philipphager/baidu-ultr_uva-bert_ips-pointwise
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Query-document vectors and clicks for a subset of the Baidu Unbiased Learning to Rank dataset. This dataset uses a BERT cross-encoder with 12 layers trained on a Masked Language Modeling (MLM) and click-through-rate (CTR) prediction task to compute query-document vectors (768 dims). The model is available at: https://huggingface.co/philipphager/baidu-ultr_uva-bert_naive-pointwise
pip install pandas pyarrow
pyarrow >= 14.0.1
from datasets import load_dataset
dataset = load_dataset(
"philipphager/baidu-ultr_baidu-mlm-ctr",
name="clicks",
split="train", # ["train", "test"]
cache_dir="~/.cache/huggingface",
)
dataset.set_format("torch") # [None, "numpy", "torch", "tensorflow", "pandas", "arrow"]
from datasets import load_dataset
dataset = load_dataset(
"philipphager/baidu-ultr_baidu-mlm-ctr",
name="annotations",
split="test",
cache_dir="~/.cache/huggingface",
)
dataset.set_format("torch") # [None, "numpy", "torch", "tensorflow", "pandas", "arrow"]
Each row of the click / annotation dataset contains the following attributes. Use a custom collate_fn
to select specific features (see below):
name | dtype | description |
---|---|---|
query_id | string | Baidu query_id |
query_md5 | string | MD5 hash of query text |
query | List[int32] | List of query tokens |
query_length | int32 | Number of query tokens |
n | int32 | Number of documents for current query, useful for padding |
url_md5 | List[string] | MD5 hash of document URL, most reliable document identifier |
text_md5 | List[string] | MD5 hash of document title and abstract |
title | List[List[int32]] | List of tokens for document titles |
abstract | List[List[int32]] | List of tokens for document abstracts |
query_document_embedding | Tensor[Tensor[float16]] | BERT CLS token |
click | Tensor[int32] | Click / no click on a document |
position | Tensor[int32] | Position in ranking (does not always match original item position) |
media_type | Tensor[int32] | Document type (label encoding recommended as IDs do not occupy a continuous integer range) |
displayed_time | Tensor[float32] | Seconds a document was displayed on the screen |
serp_height | Tensor[int32] | Pixel height of a document on the screen |
slipoff_count_after_click | Tensor[int32] | Number of times a document was scrolled off the screen after previously clicking on it |
bm25 | Tensor[float32] | BM25 score for documents |
bm25_title | Tensor[float32] | BM25 score for document titles |
bm25_abstract | Tensor[float32] | BM25 score for document abstracts |
tf_idf | Tensor[float32] | TF-IDF score for documents |
tf | Tensor[float32] | Term frequency for documents |
idf | Tensor[float32] | Inverse document frequency for documents |
ql_jelinek_mercer_short | Tensor[float32] | Query likelihood score for documents using Jelinek-Mercer smoothing (alpha = 0.1) |
ql_jelinek_mercer_long | Tensor[float32] | Query likelihood score for documents using Jelinek-Mercer smoothing (alpha = 0.7) |
ql_dirichlet | Tensor[float32] | Query likelihood score for documents using Dirichlet smoothing (lambda = 128) |
document_length | Tensor[int32] | Length of documents |
title_length | Tensor[int32] | Length of document titles |
abstract_length | Tensor[int32] | Length of document abstracts |
name | dtype | description |
---|---|---|
query_id | string | Baidu query_id |
query_md5 | string | MD5 hash of query text |
query | List[int32] | List of query tokens |
query_length | int32 | Number of query tokens |
frequency_bucket | int32 | Monthly frequency of query (bucket) from 0 (high frequency) to 9 (low frequency) |
n | int32 | Number of documents for current query, useful for padding |
url_md5 | List[string] | MD5 hash of document URL, most reliable document identifier |
text_md5 | List[string] | MD5 hash of document title and abstract |
title | List[List[int32]] | List of tokens for document titles |
abstract | List[List[int32]] | List of tokens for document abstracts |
query_document_embedding | Tensor[Tensor[float16]] | BERT CLS token |
label | Tensor[int32] | Relevance judgments on a scale from 0 (bad) to 4 (excellent) |
bm25 | Tensor[float32] | BM25 score for documents |
bm25_title | Tensor[float32] | BM25 score for document titles |
bm25_abstract | Tensor[float32] | BM25 score for document abstracts |
tf_idf | Tensor[float32] | TF-IDF score for documents |
tf | Tensor[float32] | Term frequency for documents |
idf | Tensor[float32] | Inverse document frequency for documents |
ql_jelinek_mercer_short | Tensor[float32] | Query likelihood score for documents using Jelinek-Mercer smoothing (alpha = 0.1) |
ql_jelinek_mercer_long | Tensor[float32] | Query likelihood score for documents using Jelinek-Mercer smoothing (alpha = 0.7) |
ql_dirichlet | Tensor[float32] | Query likelihood score for documents using Dirichlet smoothing (lambda = 128) |
document_length | Tensor[int32] | Length of documents |
title_length | Tensor[int32] | Length of document titles |
abstract_length | Tensor[int32] | Length of document abstracts |
Each sample in the dataset is a single query with multiple documents. The following example demonstrates how to create a batch containing multiple queries with varying numbers of documents by applying padding:
import torch
from typing import List
from collections import defaultdict
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader
def collate_clicks(samples: List):
batch = defaultdict(lambda: [])
for sample in samples:
batch["query_document_embedding"].append(sample["query_document_embedding"])
batch["position"].append(sample["position"])
batch["click"].append(sample["click"])
batch["n"].append(sample["n"])
return {
"query_document_embedding": pad_sequence(
batch["query_document_embedding"], batch_first=True
),
"position": pad_sequence(batch["position"], batch_first=True),
"click": pad_sequence(batch["click"], batch_first=True),
"n": torch.tensor(batch["n"]),
}
loader = DataLoader(dataset, collate_fn=collate_clicks, batch_size=16)