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@@ -50,16 +50,32 @@ Each row of the click / annotation dataset contains the following attributes. Us
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  |------------------------------|----------------|-------------|
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  | query_id | string | Baidu query_id |
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  | query_md5 | string | MD5 hash of query text |
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- | url_md5 | List[string] | MD5 hash of document url, most reliable document identifier |
 
 
 
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  | text_md5 | List[string] | MD5 hash of document title and abstract |
 
 
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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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- | n | int32 | Number of documents for current query, useful for padding |
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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 continous integer range) |
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- | displayed_time | Tensor[float32]| Seconds a document was displayed on screen |
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- | serp_height | Tensor[int32] | Pixel height of a document on screen |
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- | slipoff_count_after_click | Tensor[int32] | Number of times a document was scrolled off screen after previously clicking on it |
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Expert annotation dataset
@@ -67,11 +83,28 @@ Each row of the click / annotation dataset contains the following attributes. Us
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  |------------------------------|----------------|-------------|
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  | query_id | string | Baidu query_id |
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  | query_md5 | string | MD5 hash of query text |
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- | text_md5 | List[string] | MD5 hash of document title and abstract |
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- | query_document_embedding | Tensor[float16]| BERT CLS token |
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- | label | Tensor[int32] | Relevance judgment on a scale from 0 (bad) to 4 (excellent) |
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- | n | int32 | Number of documents for current query, useful for padding |
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  | frequency_bucket | int32 | Monthly frequency of query (bucket) from 0 (high frequency) to 9 (low frequency) |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Example PyTorch collate function
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  Each sample in the dataset is a single query with multiple documents.
 
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  |------------------------------|----------------|-------------|
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  | query_id | string | Baidu query_id |
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  | query_md5 | string | MD5 hash of query text |
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+ | query | List[int32] | List of query tokens |
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+ | query_length | int32 | Number of query tokens |
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+ | n | int32 | Number of documents for current query, useful for padding |
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+ | url_md5 | List[string] | MD5 hash of document URL, most reliable document identifier |
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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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+ | displayed_time | Tensor[float32]| Seconds a document was displayed on the screen |
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+ | serp_height | Tensor[int32] | Pixel height of a document on the screen |
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+ | slipoff_count_after_click | Tensor[int32] | Number of times a document was scrolled off the screen after previously clicking on it |
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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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+ | abstract_bm25 | Tensor[float32] | BM25 score for document abstracts |
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+ | tf_idf | Tensor[float32] | TF-IDF score for documents |
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+ | tf | Tensor[float32] | Term frequency for documents |
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+ | idf | Tensor[float32] | Inverse document frequency for documents |
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+ | ql_jelinek_mercer_short | Tensor[float32] | Query likelihood score for documents using Jelinek-Mercer smoothing (alpha = 0.1) |
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+ | ql_jelinek_mercer_long | Tensor[float32] | Query likelihood score for documents using Jelinek-Mercer smoothing (alpha = 0.7) |
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+ | ql_dirichlet | Tensor[float32] | Query likelihood score for documents using Dirichlet smoothing (lambda = 128) |
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+ | document_length | Tensor[int32] | Length of documents |
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+ | title_length | Tensor[int32] | Length of document titles |
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+ | abstract_length | Tensor[int32] | Length of document abstracts |
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  ### Expert annotation dataset
 
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  |------------------------------|----------------|-------------|
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  | query_id | string | Baidu query_id |
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  | query_md5 | string | MD5 hash of query text |
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+ | query | List[int32] | List of query tokens |
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+ | query_length | int32 | Number of query tokens |
 
 
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  | frequency_bucket | int32 | Monthly frequency of query (bucket) from 0 (high frequency) to 9 (low frequency) |
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+ | n | int32 | Number of documents for current query, useful for padding |
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+ | url_md5 | List[string] | MD5 hash of document URL, most reliable document identifier |
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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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+ | abstract_bm25 | Tensor[float32] | BM25 score for document abstracts |
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+ | tf_idf | Tensor[float32] | TF-IDF score for documents |
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+ | tf | Tensor[float32] | Term frequency for documents |
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+ | idf | Tensor[float32] | Inverse document frequency for documents |
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+ | ql_jelinek_mercer_short | Tensor[float32] | Query likelihood score for documents using Jelinek-Mercer smoothing (alpha = 0.1) |
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+ | ql_jelinek_mercer_long | Tensor[float32] | Query likelihood score for documents using Jelinek-Mercer smoothing (alpha = 0.7) |
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+ | ql_dirichlet | Tensor[float32] | Query likelihood score for documents using Dirichlet smoothing (lambda = 128) |
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+ | document_length | Tensor[int32] | Length of documents |
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+ | title_length | Tensor[int32] | Length of document titles |
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+ | abstract_length | Tensor[int32] | Length of document abstracts |
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  ## Example PyTorch collate function
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  Each sample in the dataset is a single query with multiple documents.