Datasets:
Tasks:
Text Retrieval
Sub-tasks:
document-retrieval
Languages:
English
Multilinguality:
monolingual
Size Categories:
10M<n<100M
| language: | |
| - en | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10M<n<100M | |
| task_categories: | |
| - text-retrieval | |
| task_ids: | |
| - document-retrieval | |
| language_bcp47: | |
| - en-US | |
| # TREC Cast 2019 | |
| [TREC Cast](http://www.treccast.ai) have released a document collection with topics and qrels of which a subset has been annotated such that it is suitable for multi-turn conversational search. | |
| ## Dataset statistics | |
| - # Passages: 38,426,252 | |
| - # Topics: 20 | |
| - # Queries: 173 | |
| ## Subsets | |
| ### CAR + MSMARCO Collection | |
| Together CAR and MSMARCO have a size of 6,13G, so downloading will take a while. You can use the collection as followed: | |
| ```python | |
| collection = load_dataset('trec-cast-2019-multi-turn', 'test_collection') | |
| ``` | |
| The collection has the following data format: | |
| ``` | |
| docno: str | |
| The document id format is [collection_id_paragraph_id] with collection id and paragraph id separated by an underscore. | |
| The collection ids are in the set: {MARCO, CAR}. E.g.: CAR_6869dee46ab12f0f7060874f7fc7b1c57d53144a | |
| text: str | |
| The content of the passage. | |
| ``` | |
| #### Sample | |
| Instead of using the entire data set, you can also download a sample set containing only 200,000 items: | |
| ```python | |
| collection = load_dataset('trec-cast-2019-multi-turn', 'test_collection_sample') | |
| ``` | |
| ### Topics | |
| You can get the topics as followed: | |
| ```python | |
| topics = load_dataset('trec-cast-2019-multi-turn', 'topics') | |
| ``` | |
| The topics have the following dataformat: | |
| ``` | |
| qid: str | |
| Query ID of the format "topicId_questionNumber" | |
| history: str[] | |
| A list of queries. It can be empty for the first question in a topic. | |
| query: str | |
| The query | |
| ``` | |
| ### Qrels | |
| You can get the qrels as followed: | |
| ```python | |
| qrels = load_dataset('trec-cast-2019-multi-turn', 'qrels') | |
| ``` | |
| The qrels have the following data format: | |
| ``` | |
| qid: str | |
| Query ID of the format "topicId_questionNumber" | |
| qrels: List[dict] | |
| A list of dictionaries with the keys 'docno' and 'relevance'. Relevance is an integer in the range [0, 4] | |
| ``` |