": "C", "4": "D", "5": "E"}
-        doc["answerKey"] = num_to_letter.get(doc["answerKey"], doc["answerKey"])
-        out_doc = {
-            "id": doc["id"],
-            "question": doc["question"],
-            "choices": doc["choices"]["text"],
-            "gold": ["A", "B", "C", "D", "E"].index(doc["answerKey"]),
-        }
-        return out_doc
-
-    def doc_to_text(self, doc):
-        doc_to_text = get_prompt("qa-basic:question-newline-answer")
-        return utils.apply_template(doc_to_text, doc)
-
-    def should_decontaminate(self):
-        return True
-
-    def doc_to_decontamination_query(self, doc):
-        return doc["query"]
-
-
-@register_group("arc")
-@register_task("arc_challenge")
-class ARCChallenge(ARCEasy):
-    DATASET_PATH = "ai2_arc"
-    DATASET_NAME = "ARC-Challenge"

lm_eval/tasks/arc/arc_challenge.yaml

 group:
-  - arc_yaml
-task: arc_challenge_yaml
+  - ai2_arc
+  - multiple_choice
+task: arc_challenge
 dataset_path: ai2_arc
 dataset_name: ARC-Challenge
 output_type: multiple_choice

lm_eval/tasks/arc/arc_easy.yaml

 group:
-  - arc_yaml
-task: arc_easy_yaml
+  - ai2_arc
+  - multiple_choice
+task: arc_easy
 dataset_path: ai2_arc
 dataset_name: ARC-Easy
 output_type: multiple_choice

lm_eval/tasks/gsm8k.py

@@ -24,7 +24,7 @@ from lm_eval.api.instance import Instance
 
 from lm_eval.prompts import get_prompt
 
-from lm_eval.api.register import register_task, register_group
+from lm_eval.api.registry import register_task, register_group
 
 _CITATION = """
 @misc{cobbe2021training,
@@ -92,7 +92,7 @@ class GradeSchoolMath8K(Task):
         return Instance(
             request_type=self.OUTPUT_TYPE,
             doc=doc,
-            arguments=(ctx, ["\n"]),
+            arguments=(ctx, ["\n\n"]),
             idx=0,
             **kwargs
         )
@@ -113,7 +113,7 @@ class GradeSchoolMath8K(Task):
         assert gold != INVALID_ANS, "No ground truth answer found in the document."
         # return self._extract_answer(completion) == gold
         # print(completion)
-        return completion == gold
+        return self._extract_answer(completion) == gold
 
     def process_results(self, doc, results):
         """Take a single document and the LM results and evaluates, returning a

lm_eval/tasks/gsm8k/README.md

+# GSM8k
+
+## Paper
+Training Verifiers to Solve Math Word Problems
+https://arxiv.org/abs/2110.14168
+
+State-of-the-art language models can match human performance on many tasks, but
+they still struggle to robustly perform multi-step mathematical reasoning. To
+diagnose the failures of current models and support research, we introduce GSM8K,
+a dataset of 8.5K high quality linguistically diverse grade school math word problems.
+We find that even the largest transformer models fail to achieve high test performance,
+despite the conceptual simplicity of this problem distribution.
+
+NOTE: See the official implementation of the task:
+    https://github.com/openai/grade-school-math/blob/master/grade_school_math/calculator.py
+for how to make use of the dataset's calculator annotations in your language
+model's sample/generation function.
+
+Homepage: https://github.com/openai/grade-school-math
+
+
+## Citation
+```
+@misc{cobbe2021training,
+      title={Training Verifiers to Solve Math Word Problems},
+      author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},
+      year={2021},
+      eprint={2110.14168},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```
+
+
+### Checklist
+
+- [x] Is in Eval-harness v1.0 ?
+- [ ] Has been checked for regression from v1.0?
+- [ ] Has been checked for equivalence with original paper methodology?
+- [ ] "Main" checked variant clearly denoted?
+
+### Variant Wishlist
+
+- [ ] Variant with Calculator (see https://github.com/openai/grade-school-math/blob/master/grade_school_math/calculator.py for example implementation)
+- [ ] Using Verifiers
+- [ ] Majority voting "without CoT"
\ No newline at end of file

