add round2

dataset_infos.json

@@ -1 +1 @@
-{"dynabench.dynasent.r1.all": {"description": "DynaSent is an English-language benchmark task for ternary\n            (positive/negative/neutral) sentiment analysis.\n            For more details on the dataset construction process,\n            see https://github.com/cgpotts/dynasent.", "citation": "@article{\n                potts-etal-2020-dynasent,\n                title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis},\n                author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus\n                        and Kiela, Douwe},\n                journal={arXiv preprint arXiv:2012.15349},\n                url={https://arxiv.org/abs/2012.15349},\n                year={2020}\n            }", "homepage": "https://dynabench.org/tasks/3", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "hit_ids": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "indices_into_review_text": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "model_0_label": {"dtype": "string", "id": null, "_type": "Value"}, "model_0_probs": {"negative": {"dtype": "float32", "id": null, "_type": "Value"}, "positive": {"dtype": "float32", "id": null, "_type": "Value"}, "neutral": {"dtype": "float32", "id": null, "_type": "Value"}}, "text_id": {"dtype": "string", "id": null, "_type": "Value"}, "review_id": {"dtype": "string", "id": null, "_type": "Value"}, "review_rating": {"dtype": "int32", "id": null, "_type": "Value"}, "label_distribution": {"positive": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "negative": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "neutral": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "mixed": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "gold_label": {"dtype": "string", "id": null, "_type": "Value"}, "metadata": {"split": {"dtype": "string", "id": null, "_type": "Value"}, "round": {"dtype": "int32", "id": null, "_type": "Value"}, "subset": {"dtype": "string", "id": null, "_type": "Value"}, "model_in_the_loop": {"dtype": "string", "id": null, "_type": "Value"}}}, "post_processed": null, "supervised_keys": null, "builder_name": "dynabench_dyna_sent", "config_name": "dynabench.dynasent.r1.all", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 23007540, "num_examples": 80488, "dataset_name": "dynabench_dyna_sent"}, "validation": {"name": "validation", "num_bytes": 1057327, "num_examples": 3600, "dataset_name": "dynabench_dyna_sent"}, "test": {"name": "test", "num_bytes": 1035527, "num_examples": 3600, "dataset_name": "dynabench_dyna_sent"}}, "download_checksums": {"https://github.com/cgpotts/dynasent/raw/main/dynasent-v1.1.zip": {"num_bytes": 17051772, "checksum": "33001cf394618aa38f9530c43ca87072b92f5ee609a02afa2d168d25560cedfd"}}, "download_size": 17051772, "post_processing_size": null, "dataset_size": 25100394, "size_in_bytes": 42152166}}
+{"dynabench.dynasent.r1.all": {"description": "DynaSent is an English-language benchmark task for ternary\n            (positive/negative/neutral) sentiment analysis.\n            For more details on the dataset construction process,\n            see https://github.com/cgpotts/dynasent.", "citation": "@article{\n                potts-etal-2020-dynasent,\n                title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis},\n                author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus\n                        and Kiela, Douwe},\n                journal={arXiv preprint arXiv:2012.15349},\n                url={https://arxiv.org/abs/2012.15349},\n                year={2020}\n            }", "homepage": "https://dynabench.org/tasks/3", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "hit_ids": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "indices_into_review_text": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "model_0_label": {"dtype": "string", "id": null, "_type": "Value"}, "model_0_probs": {"negative": {"dtype": "float32", "id": null, "_type": "Value"}, "positive": {"dtype": "float32", "id": null, "_type": "Value"}, "neutral": {"dtype": "float32", "id": null, "_type": "Value"}}, "text_id": {"dtype": "string", "id": null, "_type": "Value"}, "review_id": {"dtype": "string", "id": null, "_type": "Value"}, "review_rating": {"dtype": "int32", "id": null, "_type": "Value"}, "label_distribution": {"positive": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "negative": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "neutral": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "mixed": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "gold_label": {"dtype": "string", "id": null, "_type": "Value"}, "metadata": {"split": {"dtype": "string", "id": null, "_type": "Value"}, "round": {"dtype": "int32", "id": null, "_type": "Value"}, "subset": {"dtype": "string", "id": null, "_type": "Value"}, "model_in_the_loop": {"dtype": "string", "id": null, "_type": "Value"}}}, "post_processed": null, "supervised_keys": null, "builder_name": "dynabench_dyna_sent", "config_name": "dynabench.dynasent.r1.all", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 23007540, "num_examples": 80488, "dataset_name": "dynabench_dyna_sent"}, "validation": {"name": "validation", "num_bytes": 1057327, "num_examples": 3600, "dataset_name": "dynabench_dyna_sent"}, "test": {"name": "test", "num_bytes": 1035527, "num_examples": 3600, "dataset_name": "dynabench_dyna_sent"}}, "download_checksums": {"https://github.com/cgpotts/dynasent/raw/main/dynasent-v1.1.zip": {"num_bytes": 17051772, "checksum": "33001cf394618aa38f9530c43ca87072b92f5ee609a02afa2d168d25560cedfd"}}, "download_size": 17051772, "post_processing_size": null, "dataset_size": 25100394, "size_in_bytes": 42152166}, "dynabench.dynasent.r2.all": {"description": "DynaSent is an English-language benchmark task for ternary\n            (positive/negative/neutral) sentiment analysis.\n            For more details on the dataset construction process,\n            see https://github.com/cgpotts/dynasent.", "citation": "@article{\n                potts-etal-2020-dynasent,\n                title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis},\n                author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus\n                        and Kiela, Douwe},\n                journal={arXiv preprint arXiv:2012.15349},\n                url={https://arxiv.org/abs/2012.15349},\n                year={2020}\n            }", "homepage": "https://dynabench.org/tasks/3", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "hit_ids": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_author": {"dtype": "string", "id": null, "_type": "Value"}, "has_prompt": {"dtype": "bool", "id": null, "_type": "Value"}, "prompt_data": {"indices_into_review_text": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "review_rating": {"dtype": "int32", "id": null, "_type": "Value"}, "prompt_sentence": {"dtype": "string", "id": null, "_type": "Value"}, "review_id": {"dtype": "string", "id": null, "_type": "Value"}}, "model_1_label": {"dtype": "string", "id": null, "_type": "Value"}, "model_1_probs": {"negative": {"dtype": "float32", "id": null, "_type": "Value"}, "positive": {"dtype": "float32", "id": null, "_type": "Value"}, "neutral": {"dtype": "float32", "id": null, "_type": "Value"}}, "text_id": {"dtype": "string", "id": null, "_type": "Value"}, "label_distribution": {"positive": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "negative": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "neutral": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "mixed": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "gold_label": {"dtype": "string", "id": null, "_type": "Value"}, "metadata": {"split": {"dtype": "string", "id": null, "_type": "Value"}, "round": {"dtype": "int32", "id": null, "_type": "Value"}, "subset": {"dtype": "string", "id": null, "_type": "Value"}, "model_in_the_loop": {"dtype": "string", "id": null, "_type": "Value"}}}, "post_processed": null, "supervised_keys": null, "builder_name": "dynabench_dyna_sent", "config_name": "dynabench.dynasent.r2.all", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4604051, "num_examples": 13065, "dataset_name": "dynabench_dyna_sent"}, "validation": {"name": "validation", "num_bytes": 264059, "num_examples": 720, "dataset_name": "dynabench_dyna_sent"}, "test": {"name": "test", "num_bytes": 259782, "num_examples": 720, "dataset_name": "dynabench_dyna_sent"}}, "download_checksums": {"https://github.com/cgpotts/dynasent/raw/main/dynasent-v1.1.zip": {"num_bytes": 17051772, "checksum": "33001cf394618aa38f9530c43ca87072b92f5ee609a02afa2d168d25560cedfd"}}, "download_size": 17051772, "post_processing_size": null, "dataset_size": 5127892, "size_in_bytes": 22179664}}

