HELMET-Eval
Collection
21 subsets of HELMET evaluation datasets
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21 items
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Updated
Dataset Viewer
context
stringlengths 151k
151k
| demos
stringlengths 202
202
| question
stringlengths 36
36
| answer
stringlengths 36
36
| depth
float64 0
1
| num_kvs
int64 1.8k
1.8k
| k
int64 1.8k
1.8k
| prompt_template
stringclasses 1
value | user_template
stringclasses 1
value | system_template
stringclasses 1
value | post_process
stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|
JSON data:
{
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"75d5d186-0e12-47a7-af26-5e49735de67d": "ac35740f-e220-440b-b626-34bd1f0ea3c3",
"10bb8bdb-40b6-44b6-966f-061b8880ec42": "0e42146e-5268-4dd0-8a0c-ab2f2e620e3a",
"a23daf36-3eb1-4df6-8a85-7bb09fe7c056": "d3f07055-2b4e-47c6-b7f9-97b02f1f6176",
"ccc3b268-a7b8-4897-b558-100d56bf6e76": "3cdc2927-f5dc-44c7-a7b6-aa6daaea045e",
"fc1f70f8-1b58-4413-b915-820ad68f60a0": "dd78de5f-2031-4cb8-bfbd-11dfdf418bd0",
"77db6234-adb3-4005-960e-7f678fb99faa": "35f0b358-eb93-44c6-9ac9-69703e1e4150",
"665ae427-993a-4053-ab30-1424ba5a51f6": "171a068c-d3c6-4511-8a11-b36493e097ce",
"9cb97962-4cd3-48ce-8cbb-0e41e42c2d9e": "e57cec98-b962-4bcf-93b4-773392749beb",
"a5146581-a0ba-401c-81b7-e05af1dd4f60": "056ca84d-0d83-44b1-af6c-995f59508be5",
"529a4885-2f34-490a-bb13-72a81eff9f47": "2e09a842-f439-4455-9fbb-489aa790f623",
"e35cf3fc-ce56-4209-9e24-352150bd0e4f": "c762172b-eceb-4d86-9e7c-01fc1183e101",
"1f81a766-ddcc-4ee7-9d6a-83e9d89e6584": "7b154916-1dd2-4d2d-b5d7-6dfc0d948332",
"6ba06238-9524-44d5-b000-1016e7cde532": "f6ff7767-f9bb-454d-9583-faaeca120001",
"2602cdf3-4ee5-48c0-9bc4-b95a60622722": "3aa64438-aceb-48a3-8b04-05141ed7987c",
"1c4fe84b-8702-455f-9603-9dc7fece08ba": "6909c61b-1719-4f6c-bee1-8174439f8a29",
"6cec4c03-936f-49c3-939b-a599605ecb45": "f04fba7e-ab81-49d2-9739-ea07b07bc7f3",
"7b7ad29d-cdeb-4475-bed0-e19f1c8c0d0e": "d7ff7f04-07d2-4e17-9666-c976f1a1f93b",
"09c07409-0ac4-4350-9be6-c9db86fa74f7": "ca94916d-bf18-42f0-abfd-dd5e9e1fa47f",
"f27f4b5f-287f-4abe-8f41-7a513fa516f8": "03170616-53dd-46d0-bb69-81b1b47b6b29",
"8f3b0ec2-785f-4c35-9c32-7cf28debc5d7": "dbc5262c-6a5c-4962-baf0-85a5fa39f6ab",
"2fb5006f-e00b-40b3-af52-1c09be15b302": "5aa2c77d-6bf7-45f6-ae8c-e644f576083d",
"6dc1369d-10ad-4e29-9f76-7c83cd32d073": "1c8620c7-dcd6-4187-a679-03b38858a3b9",
"a35ebd18-2ad2-4a54-99b7-cefba5dc6e0b": "4f232c10-14ab-40b7-bac0-1274e316f41a",
"658f79d5-2c7f-4e3c-909a-97f10291fe48": "501339e6-46e8-4127-818f-3c7101c34962",
"5b8cc1a6-9fbd-4160-817e-382d423aa601": "8d4812ed-966d-4a9f-85d7-c7909038f602",
"f85d2471-be08-4702-829f-69959ac07087": "501c93b4-88d9-4bd2-89d9-69fa60d622ae",
"5857f5bd-8c3c-4940-992b-cd94d235f903": "8a8bce2b-e3e0-4a34-87c9-22555a24c9ea",
"aed73bc5-bd45-4b02-b115-691583b86b2e": "558fb94d-a366-4e28-87c1-1b1658ad3166",
"4f695e66-a671-4a27-aa8e-81407eb3e94d": "c4689d96-a99d-49c9-9769-e2ffc7b3b7c1",
"f08dd1f9-1635-4619-9e24-8c8ba6049443": "caa3ef02-ac65-40cf-ae7e-27b01ed4ddba",
