Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
json
Sub-tasks:
document-retrieval
Size:
10K - 100K
Tags:
text-retrieval
Dataset Viewer
query-id
stringlengths 5
5
| corpus-id
stringlengths 9
9
| score
float64 1
1
|
|---|---|---|
00000 | 000000000 | 1 |
00001 | 000000001 | 1 |
00002 | 000000002 | 1 |
00003 | 000000003 | 1 |
00004 | 000000004 | 1 |
00005 | 000000005 | 1 |
00006 | 000000006 | 1 |
00007 | 000000007 | 1 |
00008 | 000000008 | 1 |
00009 | 000000009 | 1 |
00010 | 000000010 | 1 |
00011 | 000000011 | 1 |
00012 | 000000012 | 1 |
00013 | 000000013 | 1 |
00014 | 000000014 | 1 |
00015 | 000000015 | 1 |
00016 | 000000016 | 1 |
00017 | 000000017 | 1 |
00018 | 000000018 | 1 |
00019 | 000000019 | 1 |
00020 | 000000020 | 1 |
00021 | 000000021 | 1 |
00022 | 000000022 | 1 |
00023 | 000000023 | 1 |
00024 | 000000024 | 1 |
00025 | 000000025 | 1 |
00026 | 000000026 | 1 |
00027 | 000000027 | 1 |
00028 | 000000028 | 1 |
00029 | 000000029 | 1 |
00030 | 000000030 | 1 |
00031 | 000000031 | 1 |
00032 | 000000032 | 1 |
00033 | 000000033 | 1 |
00034 | 000000034 | 1 |
00035 | 000000035 | 1 |
00036 | 000000036 | 1 |
00037 | 000000037 | 1 |
00038 | 000000038 | 1 |
00039 | 000000039 | 1 |
00040 | 000000040 | 1 |
00041 | 000000041 | 1 |
00042 | 000000042 | 1 |
00043 | 000000043 | 1 |
00044 | 000000044 | 1 |
00045 | 000000045 | 1 |
00046 | 000000046 | 1 |
00047 | 000000047 | 1 |
00048 | 000000048 | 1 |
00049 | 000000049 | 1 |
00050 | 000000050 | 1 |
00051 | 000000051 | 1 |
00052 | 000000052 | 1 |
00053 | 000000053 | 1 |
00054 | 000000054 | 1 |
00055 | 000000055 | 1 |
00056 | 000000056 | 1 |
00057 | 000000057 | 1 |
00058 | 000000058 | 1 |
00059 | 000000059 | 1 |
00060 | 000000060 | 1 |
00061 | 000000061 | 1 |
00062 | 000000062 | 1 |
00063 | 000000063 | 1 |
00064 | 000000064 | 1 |
00065 | 000000065 | 1 |
00066 | 000000066 | 1 |
00067 | 000000067 | 1 |
00068 | 000000068 | 1 |
00069 | 000000069 | 1 |
00070 | 000000070 | 1 |
00071 | 000000071 | 1 |
00072 | 000000072 | 1 |
00073 | 000000073 | 1 |
00074 | 000000074 | 1 |
00075 | 000000075 | 1 |
00076 | 000000076 | 1 |
00077 | 000000077 | 1 |
00078 | 000000078 | 1 |
00079 | 000000079 | 1 |
00080 | 000000080 | 1 |
00081 | 000000081 | 1 |
00082 | 000000082 | 1 |
00083 | 000000083 | 1 |
00084 | 000000084 | 1 |
00085 | 000000085 | 1 |
00086 | 000000086 | 1 |
00087 | 000000087 | 1 |
00088 | 000000088 | 1 |
00089 | 000000089 | 1 |
00090 | 000000090 | 1 |
00091 | 000000091 | 1 |
00092 | 000000092 | 1 |
00093 | 000000093 | 1 |
00094 | 000000094 | 1 |
00095 | 000000095 | 1 |
00096 | 000000096 | 1 |
00097 | 000000097 | 1 |
00098 | 000000098 | 1 |
00099 | 000000099 | 1 |
End of preview. Expand
in Data Studio
- The corpus set consists of the legal documents.
- The query set includes questions pertaining to legal documents.
Usage
import datasets
# Download the dataset
queries = datasets.load_dataset("embedding-benchmark/ChatDoctor_HealthCareMagic", "queries")
documents = datasets.load_dataset("embedding-benchmark/ChatDoctor_HealthCareMagic", "corpus")
pair_labels = datasets.load_dataset("embedding-benchmark/ChatDoctor_HealthCareMagic", "default")
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