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README.md

@@ -1,45 +1,117 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: question
-    dtype: string
-  - name: context
-    dtype: string
-  - name: answers
-    list: string
-  - name: source
-    dtype: string
-  - name: question_timestamp
-    dtype: string
-  - name: evidence_timestamp
-    dtype: string
-  - name: dimensions
-    dtype: string
-  - name: is_hard_negative
-    dtype: bool
-  - name: original_question
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 87197610
-    num_examples: 25041
-  - name: validation
-    num_bytes: 10292059
-    num_examples: 2800
-  - name: test
-    num_bytes: 30863658
-    num_examples: 9089
-  download_size: 71628923
-  dataset_size: 128353327
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+license: mit
+task_categories:
+  - question-answering
+  - text-retrieval
+language:
+  - en
+tags:
+  - temporal-qa
+  - time-sensitive
+  - retrieval-augmented-generation
+  - timestamp
+  - streamingqa
+  - timeqa
+size_categories:
+  - 10K<n<100K
 ---
+
+# Time-Sensitive QA Dataset (StreamingQA + TimeQA)
+
+A time-sensitive question answering dataset combining **StreamingQA** and **TimeQA** for evaluating temporal reasoning in RAG systems.
+
+## Dataset Description
+
+This dataset contains question-answer pairs where the correct answer depends on a specific timestamp.
+Each question is prefixed with a date (e.g., "Today is Tuesday, September 24, 2013.") and the model
+must use temporal context to provide the correct answer for that point in time.
+
+### Sources
+
+- **StreamingQA** ([Liska et al., 2022](https://arxiv.org/abs/2205.02266)): Time-sensitive questions about news articles from the WMT corpus
+- **TimeQA** ([Chen et al., 2021](https://arxiv.org/abs/2108.06314)): Temporal knowledge base questions derived from WikiData
+
+### Features
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | string | Unique identifier |
+| `question` | string | Full question with timestamp prefix (e.g., "Today is Monday, Jan 5, 2015. Who is...") |
+| `context` | string | Evidence document containing the answer |
+| `answers` | list[string] | List of acceptable answers |
+| `source` | string | Original dataset (`streamingqa` or `timeqa`) |
+| `question_timestamp` | string | When the question is asked (ISO format) |
+| `evidence_timestamp` | string | When the evidence document was published |
+| `dimensions` | string | Metadata about temporal dimensions (JSON) |
+| `is_hard_negative` | bool | Whether this is a hard negative (temporal perturbation) |
+| `original_question` | string | Question without timestamp prefix |
+
+### Splits
+
+| Split | Examples | Description |
+|-------|----------|-------------|
+| train | 25,041 | Training set |
+| validation | 2,800 | Validation set |
+| test | 9,089 | Test/evaluation set |
+
+### Hard Negatives
+
+Examples with `is_hard_negative=True` are temporal perturbations where the question timestamp
+is shifted to make the context outdated or incorrect. These are useful for evaluating a model's
+ability to detect temporal mismatches between query time and evidence time.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("Catkamakura/ts-qa")
+
+# Access splits
+train = dataset["train"]
+validation = dataset["validation"]
+test = dataset["test"]
+
+# Example
+example = train[0]
+print(example["question"])
+# "Today is Tuesday, September 24, 2013. Who was the Transport Minister of Thailand?"
+print(example["answers"])
+# ["Chadchart Sittipunt"]
+print(example["source"])
+# "streamingqa"
+
+# Filter by source
+streamingqa_examples = train.filter(lambda x: x["source"] == "streamingqa")
+timeqa_examples = train.filter(lambda x: x["source"] == "timeqa")
+
+# Filter hard negatives
+hard_negatives = train.filter(lambda x: x["is_hard_negative"])
+```
+
+## Citation
+
+If you use this dataset, please cite the original sources:
+
+```bibtex
+@inproceedings{liska2022streamingqa,
+  title={StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models},
+  author={Liska, Adam and Kocisky, Tomas and Gribovskaya, Elena and Terber, Tayfun and Shutova, Ekaterina and Glaese, Amelia},
+  booktitle={International Conference on Machine Learning},
+  pages={13604--13622},
+  year={2022},
+  organization={PMLR}
+}
+
+@inproceedings{chen2021timeqa,
+  title={A Dataset for Answering Time-Sensitive Questions},
+  author={Chen, Wenhu and Wang, Xinyi and Wang, William Yang},
+  booktitle={NeurIPS 2021 Datasets and Benchmarks Track},
+  year={2021}
+}
+```
+
+## License
+
+MIT License