"4b0850fe-debd-461a-b868-2a1e90160969": "b0724bda-910c-42f4-bf02-f704e24486cd",
"4328f089-f6e2-4a65-b6b5-18dc65c339b6": "282a5f1c-1af4-489a-b689-b560b3c23b0f",
"9dd69ee4-9d7f-461c-b9a9-7ed4cb5c0f28": "c5565bec-139c-4f94-93bd-f865d0cbc6ee",
"29233000-7c07-49b7-8a38-2a6ab054fa10": "885ae6d0-a27b-4092-b1f4-d61e020afe14"
}
|
Key: 3ceaa38b-96e8-4702-aef8-32eb45eba6c6
Corresponding value: 6bb4e1f2-496d-4776-9507-8e49c1d0b683
Key: a974982a-cfe6-4b9f-a606-ceb0950c60a7
Corresponding value: 79e162d0-893d-450e-86b0-d8585d86f322
|
54d3a66f-7dfb-46a7-9526-3d78097fdfd4
|
e19f64f7-7f3d-47ec-97ae-898caf9493be
| 0 | 1,800 | 1,800 |
{context}
Extract the value corresponding to the specified key in the JSON object below.
{demos}Key: {question}
Corresponding value:
|
{context}
Extract the value corresponding to the specified key in the JSON object below.
{demos}Key: {question}
|
Corresponding value:
|
def post_process(output, example):
prediction = output["output"]
answer = example["answer"]
mets = calculate_metrics(prediction, answer)
# we don't really need to parse because we ues substring em, but could be nice to see how precise the model is
parsed_pred = parse_output(prediction, "corresponding value:")
new_mets = calculate_metrics(parsed_pred, answer)
mets = {k: max(v, new_mets[k]) for k, v in mets.items()}
return mets, {"parsed_output": parsed_pred}
|
"JSON data:\n{\n \"86e0a95c-ee3d-4f6e-9ea7-67de1857d696\": \"d5be49a3-156d-4309-985c-bfeccf861f2d(...TRUNCATED) | "Key: b1bb2c30-0671-4ee2-bf42-638f2f4abaa0\nCorresponding value: 97fd5451-06fd-4972-b418-0d3bbad0015(...TRUNCATED) |
c7c4da6f-37a7-4948-a430-17e79ef801f2
|
87e3ba9f-3ae0-482e-a607-195db01ee16f
| 0.2 | 1,800 | 1,800 | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) |
Corresponding value:
| " def post_process(output, example):\n prediction = output[\"output\"]\n answer = e(...TRUNCATED) |
"JSON data:\n{\n \"ae7f83ef-5cb7-4617-84f4-f1aab07d4420\": \"bc6d059f-4a09-441a-bdcb-cf3f71f425f8(...TRUNCATED) | "Key: 5f1d1067-9d61-463e-ac8b-475145661010\nCorresponding value: ef3875ff-2698-4a51-aabf-13b1790a131(...TRUNCATED) |
769fe0ff-d097-4b54-b785-aee8b808fe84
|
1097f024-e8da-4418-9ce3-4d33e8e38f5c
| 0.4 | 1,800 | 1,800 | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) |
Corresponding value:
| " def post_process(output, example):\n prediction = output[\"output\"]\n answer = e(...TRUNCATED) |
"JSON data:\n{\n \"0ea39e94-7ceb-46c1-8ed9-df41ad173773\": \"e2c1e117-dc3b-46ed-8d43-43f172883051(...TRUNCATED) | "Key: f327b91d-c04a-4dfb-b585-1c46991fc3a3\nCorresponding value: bf15f73b-453b-4ef7-b882-5ff0f2e1c08(...TRUNCATED) |
ad6fcd3d-0843-4adf-946c-d700753d3008
|
24592eb6-e47a-4e64-b419-5861eea62881
| 0.6 | 1,800 | 1,800 | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) |
Corresponding value:
| " def post_process(output, example):\n prediction = output[\"output\"]\n answer = e(...TRUNCATED) |
"JSON data:\n{\n \"ee432bee-927b-484d-939d-228ccf0b0acc\": \"84fac742-d989-4bf4-a77b-58e94d7e5669(...TRUNCATED) | "Key: 53f2da52-9749-4670-a22e-49964b1e6d33\nCorresponding value: 501eb808-bbad-46d8-8349-2c6fa6ab012(...TRUNCATED) |
