Support streaming

README.md

@@ -37,13 +37,13 @@ dataset_info:
     dtype: bool
   splits:
   - name: train
-    num_bytes: 3656881376
+    num_bytes: 3656865072
     num_examples: 20377568
   - name: validation
-    num_bytes: 168005155
+    num_bytes: 168004403
     num_examples: 930062
-  download_size: 3563857920
-  dataset_size: 3824886531
+  download_size: 1482064429
+  dataset_size: 3824869475
 ---
 
 # Dataset Card for "asnq"

asnq.py

@@ -59,7 +59,7 @@ and
 https://research.google/pubs/pub47761/
 """
 
-_URL = "https://d3t7erp6ge410c.cloudfront.net/tanda-aaai-2020/data/asnq.tar"
+_URL = "data/asnq.zip"
 
 
 class ASNQ(datasets.GeneratorBasedBuilder):

data/asnq.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d1b3ee889be3d2a090d075a7494cee339f6fcfae6bc16ce5d06ad66874e591c
+size 1482064429

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "ASNQ is a dataset for answer sentence selection derived from\nGoogle's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).\n\nEach example contains a question, candidate sentence, label indicating whether or not\nthe sentence answers the question, and two additional features -- \nsentence_in_long_answer and short_answer_in_sentence indicating whether ot not the \ncandidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.\n\nFor more details please see \nhttps://arxiv.org/pdf/1911.04118.pdf\n\nand \n\nhttps://research.google/pubs/pub47761/\n", "citation": "@article{garg2019tanda,\n    title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},\n    author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},\n    year={2019},\n    eprint={1911.04118},\n}\n", "homepage": "https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["neg", "pos"], "names_file": null, "id": null, "_type": "ClassLabel"}, "sentence_in_long_answer": {"dtype": "bool", "id": null, "_type": "Value"}, "short_answer_in_sentence": {"dtype": "bool", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "asnq", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3656881376, "num_examples": 20377568, "dataset_name": "asnq"}, "validation": {"name": "validation", "num_bytes": 168005155, "num_examples": 930062, "dataset_name": "asnq"}}, "download_checksums": {"https://wqa-public.s3.amazonaws.com/tanda-aaai-2020/data/asnq.tar": {"num_bytes": 3563857920, "checksum": "4211d3e507e7cfa345a9eea3c5222b7d79fd963cf27407555c5558c37344ddf1"}}, "download_size": 3563857920, "post_processing_size": null, "dataset_size": 3824886531, "size_in_bytes": 7388744451}}
+{"default": {"description": "ASNQ is a dataset for answer sentence selection derived from\nGoogle's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).\n\nEach example contains a question, candidate sentence, label indicating whether or not\nthe sentence answers the question, and two additional features --\nsentence_in_long_answer and short_answer_in_sentence indicating whether ot not the\ncandidate sentence is contained in the long_answer and if the short_answer is in the candidate sentence.\n\nFor more details please see\nhttps://arxiv.org/pdf/1911.04118.pdf\n\nand\n\nhttps://research.google/pubs/pub47761/\n", "citation": "@article{garg2019tanda,\n    title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},\n    author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},\n    year={2019},\n    eprint={1911.04118},\n}\n", "homepage": "https://github.com/alexa/wqa_tanda#answer-sentence-natural-questions-asnq", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["neg", "pos"], "id": null, "_type": "ClassLabel"}, "sentence_in_long_answer": {"dtype": "bool", "id": null, "_type": "Value"}, "short_answer_in_sentence": {"dtype": "bool", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "asnq", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3656865072, "num_examples": 20377568, "dataset_name": "asnq"}, "validation": {"name": "validation", "num_bytes": 168004403, "num_examples": 930062, "dataset_name": "asnq"}}, "download_checksums": {"data/asnq.zip": {"num_bytes": 1482064429, "checksum": "4d1b3ee889be3d2a090d075a7494cee339f6fcfae6bc16ce5d06ad66874e591c"}}, "download_size": 1482064429, "post_processing_size": null, "dataset_size": 3824869475, "size_in_bytes": 5306933904}}