conflict solved

lm_eval/datasets/coqa/coqa.py

@@ -61,7 +61,7 @@ _EMPTY_ADDITIONAL_ANSWER = {
             "span_end": -1,
             "span_text": "",
             "input_text": "",
-            "turn_id": -1
+            "turn_id": -1,
         }
     ],
     "1": [
@@ -70,7 +70,7 @@ _EMPTY_ADDITIONAL_ANSWER = {
             "span_end": -1,
             "span_text": "",
             "input_text": "",
-            "turn_id": -1
+            "turn_id": -1,
         }
     ],
     "2": [
@@ -79,7 +79,7 @@ _EMPTY_ADDITIONAL_ANSWER = {
             "span_end": -1,
             "span_text": "",
             "input_text": "",
-            "turn_id": -1
+            "turn_id": -1,
         }
     ],
 }
@@ -91,8 +91,9 @@ class Coqa(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("0.0.1")
 
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="coqa", version=VERSION,
-                               description="The CoQA dataset."),
+        datasets.BuilderConfig(
+            name="coqa", version=VERSION, description="The CoQA dataset."
+        ),
     ]
 
     def _info(self):
@@ -101,41 +102,52 @@ class Coqa(datasets.GeneratorBasedBuilder):
                 "id": datasets.Value("string"),
                 "source": datasets.Value("string"),
                 "story": datasets.Value("string"),
-                "questions": datasets.features.Sequence({
+                "questions": datasets.features.Sequence(
+                    {
                         "input_text": datasets.Value("string"),
                         "turn_id": datasets.Value("int32"),
-                }),
-                "answers": datasets.features.Sequence({
+                    }
+                ),
+                "answers": datasets.features.Sequence(
+                    {
                         "span_start": datasets.Value("int32"),
                         "span_end": datasets.Value("int32"),
                         "span_text": datasets.Value("string"),
                         "input_text": datasets.Value("string"),
                         "turn_id": datasets.Value("int32"),
-                }),
+                    }
+                ),
                 "additional_answers": {
-                    "0": datasets.features.Sequence({
+                    "0": datasets.features.Sequence(
+                        {
                             "span_start": datasets.Value("int32"),
                             "span_end": datasets.Value("int32"),
                             "span_text": datasets.Value("string"),
                             "input_text": datasets.Value("string"),
                             "turn_id": datasets.Value("int32"),
-                    }),
-                    "1": datasets.features.Sequence({
+                        }
+                    ),
+                    "1": datasets.features.Sequence(
+                        {
                             "span_start": datasets.Value("int32"),
                             "span_end": datasets.Value("int32"),
                             "span_text": datasets.Value("string"),
                             "input_text": datasets.Value("string"),
                             "turn_id": datasets.Value("int32"),
-                    }),
-                    "2": datasets.features.Sequence({
+                        }
+                    ),
+                    "2": datasets.features.Sequence(
+                        {
                             "span_start": datasets.Value("int32"),
                             "span_end": datasets.Value("int32"),
                             "span_text": datasets.Value("string"),
                             "input_text": datasets.Value("string"),
                             "turn_id": datasets.Value("int32"),
-                    }),
                         }
-            })
+                    ),
+                },
+            }
+        )
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=features,
@@ -175,10 +187,7 @@ class Coqa(datasets.GeneratorBasedBuilder):
                 source = row["source"]
                 story = row["story"]
                 questions = [
-                    {
-                        "input_text": q["input_text"],
-                        "turn_id": q["turn_id"]
-                    }
+                    {"input_text": q["input_text"], "turn_id": q["turn_id"]}
                     for q in row["questions"]
                 ]
                 answers = [
@@ -187,7 +196,7 @@ class Coqa(datasets.GeneratorBasedBuilder):
                         "span_end": a["span_end"],
                         "span_text": a["span_text"],
                         "input_text": a["input_text"],
-                        "turn_id": a["turn_id"]
+                        "turn_id": a["turn_id"],
                     }
                     for a in row["answers"]
                 ]
@@ -201,7 +210,7 @@ class Coqa(datasets.GeneratorBasedBuilder):
                                 "span_end": a0["span_end"],
                                 "span_text": a0["span_text"],
                                 "input_text": a0["input_text"],
-                                "turn_id": a0["turn_id"]
+                                "turn_id": a0["turn_id"],
                             }
                             for a0 in row["additional_answers"]["0"]
                         ],
@@ -211,7 +220,7 @@ class Coqa(datasets.GeneratorBasedBuilder):
                                 "span_end": a1["span_end"],
                                 "span_text": a1["span_text"],
                                 "input_text": a1["input_text"],
-                                "turn_id": a1["turn_id"]
+                                "turn_id": a1["turn_id"],
                             }
                             for a1 in row["additional_answers"]["1"]
                         ],
@@ -221,7 +230,7 @@ class Coqa(datasets.GeneratorBasedBuilder):
                                 "span_end": a2["span_end"],
                                 "span_text": a2["span_text"],
                                 "input_text": a2["input_text"],
-                                "turn_id": a2["turn_id"]
+                                "turn_id": a2["turn_id"],
                             }
                             for a2 in row["additional_answers"]["2"]
                         ],
@@ -232,5 +241,5 @@ class Coqa(datasets.GeneratorBasedBuilder):
                     "source": source,
                     "questions": questions,
                     "answers": answers,
-                    "additional_answers": additional_answers
+                    "additional_answers": additional_answers,
                 }