808ec00f-ba45-471d-9584-3acbc51facb6
|
b4e9c932-6192-4a58-beae-14709c41b52a
| 0.6 | 1,800 | 1,800 | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) |
Corresponding value:
| " def post_process(output, example):\n prediction = output[\"output\"]\n answer = e(...TRUNCATED) |
"JSON data:\n{\n \"0bb1cfab-8720-4bf8-b29d-9829a6e950d4\": \"f8fd26e0-7b2e-4116-8e20-775ab2d9a1b6(...TRUNCATED) | "Key: 912e4211-3761-44ff-887e-c7f6386e9d4b\nCorresponding value: c2dcbe08-f234-4f5c-a934-597cba4d0ac(...TRUNCATED) |
91b01089-682f-4b9d-a394-db82510ece46
|
0995a690-6cd2-42d0-a42b-df073579e10e
| 0.8 | 1,800 | 1,800 | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) |
Corresponding value:
| " def post_process(output, example):\n prediction = output[\"output\"]\n answer = e(...TRUNCATED) |
"JSON data:\n{\n \"0757018e-79ef-4c89-a340-3eac5353a67c\": \"bbcc0fbf-4c75-4947-a443-624f44a5d5b9(...TRUNCATED) | "Key: be44ea07-0c6c-4df9-8018-c234b8e3f0c3\nCorresponding value: e867492f-0f5c-4333-9a10-7b373d30d5d(...TRUNCATED) |
5131084e-1b44-4bf6-8427-502a985c6789
|
67856300-31d9-4198-816c-d9920145da43
| 0.2 | 1,800 | 1,800 | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) |
Corresponding value:
| " def post_process(output, example):\n prediction = output[\"output\"]\n answer = e(...TRUNCATED) |
"JSON data:\n{\n \"9139cb0d-3655-46ae-8708-26d4e48a37f6\": \"6eb6c7ac-8ffb-474d-8f09-94b3977be15c(...TRUNCATED) | "Key: 7cdd61d7-6980-4443-bad0-59765e6fdc10\nCorresponding value: 22f078ed-5e9a-44ec-8bd7-5d6a7999099(...TRUNCATED) |
9139cb0d-3655-46ae-8708-26d4e48a37f6
|
6eb6c7ac-8ffb-474d-8f09-94b3977be15c
| 0 | 1,800 | 1,800 | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) |
Corresponding value:
| " def post_process(output, example):\n prediction = output[\"output\"]\n answer = e(...TRUNCATED) |
"JSON data:\n{\n \"1dc10936-1fc0-4efd-b923-5419d260fda4\": \"ca875896-a384-4b19-a00c-a7f2828600d7(...TRUNCATED) | "Key: 43471813-29b6-4f41-b6c8-8e53b2998126\nCorresponding value: 6fe896ce-d605-47a0-b45b-1287588f098(...TRUNCATED) |
99c9d452-6071-47d1-a293-8a2d23d87899
|
7d580715-a332-467f-ad62-cb51e5367ce2
| 0.6 | 1,800 | 1,800 | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) |
Corresponding value:
| " def post_process(output, example):\n prediction = output[\"output\"]\n answer = e(...TRUNCATED) |
"JSON data:\n{\n \"9139cb0d-3655-46ae-8708-26d4e48a37f6\": \"6eb6c7ac-8ffb-474d-8f09-94b3977be15c(...TRUNCATED) | "Key: 8a986003-19e0-4eda-840c-f83da8632f7b\nCorresponding value: 19ee7ade-df56-4d1b-81c8-950abd44651(...TRUNCATED) |
17e5b9b1-39f0-4c81-af69-51866222facd
|
5a802315-2123-49fd-b449-48b11580f2fd
| 0.2 | 1,800 | 1,800 | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) | "{context}\n\nExtract the value corresponding to the specified key in the JSON object below.\n\n{dem(...TRUNCATED) |
Corresponding value:
| " def post_process(output, example):\n prediction = output[\"output\"]\n answer = e(...TRUNCATED) |
End of preview. Expand
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