Fix NonMatchingSplitsSizesError (#3)

- Delete legacy dataset_infos.json (fcaa2122dfe722063ad01b19a4dc7adee543c9c0)
- Fix size of test split in dataset card (66809f999db39c5b260bb447a62844377fb2c854)

README.md

@@ -10,7 +10,6 @@ license:
 - apache-2.0
 multilinguality:
 - monolingual
-pretty_name: MultiDoc2Dial
 size_categories:
 - 10K<n<100K
 - 1K<n<10K
@@ -22,6 +21,11 @@ task_categories:
 task_ids:
 - open-domain-qa
 paperswithcode_id: multidoc2dial
+pretty_name: MultiDoc2Dial
+config_names:
+- dialogue_domain
+- document_domain
+- multidoc2dial
 dataset_info:
 - config_name: dialogue_domain
   features:
@@ -49,13 +53,13 @@ dataset_info:
       dtype: string
   splits:
   - name: train
-    num_bytes: 11700598
+    num_bytes: 11700558
     num_examples: 3474
   - name: validation
-    num_bytes: 2210378
+    num_bytes: 2210338
     num_examples: 661
-  download_size: 6451144
-  dataset_size: 13910976
+  download_size: 6868509
+  dataset_size: 13910896
 - config_name: document_domain
   features:
   - name: domain
@@ -102,10 +106,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 29378955
+    num_bytes: 29378879
     num_examples: 488
-  download_size: 6451144
-  dataset_size: 29378955
+  download_size: 6868509
+  dataset_size: 29378879
 - config_name: multidoc2dial
   features:
   - name: id
@@ -130,20 +134,16 @@ dataset_info:
     dtype: string
   splits:
   - name: validation
-    num_bytes: 24331976
+    num_bytes: 24331936
     num_examples: 4201
   - name: train
-    num_bytes: 126589982
+    num_bytes: 126589862
     num_examples: 21451
   - name: test
-    num_bytes: 33032
-    num_examples: 5
-  download_size: 6451144
-  dataset_size: 150954990
-config_names:
-- dialogue_domain
-- document_domain
-- multidoc2dial
+    num_bytes: 23026892
+    num_examples: 4094
+  download_size: 6868509
+  dataset_size: 173948690
 ---
 
 # Dataset Card for MultiDoc2Dial

dataset_infos.json

@@ -1 +0,0 @@
-{"dialogue_domain": {"description": "MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. \n", "citation": "@inproceedings{feng2021multidoc2dial,\n    title={MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents},\n    author={Feng, Song and Patel, Siva Sankalp and Wan, Hui and Joshi, Sachindra},\n    booktitle={EMNLP},\n    year={2021}\n}\n", "homepage": "https://doc2dial.github.io/multidoc2dial/", "license": "", "features": {"dial_id": {"dtype": "string", "id": null, "_type": "Value"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}, "turns": [{"turn_id": {"dtype": "int32", "id": null, "_type": "Value"}, "role": {"dtype": "string", "id": null, "_type": "Value"}, "da": {"dtype": "string", "id": null, "_type": "Value"}, "references": [{"id_sp": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}, "doc_id": {"dtype": "string", "id": null, "_type": "Value"}}], "utterance": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "multi_doc2dial", "config_name": "dialogue_domain", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 11700598, "num_examples": 3474, "dataset_name": "multi_doc2dial"}, "validation": {"name": "validation", "num_bytes": 2210378, "num_examples": 661, "dataset_name": "multi_doc2dial"}}, "download_checksums": {"https://doc2dial.github.io/multidoc2dial/file/multidoc2dial.zip": {"num_bytes": 6451144, "checksum": "a8051237dd3be50d81c06aca82ed5171716922e35f44bfa5b9c024f090903419"}}, "download_size": 6451144, "post_processing_size": null, "dataset_size": 13910976, "size_in_bytes": 20362120}, "document_domain": {"description": "MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. \n", "citation": "@inproceedings{feng2021multidoc2dial,\n    title={MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents},\n    author={Feng, Song and Patel, Siva Sankalp and Wan, Hui and Joshi, Sachindra},\n    booktitle={EMNLP},\n    year={2021}\n}\n", "homepage": "https://doc2dial.github.io/multidoc2dial/", "license": "", "features": {"domain": {"dtype": "string", "id": null, "_type": "Value"}, "doc_id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "doc_text": {"dtype": "string", "id": null, "_type": "Value"}, "spans": [{"id_sp": {"dtype": "string", "id": null, "_type": "Value"}, "tag": {"dtype": "string", "id": null, "_type": "Value"}, "start_sp": {"dtype": "int32", "id": null, "_type": "Value"}, "end_sp": {"dtype": "int32", "id": null, "_type": "Value"}, "text_sp": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "parent_titles": {"feature": {"id_sp": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "level": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "id_sec": {"dtype": "string", "id": null, "_type": "Value"}, "start_sec": {"dtype": "int32", "id": null, "_type": "Value"}, "text_sec": {"dtype": "string", "id": null, "_type": "Value"}, "end_sec": {"dtype": "int32", "id": null, "_type": "Value"}}], "doc_html_ts": {"dtype": "string", "id": null, "_type": "Value"}, "doc_html_raw": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "multi_doc2dial", "config_name": "document_domain", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 29378955, "num_examples": 488, "dataset_name": "multi_doc2dial"}}, "download_checksums": {"https://doc2dial.github.io/multidoc2dial/file/multidoc2dial.zip": {"num_bytes": 6451144, "checksum": "a8051237dd3be50d81c06aca82ed5171716922e35f44bfa5b9c024f090903419"}}, "download_size": 6451144, "post_processing_size": null, "dataset_size": 29378955, "size_in_bytes": 35830099}, "multidoc2dial": {"description": "MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. \n", "citation": "@inproceedings{feng2021multidoc2dial,\n    title={MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents},\n    author={Feng, Song and Patel, Siva Sankalp and Wan, Hui and Joshi, Sachindra},\n    booktitle={EMNLP},\n    year={2021}\n}\n", "homepage": "https://doc2dial.github.io/multidoc2dial/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "da": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "utterance": {"dtype": "string", "id": null, "_type": "Value"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "multi_doc2dial", "config_name": "multidoc2dial", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 24331976, "num_examples": 4201, "dataset_name": "multi_doc2dial"}, "train": {"name": "train", "num_bytes": 126589982, "num_examples": 21451, "dataset_name": "multi_doc2dial"}, "test": {"name": "test", "num_bytes": 33032, "num_examples": 5, "dataset_name": "multi_doc2dial"}}, "download_checksums": {"https://doc2dial.github.io/multidoc2dial/file/multidoc2dial.zip": {"num_bytes": 6451144, "checksum": "a8051237dd3be50d81c06aca82ed5171716922e35f44bfa5b9c024f090903419"}}, "download_size": 6451144, "post_processing_size": null, "dataset_size": 150954990, "size_in_bytes": 157406134}}