lm_eval/datasets/drop/drop.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-# Custom DROP dataet that, unlike HF, keeps all question-answer pairs
+# Custom DROP dataset that, unlike HF, keeps all question-answer pairs
 # even if there are multiple types of answers for the same question.
 """DROP dataset."""
 
@@ -50,7 +50,8 @@ _URLS = {
     "drop": "https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip",
 }
 
-_EMPTY_VALIDATED_ANSWER = [{
+_EMPTY_VALIDATED_ANSWER = [
+    {
         "number": "",
         "date": {
             "day": "",
@@ -59,8 +60,9 @@ _EMPTY_VALIDATED_ANSWER = [{
         },
         "spans": [],
         "worker_id": "",
-    "hit_id": ""
-}]
+        "hit_id": "",
+    }
+]
 
 
 class Drop(datasets.GeneratorBasedBuilder):
@@ -69,12 +71,14 @@ class Drop(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("0.0.1")
 
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="drop", version=VERSION,
-                               description="The DROP dataset."),
+        datasets.BuilderConfig(
+            name="drop", version=VERSION, description="The DROP dataset."
+        ),
     ]
 
     def _info(self):
-        features = datasets.Features({
+        features = datasets.Features(
+            {
                 "section_id": datasets.Value("string"),
                 "passage": datasets.Value("string"),
                 "question": datasets.Value("string"),
@@ -90,7 +94,8 @@ class Drop(datasets.GeneratorBasedBuilder):
                     "worker_id": datasets.Value("string"),
                     "hit_id": datasets.Value("string"),
                 },
-            "validated_answers": datasets.features.Sequence({
+                "validated_answers": datasets.features.Sequence(
+                    {
                         "number": datasets.Value("string"),
                         "date": {
                             "day": datasets.Value("string"),
@@ -100,8 +105,10 @@ class Drop(datasets.GeneratorBasedBuilder):
                         "spans": datasets.features.Sequence(datasets.Value("string")),
                         "worker_id": datasets.Value("string"),
                         "hit_id": datasets.Value("string"),
-            }),
-        })
+                    }
+                ),
+            }
+        )
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=features,
@@ -118,7 +125,9 @@ class Drop(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "drop_dataset", "drop_dataset_train.json"),
+                    "filepath": os.path.join(
+                        data_dir, "drop_dataset", "drop_dataset_train.json"
+                    ),
                     "split": "train",
                 },
             ),
@@ -126,7 +135,9 @@ class Drop(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "drop_dataset", "drop_dataset_dev.json"),
+                    "filepath": os.path.join(
+                        data_dir, "drop_dataset", "drop_dataset_dev.json"
+                    ),
                     "split": "validation",
                 },
             ),

lm_eval/datasets/gsm8k/dataset_infos.json

-{"gsm8k": {"description": "State-of-the-art language models can match human performance on many tasks, but \nthey still struggle to robustly perform multi-step mathematical reasoning. To \ndiagnose the failures of current models and support research, we introduce GSM8K,\na dataset of 8.5K high quality linguistically diverse grade school math word problems.\nWe find that even the largest transformer models fail to achieve high test performance, \ndespite the conceptual simplicity of this problem distribution.\n", "citation": "@misc{cobbe2021training,\n      title={Training Verifiers to Solve Math Word Problems},\n      author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},\n      year={2021},\n      eprint={2110.14168},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n", "homepage": "https://github.com/openai/grade-school-math", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "gsm8_k", "config_name": "gsm8k", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 3963202, "num_examples": 7473, "dataset_name": "gsm8_k"}, "test": {"name": "test", "num_bytes": 713732, "num_examples": 1319, "dataset_name": "gsm8_k"}}, "download_checksums": {"https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/train.jsonl": {"num_bytes": 4166206, "checksum": "17f347dc51477c50d4efb83959dbb7c56297aba886e5544ee2aaed3024813465"}, "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl": {"num_bytes": 749738, "checksum": "3730d312f6e3440559ace48831e51066acaca737f6eabec99bccb9e4b3c39d14"}}, "download_size": 4915944, "post_processing_size": null, "dataset_size": 4676934, "size_in_bytes": 9592878}}
\ No newline at end of file