lm_eval/tasks/gsm8k/cot-gsm8k.yaml

-# "Training Verifiers to Solve Math Word Problems"
-# https://arxiv.org/abs/2110.14168
-
-# State-of-the-art language models can match human performance on many tasks, but
-# they still struggle to robustly perform multi-step mathematical reasoning. To
-# diagnose the failures of current models and support research, we introduce GSM8K,
-# a dataset of 8.5K high quality linguistically diverse grade school math word problems.
-# We find that even the largest transformer models fail to achieve high test performance,
-# despite the conceptual simplicity of this problem distribution.
-
-# NOTE: See the official implementation of the task:
-#     https://github.com/openai/grade-school-math/blob/master/grade_school_math/calculator.py
-# for how to make use of the dataset's calculator annotations in your language
-# model's sample/generation function.
-
-# Homepage: https://github.com/openai/grade-school-math
-
-# _CITATION = """
-# @misc{cobbe2021training,
-#       title={Training Verifiers to Solve Math Word Problems},
-#       author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},
-#       year={2021},
-#       eprint={2110.14168},
-#       archivePrefix={arXiv},
-#       primaryClass={cs.LG}
-# }
-# """
-
-task: gsm8k_yaml
-dataset_path: gsm8k
-dataset_name: main
-training_split: train
-test_split: test
-use_prompt: "qa-basic:question-newline-answer"
-doc_to_target: "{{answer.split('### ')[-1]}}"
-metric_list:
-  - metric: exact_match
-    aggregation: mean
-    higher_is_better: true
-    ignore_case: true
-    ignore_punctuation: true
-delimiter: "\n"
-repeats: 4
-# filter_list:
-#   - name: "get-answer"
-#     filter:
-#       - function: "regex"
-#         regex_pattern: "#### (\-?[0-9\.\,]+)"

lm_eval/tasks/gsm8k/gsm8k-cot-self-consistency.yaml

+include: gsm8k-cot.yaml
+group:
+  - chain_of_thought
+  - self_consistency
+task: gsm8k_cot_self_consistency
+generation_kwargs:
+  until:
+    - "Q:"
+    - "\n\n"
+  do_sample: true
+  temperature: 0.2
+repeats: 64
+filter_list:
+  - name: "score-first" # pick only the first response, and report metrics on that
+    filter:
+      - function: "regex"
+        regex_pattern: "The answer is (\\-?[0-9\\.\\,]*[0-9]+)"
+      - function: "take_first"
+  - name: "maj@64"
+    filter:
+      - function: "regex"
+        regex_pattern: "The answer is (\\-?[0-9\\.\\,]*[0-9]+)"
+      - function: "majority_vote"
+      - function: "take_first"
+  - name: "maj@8" # get Maj@8 , via selecting the first 8 responses. Using a better estimator would be optimal.
+    filter:
+      - function: "take_first_k"
+        k: 8
+      - function: "regex"
+        regex_pattern: "The answer is (\\-?[0-9\\.\\,]*[0-9]+)"
+      - function: "majority_vote"
+      - function: "take_first"

