Fix yield for crd3 (#4240)

* yielding example per chunk id

* changing data type for turns

* removing unused variable

* Update crd3.py

Co-authored-by: Shanya Sharma - s0s0cr3 <Shanya.Sharma@walmartlabs.com>

Commit from https://github.com/huggingface/datasets/commit/23efe55f5547c640f9efdcb2bc678fb7b76e663e

crd3.py

@@ -74,13 +74,13 @@ class CRD3(datasets.GeneratorBasedBuilder):
                     "turn_start": datasets.Value("int32"),
                     "turn_end": datasets.Value("int32"),
                     "alignment_score": datasets.Value("float32"),
-                    "turn_num": datasets.Value("int32"),
-                    "turns": datasets.features.Sequence(
+                    "turns": [
                         {
-                            "names": datasets.Value("string"),
-                            "utterances": datasets.Value("string"),
+                            "names": datasets.features.Sequence(datasets.Value("string")),
+                            "utterances": datasets.features.Sequence(datasets.Value("string")),
+                            "number": datasets.Value("int32"),
                         }
-                    ),
+                    ],
                 }
             ),
             homepage="https://github.com/RevanthRameshkumar/CRD3",
@@ -135,19 +135,20 @@ class CRD3(datasets.GeneratorBasedBuilder):
                     turn_start = row["ALIGNMENT"]["TURN START"]
                     turn_end = row["ALIGNMENT"]["TURN END"]
                     score = row["ALIGNMENT"]["ALIGNMENT SCORE"]
-                    for id2, turn in enumerate(row["TURNS"]):
-                        turn_names = turn["NAMES"]
-                        turn_utterances = turn["UTTERANCES"]
-                        turn_num = turn["NUMBER"]
-                        yield str(id0) + "_" + str(id1) + "_" + str(id2), {
-                            "chunk": chunk,
-                            "chunk_id": chunk_id,
-                            "turn_start": turn_start,
-                            "turn_end": turn_end,
-                            "alignment_score": score,
-                            "turn_num": turn_num,
-                            "turns": {
-                                "names": turn_names,
-                                "utterances": turn_utterances,
-                            },
-                        }
+                    for turn in row["TURNS"]:
+                        turn["names"] = turn["NAMES"]
+                        turn["utterances"] = turn["UTTERANCES"]
+                        turn["number"] = turn["NUMBER"]
+
+                        del turn["NAMES"]
+                        del turn["UTTERANCES"]
+                        del turn["NUMBER"]
+
+                    yield str(id0) + "_" + str(id1), {
+                        "chunk": chunk,
+                        "chunk_id": chunk_id,
+                        "turn_start": turn_start,
+                        "turn_end": turn_end,
+                        "alignment_score": score,
+                        "turns": row["TURNS"],
+                    }

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "\nStorytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.\nCritical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.\nThe dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding\nabstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player\ncollaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail,\nand semantic ties to the previous dialogues.\n", "citation": "\n@inproceedings{\ntitle = {Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset},\nauthor = {Rameshkumar, Revanth  and Bailey, Peter},\nyear = {2020},\npublisher = {Association for Computational Linguistics},\nconference = {ACL}\n}\n ", "homepage": "https://github.com/RevanthRameshkumar/CRD3", "license": "", "features": {"chunk": {"dtype": "string", "id": null, "_type": "Value"}, "chunk_id": {"dtype": "int32", "id": null, "_type": "Value"}, "turn_start": {"dtype": "int32", "id": null, "_type": "Value"}, "turn_end": {"dtype": "int32", "id": null, "_type": "Value"}, "alignment_score": {"dtype": "float32", "id": null, "_type": "Value"}, "turn_num": {"dtype": "int32", "id": null, "_type": "Value"}, "turns": {"feature": {"names": {"dtype": "string", "id": null, "_type": "Value"}, "utterances": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "crd3", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1402558174, "num_examples": 2942362, "dataset_name": "crd3"}, "test": {"name": "test", "num_bytes": 1402558174, "num_examples": 2942362, "dataset_name": "crd3"}, "validation": {"name": "validation", "num_bytes": 1402558174, "num_examples": 2942362, "dataset_name": "crd3"}}, "download_checksums": {"https://github.com/RevanthRameshkumar/CRD3/archive/master.zip": {"num_bytes": 294222220, "checksum": "c77a937394f265735ba54b32a7a051f77a97d264c74b0535dee77ef9791815b5"}}, "download_size": 294222220, "post_processing_size": null, "dataset_size": 4207674522, "size_in_bytes": 4501896742}}
+{"default": {"description": "\nStorytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.\nCritical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.\nThe dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding\nabstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player\ncollaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail,\nand semantic ties to the previous dialogues.\n", "citation": "\n@inproceedings{\ntitle = {Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset},\nauthor = {Rameshkumar, Revanth  and Bailey, Peter},\nyear = {2020},\npublisher = {Association for Computational Linguistics},\nconference = {ACL}\n}\n ", "homepage": "https://github.com/RevanthRameshkumar/CRD3", "license": "", "features": {"chunk": {"dtype": "string", "id": null, "_type": "Value"}, "chunk_id": {"dtype": "int32", "id": null, "_type": "Value"}, "turn_start": {"dtype": "int32", "id": null, "_type": "Value"}, "turn_end": {"dtype": "int32", "id": null, "_type": "Value"}, "alignment_score": {"dtype": "float32", "id": null, "_type": "Value"}, "turns": {"feature": {"names": {"dtype": "string", "id": null, "_type": "Value"}, "utterances": {"dtype": "string", "id": null, "_type": "Value"}, "number": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "crd3", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 318560673, "num_examples": 52796, "dataset_name": "crd3"}, "test": {"name": "test", "num_bytes": 318560673, "num_examples": 52796, "dataset_name": "crd3"}, "validation": {"name": "validation", "num_bytes": 318560673, "num_examples": 52796, "dataset_name": "crd3"}}, "download_checksums": {"https://github.com/RevanthRameshkumar/CRD3/archive/master.zip": {"num_bytes": 294222220, "checksum": "c77a937394f265735ba54b32a7a051f77a97d264c74b0535dee77ef9791815b5"}}, "download_size": 294222220, "post_processing_size": null, "dataset_size": 955682019, "size_in_bytes": 1249904239}}