lm_eval/datasets/gsm8k/gsm8k.py

-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Grade School Math 8k dataset."""
-
-
-import json
-
-import datasets
-
-
-_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}
-}
-"""
-
-_DESCRIPTION = """\
-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.
-"""
-
-_HOMEPAGE = "https://github.com/openai/grade-school-math"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-_URLS = {
-    "train": "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/train.jsonl",
-    "test": "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl",
-}
-
-
-class GSM8K(datasets.GeneratorBasedBuilder):
-    """Grade School Math 8k"""
-
-    VERSION = datasets.Version("0.0.1")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="gsm8k", version=VERSION,
-                               description="The Grade School Math 8k dataset."),
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "question": datasets.Value("string"),
-                "answer": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        urls = {"train": _URLS["train"], "test": _URLS["test"]}
-        data_dir = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_dir["train"],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_dir["test"],
-                    "split": "test"
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                yield key, {
-                    "question": data["question"],
-                    "answer": data["answer"],
-                }

lm_eval/datasets/headqa/headqa.py

-# coding=utf-8
 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -65,8 +64,12 @@ class HeadQA(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.1.0")
 
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="es", version=VERSION, description="Spanish HEAD dataset"),
-        datasets.BuilderConfig(name="en", version=VERSION, description="English HEAD dataset"),
+        datasets.BuilderConfig(
+            name="es", version=VERSION, description="Spanish HEAD dataset"
+        ),
+        datasets.BuilderConfig(
+            name="en", version=VERSION, description="English HEAD dataset"
+        ),
     ]
 
     DEFAULT_CONFIG_NAME = "es"
@@ -106,15 +109,24 @@ class HeadQA(datasets.GeneratorBasedBuilder):
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_lang_dir, f"train_{dir}.json")},
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "filepath": os.path.join(data_lang_dir, f"train_{dir}.json"),
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_lang_dir, f"test_{dir}.json")},
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "filepath": os.path.join(data_lang_dir, f"test_{dir}.json"),
+                },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_lang_dir, f"dev_{dir}.json")},
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "filepath": os.path.join(data_lang_dir, f"dev_{dir}.json"),
+                },
             ),
         ]
 
@@ -134,7 +146,9 @@ class HeadQA(datasets.GeneratorBasedBuilder):
 
                     aids = [answer["aid"] for answer in question["answers"]]
                     atexts = [answer["atext"].strip() for answer in question["answers"]]
-                    answers = [{"aid": aid, "atext": atext} for aid, atext in zip(aids, atexts)]
+                    answers = [
+                        {"aid": aid, "atext": atext} for aid, atext in zip(aids, atexts)
+                    ]
 