lm_eval/tasks/gsm8k/gsm8k-cot.yaml

+group:
+  - chain_of_thought
+task: gsm8k_cot
+dataset_path: gsm8k
+dataset_name: main
+output_type: greedy_until
+test_split: test
+doc_to_text: "Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?\n\nA: There are 15 trees originally. Then there were 21 trees after some more were planted. So there must have been 21 - 15 = 6. The answer is 6.\n\n\
+Q: If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?\n\nA: There are originally 3 cars. 2 more cars arrive. 3 + 2 = 5. The answer is 5.\n\n\
+Q: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?\n\nA: Originally, Leah had 32 chocolates. Her sister had 42. So in total they had 32 + 42 = 74. After eating 35, they had 74 - 35 = 39. The answer is 39.\n\n\
+Q: Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?\n\nA: Jason started with 20 lollipops. Then he had 12 after giving some to Denny. So he gave Denny 20 - 12 = 8. The answer is 8.\n\n\
+Q: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?\n\nA: Shawn started with 5 toys. If he got 2 toys each from his mom and dad, then that is 4 more toys. 5 + 4 = 9. The answer is 9.\n\n\
+Q: There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?\n\nA: There were originally 9 computers. For each of 4 days, 5 more computers were added. So 5 * 4 = 20 computers were added. 9 + 20 is 29. The answer is 29.\n\n\
+Q: Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?\n\nA: Michael started with 58 golf balls. After losing 23 on tuesday, he had 58 - 23 = 35. After losing 2 more, he had 35 - 2 = 33 golf balls. The answer is 33.\n\n\
+Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?\n\nA: Olivia had 23 dollars. 5 bagels for 3 dollars each will be 5 x 3 = 15 dollars. So she has 23 - 15 dollars left. 23 - 15 is 8. The answer is 8.\n\n\
+Q: {{question}}\n\nA:"
+doc_to_target: "{{answer}}" #" {{answer.split('### ')[-1].rstrip()}}"
+gold_alias: "{{answer.split('### ')[-1].rstrip()}}" # this post-processes the reference that we'll score against
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: false
+    regexes_to_ignore:
+      - ","
+      - "\\$"
+delimiter: "\n\n"
+generation_kwargs:
+  until:
+    - "Q:"
+    - "\n\n"
+  do_sample: false
+  temperature: 0.0
+repeats: 1
+num_fewshot: 0
+filter_list:
+  - name: "get-answer"
+    filter:
+      - function: "regex"
+        regex_pattern: "The answer is (\\-?[0-9\\.\\,]+)"
+      - function: "take_first"

lm_eval/tasks/gsm8k/gsm8k.yaml

+group:
+  - greedy_until
+  - math_word_problems
+task: gsm8k_yaml
+dataset_path: gsm8k
+dataset_name: main
+output_type: greedy_until
+training_split: train
+fewshot_split: train
+test_split: test
+doc_to_text: "Question: {{question}}\nAnswer:"
+doc_to_target: "{{answer}}" #" {{answer.split('### ')[-1].rstrip()}}"
+gold_alias: "{{answer.split('### ')[-1].rstrip()}}" # this post-processes the reference that we'll score against
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: false
+    regexes_to_ignore:
+      - ","
+      - "\\$"
+      - ".*### "
+delimiter: "\n\n"
+generation_kwargs:
+  until:
+    - "\n\n"
+    - "Question:"
+  do_sample: false
+  temperature: 0.0
+repeats: 1
+num_fewshot: 5
+# filter_list:
+#   - name: "get-answer"
+#     filter:
+#       - function: "regex"
+#         regex_pattern: "### (\\-?[0-9\\.\\,]+)"
+#       - function: "take_first"

