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.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+2023/validation/a3fbeb63-0e8c-4a11-bff6-0e3b484c3e9c.pptx filter=lfs diff=lfs merge=lfs -text

2023/validation/1f975693-876d-457b-a649-393859e79bf3.mp3

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+oid sha256:200f767e732b49efef5c05d128903ee4d2c34e66fdce7f5593ac123b2e637673
+size 280868

2023/validation/2b3ef98c-cc05-450b-a719-711aee40ac65.mp3

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+version https://git-lfs.github.com/spec/v1
+oid sha256:946a5ea50e3212755b2e3d8149eb90390becbf32cfe5a9686acc0ade79bea62c
+size 205008

2023/validation/389793a7-ca17-4e82-81cb-2b3a2391b4b9.txt

@@ -0,0 +1,3 @@
+       H       H           H
+--------------------------------
+H          H            H    H     

2023/validation/8d46b8d6-b38a-47ff-ac74-cda14cf2d19b.csv

@@ -0,0 +1,345 @@
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+Gentoo,Biscoe,52.2,17.1,228,5400,MALE
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+Gentoo,Biscoe,49.5,16.1,224,5650,MALE
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+Gentoo,Biscoe,48.4,14.4,203,4625,FEMALE
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+Gentoo,Biscoe,47.3,13.8,216,4725,
+Gentoo,Biscoe,46.8,16.1,215,5500,MALE
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+Gentoo,Biscoe,43.3,14,208,4575,FEMALE
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+Gentoo,Biscoe,50.5,15.2,216,5000,FEMALE
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+Gentoo,Biscoe,43.5,15.2,213,4650,FEMALE
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+Gentoo,Biscoe,48.8,16.2,222,6000,MALE
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+Gentoo,Biscoe,,,,,
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2023/validation/99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3.mp3

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+oid sha256:b218c951c1f888f0bbe6f46c080f57afc7c9348fffc7ba4da35749ff1e2ac40f
+size 179304

2023/validation/9b54f9d9-35ee-4a14-b62f-d130ea00317f.zip

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+oid sha256:63e5b88f7abfcda1b09e3f885d43b772471fcc2ee2852258644c953e9f21f3f8
+size 11689

2023/validation/a3fbeb63-0e8c-4a11-bff6-0e3b484c3e9c.pptx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0487ecc2323124b56381e11f47ccc75534ed6c841671b39a0de8b86216213d76
+size 388996

