Datasets:
ArXiv:
License:
philipphager
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Update README.md
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README.md
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@@ -57,7 +57,7 @@ Each row of the click / annotation dataset contains the following attributes. Us
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| text_md5 | List[string] | MD5 hash of document title and abstract |
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| title | List[List[int32]] | List of tokens for document titles |
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| abstract | List[List[int32]] | List of tokens for document abstracts |
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| query_document_embedding | Tensor[float16]| BERT CLS token |
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| click | Tensor[int32] | Click / no click on a document |
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| position | Tensor[int32] | Position in ranking (does not always match original item position) |
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| media_type | Tensor[int32] | Document type (label encoding recommended as IDs do not occupy a continuous integer range) |
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@@ -91,7 +91,7 @@ Each row of the click / annotation dataset contains the following attributes. Us
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| text_md5 | List[string] | MD5 hash of document title and abstract |
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| title | List[List[int32]] | List of tokens for document titles |
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| abstract | List[List[int32]] | List of tokens for document abstracts |
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| query_document_embedding | Tensor[float16] | BERT CLS token |
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| label | Tensor[int32] | Relevance judgments on a scale from 0 (bad) to 4 (excellent) |
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| bm25 | Tensor[float32] | BM25 score for documents |
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| title_bm25 | Tensor[float32] | BM25 score for document titles |
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| text_md5 | List[string] | MD5 hash of document title and abstract |
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| title | List[List[int32]] | List of tokens for document titles |
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| abstract | List[List[int32]] | List of tokens for document abstracts |
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| query_document_embedding | Tensor[Tensor[float16]]| BERT CLS token |
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| click | Tensor[int32] | Click / no click on a document |
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| position | Tensor[int32] | Position in ranking (does not always match original item position) |
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| media_type | Tensor[int32] | Document type (label encoding recommended as IDs do not occupy a continuous integer range) |
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| text_md5 | List[string] | MD5 hash of document title and abstract |
|
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| title | List[List[int32]] | List of tokens for document titles |
|
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| abstract | List[List[int32]] | List of tokens for document abstracts |
|
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| query_document_embedding | Tensor[Tensor[float16]] | BERT CLS token |
|
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| label | Tensor[int32] | Relevance judgments on a scale from 0 (bad) to 4 (excellent) |
|
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| bm25 | Tensor[float32] | BM25 score for documents |
|
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| title_bm25 | Tensor[float32] | BM25 score for document titles |
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