lm_eval/tasks/lambada.py

-"""
-The LAMBADA dataset: Word prediction requiring a broad discourse context∗
-https://arxiv.org/pdf/1606.06031.pdf
-
-LAMBADA is a dataset to evaluate the capabilities of computational models for text
-understanding by means of a word prediction task. LAMBADA is a collection of narrative
-passages sharing the characteristic that human subjects are able to guess their last
-word if they are exposed to the whole passage, but not if they only see the last
-sentence preceding the target word. To succeed on LAMBADA, computational models
-cannot simply rely on local context, but must be able to keep track of information
-in the broader discourse.
-
-Homepage: https://zenodo.org/record/2630551#.X4Xzn5NKjUI
-"""
-from lm_eval.api.task import Task
-from lm_eval.api.instance import Instance
-from lm_eval.api.metrics import mean, perplexity
-
-from lm_eval.api.register import register_task, register_group
-
-_CITATION = """
-@misc{
-    author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel},
-    title={The LAMBADA dataset},
-    DOI={10.5281/zenodo.2630551},
-    publisher={Zenodo},
-    year={2016},
-    month={Aug}
-}
-"""
-
-
-class LambadaBase(Task):
-    VERSION = None
-
-    OUTPUT_TYPE = "loglikelihood"
-
-    def training_docs(self):
-        if self.has_training_docs():
-            return self.dataset["train"]
-
-    def validation_docs(self):
-        if self.has_validation_docs():
-            return self.dataset["validation"]
-
-    def test_docs(self):
-        if self.has_test_docs():
-            return self.dataset["test"]
-
-    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]
-
-    def construct_requests(self, doc, ctx, **kwargs):
-        return Instance(
-            request_type=self.OUTPUT_TYPE,
-            doc=doc,
-            arguments=(ctx, self.doc_to_target(doc)),
-            **kwargs
-        )
-
-    def process_results(self, doc, results):
-        # TODO: this ^ is a hack. filters should make it so that we only have one response per request that we score
-        results = results[
-            0
-        ]  # TODO: recheck this. currently a list of [(ll, is_greedy)] is passed in
-        ll, is_greedy = results
-
-        return {"ppl": ll, "acc": int(is_greedy)}
-
-    def aggregation(self):
-        return {"ppl": perplexity, "acc": mean}
-
-    def higher_is_better(self):
-        return {"ppl": False, "acc": True}
-
-
-@register_task("lambada_standard")
-class LambadaStandard(LambadaBase):
-    """The LAMBADA task using the standard original LAMBADA dataset."""
-
-    VERSION = "2.0"
-    DATASET_PATH = "lambada"
-
-    def has_training_docs(self):
-        return False
-
-    def has_validation_docs(self):
-        return True
-
-    def has_test_docs(self):
-        return True
-
-
-@register_task("lambada_openai")
-class LambadaOpenAI(LambadaBase):
-    """The LAMBADA task using the LAMBADA OpenAI dataset, a modified version of the
-    original LAMBADA dataset created by OpenAI for evaluating their GPT-2 model.
-
-    Reference: https://github.com/openai/gpt-2/issues/131#issuecomment-497136199
-    """
-
-    VERSION = "2.0"
-    DATASET_PATH = "EleutherAI/lambada_openai"
-
-    def has_training_docs(self):
-        return False
-
-    def has_validation_docs(self):
-        return False
-
-    def has_test_docs(self):
-        return True

lm_eval/tasks/lambada/README.md

 # LAMBADA
 
 ### Paper
-The LAMBADA dataset: Word prediction requiring a broad discourse context 
+The LAMBADA dataset: Word prediction requiring a broad discourse context
 https://arxiv.org/pdf/1606.06031.pdf
 
 LAMBADA is a dataset to evaluate the capabilities of computational models for text
@@ -23,4 +23,4 @@ Homepage: https://zenodo.org/record/2630551#.X4Xzn5NKjUI
     publisher={Zenodo},
     year={2016},
     month={Aug}
-}
+}
\ No newline at end of file

lm_eval/tasks/lambada/lambada_openai.yaml

 group:
   - lambada
-task: lambada_openai_yaml
+  - loglikelihood
+  - perplexity
+task: lambada_openai
 dataset_path: EleutherAI/lambada_openai
 dataset_name: default
 output_type: loglikelihood

lm_eval/tasks/lambada/lambada_standard.yaml

 group:
   - lambada
-task: lambada_standard_yaml
+  - loglikelihood
+  - perplexity
+task: lambada_standard
 dataset_path: lambada
 dataset_name: null
 output_type: loglikelihood

lm_eval/tasks/lambada_cloze/lambada_openai_cloze.yaml

 group:
   - lambada_cloze
+  - loglikelihood
 task: lambada_openai_cloze_yaml
 dataset_path: EleutherAI/lambada_openai
 dataset_name: default

lm_eval/tasks/lambada_cloze/lambada_standard_cloze.yaml

 group:
   - lambada_cloze
+  - loglikelihood
 task: lambada_standard_cloze_yaml
 dataset_path: lambada
 dataset_name: null