2023/validation/bec74516-02fc-48dc-b202-55e78d0e17cf.jsonld

@@ -0,0 +1,98 @@
+{
+  "@context": "http://schema.org",
+  "@type": "Collection",
+  "@id": "https://doi.org/10.5447/ipk/2022/29",
+  "url": "https://doi.ipk-gatersleben.de:443/DOI/64fb788c-7495-4800-8568-fd562b07017e/fbda7260-8307-485e-a9b7-d84292e3eb04/2",
+  "additionalType": "directory",
+  "name": "GLOBAL STRATEGY FOR THE CONSERVATION OF POTATO",
+  "author": {
+    "name": "Manuela Nagel",
+    "givenName": "Manuela",
+    "familyName": "Nagel",
+    "affiliation": {
+      "@type": "Organization",
+      "name": "Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland OT Gatersleben, Corrensstraße 3, 06466, Germany"
+    },
+    "@id": "https://orcid.org/0000-0003-0396-0333"
+  },
+  "editor": [
+    {
+      "name": "Ehsan Dulloo",
+      "givenName": "Ehsan",
+      "familyName": "Dulloo",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "International Consultant, ,"
+      },
+      "contributorType": "Researcher"
+    },
+    {
+      "name": "Prishnee Bissessur",
+      "givenName": "Prishnee",
+      "familyName": "Bissessur",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "International Consultant, ,"
+      },
+      "contributorType": "Researcher"
+    },
+    {
+      "name": "Tatjana Gavrilenko",
+      "givenName": "Tatjana",
+      "familyName": "Gavrilenko",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "N.I. Vavilov All-Russian Institute of Plant Genetic Resources, , Russia"
+      },
+      "contributorType": "Researcher",
+      "@id": "https://orcid.org/0000-0002-2605-6569"
+    },
+    {
+      "name": "John Bamberg",
+      "givenName": "John",
+      "familyName": "Bamberg",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "U. S. Potato Genebank, , USA"
+      },
+      "contributorType": "Researcher",
+      "@id": "https://orcid.org/0000-0001-6102-7846"
+    },
+    {
+      "name": "David Ellis",
+      "givenName": "David",
+      "familyName": "Ellis",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "International Potato Center (CIP), , Peru"
+      },
+      "contributorType": "Researcher",
+      "@id": "https://orcid.org/0000-0002-0209-2784"
+    },
+    {
+      "name": "Peter Giovannini",
+      "givenName": "Peter",
+      "familyName": "Giovannini",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "Global Crop Diversity Trust, ,"
+      },
+      "contributorType": "Researcher",
+      "@id": "https://orcid.org/0000-0002-1053-2030"
+    }
+  ],
+  "description": "Cultivated potato, Solanum tuberosum ssp. tuberosum, is the third most consumed crop globally and important not only for food but also for for the animal feed, pharmaceutical, textile and paper industries. To gain an overview on the current state of the conservation and use of potato genetic resources, the Global Crop Diversity Trust (Crop Trust), commissioned an update of the ‘Global conservation strategy for potato genetic resources’. This updated strategy aims to support the efficiency and effectiveness of potato diversity conservation at national, regional and international levels, and to identify priorities for strengthening the conservation and use of potato genetic resources.",
+  "keywords": "ex situ conservation, plant genetic resources, potato, Solanum tuberosum, global strategy, conservation strategy, wild potato, Andigenum group, Chilotanum group, native potato variety, genebank, accession, true potato seed, potato tuber, late blight",
+  "inLanguage": "en",
+  "contentSize": "0 B",
+  "datePublished": "2022",
+  "schemaVersion": "http://datacite.org/schema/kernel-4",
+  "publisher": {
+    "@type": "Organization",
+    "name": "e!DAL - Plant Genomics and Phenomics Research Data Repository (PGP), IPK Gatersleben, Seeland OT Gatersleben, Corrensstraße 3, 06466, Germany"
+  },
+  "provider": {
+    "@type": "Organization",
+    "name": "datacite"
+  }
+}

2023/validation/bfcd99e1-0690-4b53-a85c-0174a8629083.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5bf1a7e3bfa9cb9b65c73803323eb574043297680890a842685430ead3573d36
+size 162769

2023/validation/f918266a-b3e0-4914-865d-4faa564f1aef.py

@@ -0,0 +1,35 @@
+from random import randint
+import time
+
+class UhOh(Exception):
+    pass
+
+class Hmm:
+    def __init__(self):
+        self.value = randint(-100, 100)
+
+    def Yeah(self):
+        if self.value == 0:
+            return True
+        else:
+            raise UhOh()
+
+def Okay():
+    while True:
+        yield Hmm()
+
+def keep_trying(go, first_try=True):
+    maybe = next(go)
+    try:
+        if maybe.Yeah():
+            return maybe.value
+    except UhOh:
+        if first_try:
+            print("Working...")
+            print("Please wait patiently...")
+        time.sleep(0.1)
+        return keep_trying(go, first_try=False)
+
+if __name__ == "__main__":
+    go = Okay()
+    print(f"{keep_trying(go)}")

2023/validation/metadata.jsonl

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+{"task_id": "544b7f0c-173a-4377-8d56-57b36eb26ddf", "Question": "In Valentina Re\u2019s contribution to the 2017 book \u201cWorld Building: Transmedia, Fans, Industries\u201d, what horror movie does the author cite as having popularized metalepsis between a dream world and reality? Use the complete name with article if any.", "Level": 2, "Final answer": "A Nightmare on Elm Street", "file_name": "", "Annotator Metadata": {"Steps": "1. Search the web for \u201cworld building transmedia fans industries\u201d.\n2. Click link to PDF of the book.\n3. Navigate to the Media Cited section of the essay written by Valentina Re.\n4. Identify the horror movie, A Nightmare on Elm Street.\n5. Navigate to its mention in the essay, to confirm that it does relate to metalepsis from a dream world.", "Number of steps": "5", "How long did this take?": "5-10 minutes", "Tools": "1. Search engine\n2. Web browser\n3. PDF viewer", "Number of tools": "3"}}