                     id_ = f"{exam_id}_{qid}"
                     yield id_, {

lm_eval/datasets/hendrycks_ethics/hendrycks_ethics.py

@@ -71,54 +71,64 @@ class HendrycksEthics(datasets.GeneratorBasedBuilder):
         EthicsConfig(
             name="commonsense",
             prefix="cm",
-            features=datasets.Features({
+            features=datasets.Features(
+                {
                     "label": datasets.Value("int32"),
                     "input": datasets.Value("string"),
                     "is_short": datasets.Value("bool"),
                     "edited": datasets.Value("bool"),
-            }),
-            description="The Commonsense subset contains examples focusing on moral standards and principles that most people intuitively accept."
+                }
+            ),
+            description="The Commonsense subset contains examples focusing on moral standards and principles that most people intuitively accept.",
         ),
         EthicsConfig(
             name="deontology",
             prefix="deontology",
-            features=datasets.Features({
+            features=datasets.Features(
+                {
                     "group_id": datasets.Value("int32"),
                     "label": datasets.Value("int32"),
                     "scenario": datasets.Value("string"),
                     "excuse": datasets.Value("string"),
-            }),
+                }
+            ),
             description="The Deontology subset contains examples focusing on whether an act is required, permitted, or forbidden according to a set of rules or constraints",
         ),
         EthicsConfig(
             name="justice",
             prefix="justice",
-            features=datasets.Features({
+            features=datasets.Features(
+                {
                     "group_id": datasets.Value("int32"),
                     "label": datasets.Value("int32"),
                     "scenario": datasets.Value("string"),
-            }),
+                }
+            ),
             description="The Justice subset contains examples focusing on how a character treats another person",
         ),
         EthicsConfig(
             name="utilitarianism",
             prefix="util",
-            features=datasets.Features({
+            features=datasets.Features(
+                {
                     "activity": datasets.Value("string"),
                     "baseline": datasets.Value("string"),
                     "rating": datasets.Value("string"),  # Empty rating.
-            }),
+                }
+            ),
             description="The Utilitarianism subset contains scenarios that should be ranked from most pleasant to least pleasant for the person in the scenario",
         ),
         EthicsConfig(
             name="virtue",
             prefix="virtue",
-            features=datasets.Features({
+            features=datasets.Features(
+                {
                     "group_id": datasets.Value("int32"),
                     "label": datasets.Value("int32"),
                     "scenario": datasets.Value("string"),
                     "trait": datasets.Value("string"),
-            }),
+                }
+            ),
             description="The Virtue subset contains scenarios focusing on whether virtues or vices are being exemplified",
         ),
     ]
@@ -140,7 +150,12 @@ class HendrycksEthics(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "ethics", self.config.name, f"{self.config.prefix}_train.csv"),
+                    "filepath": os.path.join(
+                        data_dir,
+                        "ethics",
+                        self.config.name,
+                        f"{self.config.prefix}_train.csv",
+                    ),
                     "split": "train",
                 },
             ),
@@ -148,18 +163,22 @@ class HendrycksEthics(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "ethics", self.config.name, f"{self.config.prefix}_test.csv"),
-                    "split": "test"
+                    "filepath": os.path.join(
+                        data_dir,
+                        "ethics",
+                        self.config.name,
+                        f"{self.config.prefix}_test.csv",
+                    ),
+                    "split": "test",
                 },
-            )
+            ),
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):
-        with open(filepath, newline='') as f:
+        with open(filepath, newline="") as f:
             if self.config.name == "utilitarianism":
-                contents = csv.DictReader(
-                    f, fieldnames=['activity', "baseline"])
+                contents = csv.DictReader(f, fieldnames=["activity", "baseline"])
             else:
                 contents = csv.DictReader(f)
             # For subsets with grouped scenarios, tag them with an id.

lm_eval/datasets/lambada/dataset_infos.json

-{"original": {"description": "LAMBADA is a dataset to evaluate the capabilities of computational models for text\nunderstanding by means of a word prediction task. LAMBADA is a collection of narrative\ntexts sharing the characteristic that human subjects are able to guess their last\nword if they are exposed to the whole text, but not if they only see the last\nsentence preceding the target word. To succeed on LAMBADA, computational models\ncannot simply rely on local context, but must be able to keep track of information\nin the broader discourse.\n\nThe LAMBADA dataset", "citation": "@misc{\n    author={Paperno, Denis and Kruszewski, Germ\u00e1n and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fern\u00e1ndez, Raquel}, \n    title={The LAMBADA dataset},\n    DOI={10.5281/zenodo.2630551},\n    publisher={Zenodo},\n    year={2016},\n    month={Aug}\n}\n", "homepage": "https://zenodo.org/record/2630551#.X4Xzn5NKjUI", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lambada", "config_name": "original", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 1709449, "num_examples": 5153, "dataset_name": "lambada"}}, "download_checksums": {"http://eaidata.bmk.sh/data/lambada_test.jsonl": {"num_bytes": 1819752, "checksum": "4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226"}}, "download_size": 1819752, "post_processing_size": null, "dataset_size": 1709449, "size_in_bytes": 3529201}, "en": {"description": "LAMBADA is a dataset to evaluate the capabilities of computational models for text\nunderstanding by means of a word prediction task. LAMBADA is a collection of narrative\ntexts sharing the characteristic that human subjects are able to guess their last\nword if they are exposed to the whole text, but not if they only see the last\nsentence preceding the target word. To succeed on LAMBADA, computational models\ncannot simply rely on local context, but must be able to keep track of information\nin the broader discourse.\n\nThe English translated LAMBADA dataset", "citation": "@misc{\n    author={Paperno, Denis and Kruszewski, Germ\u00e1n and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fern\u00e1ndez, Raquel}, \n    title={The LAMBADA dataset},\n    DOI={10.5281/zenodo.2630551},\n    publisher={Zenodo},\n    year={2016},\n    month={Aug}\n}\n", "homepage": "https://zenodo.org/record/2630551#.X4Xzn5NKjUI", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lambada", "config_name": "en", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 1709449, "num_examples": 5153, "dataset_name": "lambada"}}, "download_checksums": {"http://eaidata.bmk.sh/data/lambada_test_en.jsonl": {"num_bytes": 1819752, "checksum": "4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226"}}, "download_size": 1819752, "post_processing_size": null, "dataset_size": 1709449, "size_in_bytes": 3529201}, "fr": {"description": "LAMBADA is a dataset to evaluate the capabilities of computational models for text\nunderstanding by means of a word prediction task. 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