GAIA.py

@@ -0,0 +1,96 @@
+"""GAIA 2023 dataset."""
+
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """ """
+
+_DESCRIPTION = """ """
+
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+_NAMES = [
+    "2023_all",
+    "2023_level1",
+    "2023_level2",
+    "2023_level3",
+]
+
+YEAR_TO_LEVELS = {"2023": [1, 2, 3]}
+
+separator = "_"
+
+
+class GAIA_dataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name=name, version=version, description=name)
+        for name, version in zip(_NAMES, [VERSION] * len(_NAMES))
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "task_id": datasets.Value("string"),
+                "Question": datasets.Value("string"),
+                "Level": datasets.Value("string"),
+                "Final answer": datasets.Value("string"), # ? for test values
+                "file_name": datasets.Value("string"),
+                "file_path": datasets.Value("string"),  # generated here
+                "Annotator Metadata": {k: datasets.Value("string") for k in ["Steps", "Number of steps", "How long did this take?", "Tools", "Number of tools"]} # "", 
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        year, level_name = self.config.name.split(separator)
+        if level_name == "all":
+            levels = YEAR_TO_LEVELS[year]
+        else:
+            level_name = int(level_name.split("level")[1])
+            levels = [level_name]
+        print(year, level_name)
+
+        output = []
+        for split in ["test", "validation"]:
+            root_file = dl_manager.download(os.path.join(year, split, "metadata.jsonl"))
+            test_attached_files = {"": ""}
+            with open(root_file, "r", encoding="utf-8") as f:
+                for line in f:
+                    cur_line = json.loads(line)
+                    if cur_line["Level"] in levels and cur_line["file_name"] != "":
+                        attached_file_name = cur_line["file_name"]
+                        attached_file = dl_manager.download(os.path.join(year, split, attached_file_name))
+                        test_attached_files[attached_file_name] = attached_file
+
+            output.append(
+                datasets.SplitGenerator(
+                    name=getattr(datasets.Split, split.upper()),
+                    gen_kwargs={"root_file": root_file, "attached_files": test_attached_files, "levels": levels},
+                )
+            )
+        return output
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, root_file: str, attached_files: dict, levels: list[int]):
+        with open(root_file, "r", encoding="utf-8") as f:
+            for key, line in enumerate(f):
+                cur_line = json.loads(line)
+                if cur_line["Level"] in levels:
+                    cur_line["file_path"] = attached_files[cur_line["file_name"]]
+                    yield key, cur_line
+
+

README.md

@@ -0,0 +1,22 @@
+---
+language:
+- en
+pretty_name: General AI Assistants Benchmark
+extra_gated_prompt: "To avoid contamination and data leakage, you agree to not reshare this dataset outside of a gated or private repository on the HF hub."
+extra_gated_fields:
+  I agree to not reshare the GAIA submissions set according to the above conditions: checkbox
+---
+# GAIA dataset
+
+GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc).
+
+We added gating to prevent bots from scraping the dataset. Please do not reshare the validation or test set in a crawlable format.
+
+## Data and leaderboard
+GAIA is made of more than 450 non-trivial question with an unambiguous answer, requiring different levels of tooling and autonomy to solve. It is therefore divided in 3 levels, where level 1 should be breakable by very good LLMs, and level 3 indicate a strong jump in model capabilities. Each level is divided into a fully public dev set for validation, and a test set with private answers and metadata.
+
+GAIA leaderboard can be found in this space (https://huggingface.co/spaces/gaia-benchmark/leaderboard).
+
+Questions are contained in metadata.jsonl. Some questions come with an additional file, that can be found in the same folder and whose id is given in the field file_name.
+
+More details in [the paper](https://arxiv.org/abs/2311.12983) for now and soon here as well.