dynasent.py

@@ -23,7 +23,7 @@ from collections import OrderedDict
 import datasets
 logger = datasets.logging.get_logger(__name__)
 _VERSION = datasets.Version("1.1.0") # v1.1 fixed for example uid.
-_NUM_ROUNDS = 1
+_NUM_ROUNDS = 2
 _DESCRIPTION = """\
     Dynabench.DynaSent is a Sentiment Analysis dataset collected using a 
     human-and-model-in-the-loop.
@@ -115,6 +115,82 @@ _ROUND_DETAILS = {
                 "model": "RoBERTa"
             }
         }),
+    ),
+    2: DynabenchRoundDetails(
+        citation="""\
+            @article{
+                potts-etal-2020-dynasent,
+                title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis},
+                author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus
+                        and Kiela, Douwe},
+                journal={arXiv preprint arXiv:2012.15349},
+                url={https://arxiv.org/abs/2012.15349},
+                year={2020}
+            }
+            """.strip(),
+        description="""\
+            DynaSent is an English-language benchmark task for ternary
+            (positive/negative/neutral) sentiment analysis.
+            For more details on the dataset construction process,
+            see https://github.com/cgpotts/dynasent.
+            """.strip(),
+        homepage="https://dynabench.org/tasks/3",
+        data_license="CC BY 4.0",
+        data_url="https://github.com/cgpotts/dynasent/raw/main/dynasent-v1.1.zip",
+        data_features=datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "hit_ids": datasets.features.Sequence(
+                    datasets.Value("string")
+                ),
+                "sentence": datasets.Value("string"),
+                "sentence_author": datasets.Value("string"),
+                "has_prompt": datasets.Value("bool"),
+                "prompt_data": {
+                    "indices_into_review_text": datasets.features.Sequence(
+                        datasets.Value("int32")
+                    ),
+                    "review_rating": datasets.Value("int32"), 
+                    "prompt_sentence": datasets.Value("string"), 
+                    "review_id": datasets.Value("string")
+                },
+                "model_1_label": datasets.Value("string"),
+                "model_1_probs": {
+                        "negative": datasets.Value("float32"),
+                        "positive": datasets.Value("float32"),
+                        "neutral": datasets.Value("float32")
+                },
+                "text_id": datasets.Value("string"),
+                "label_distribution": {
+                    "positive": datasets.features.Sequence(
+                        datasets.Value("string")
+                    ),
+                    "negative": datasets.features.Sequence(
+                        datasets.Value("string")
+                    ),
+                    "neutral": datasets.features.Sequence(
+                        datasets.Value("string")
+                    ),
+                    "mixed": datasets.features.Sequence(
+                        datasets.Value("string")
+                    )
+                },
+                "gold_label": datasets.Value("string"),
+                "metadata": {
+                    "split": datasets.Value("string"),
+                    "round": datasets.Value("int32"),
+                    "subset": datasets.Value("string"),
+                    "model_in_the_loop": datasets.Value("string"),
+                }
+            }
+        ),
+        data_subset_map=OrderedDict({
+            "all": {
+                "dir": "dynasent-v1.1",
+                "file_prefix": "dynasent-v1.1-round02-dynabench-", 
+                "model": "RoBERTa"
+            }
+        }),
     )
 }
 
@@ -221,25 +297,79 @@ class DynabenchDynaSent(datasets.GeneratorBasedBuilder):
             for line in f:
                 d = json.loads(line)
                 if d['gold_label'] in ternary_labels:
-                    # Construct DynaSent features.
-                    yield d["text_id"], {
-                        "id": d["text_id"],
-                        # DynaSent Example.
-                        "hit_ids": d["hit_ids"],
-                        "sentence": d["sentence"],
-                        "indices_into_review_text": d["indices_into_review_text"],
-                        "model_0_label": d["model_0_label"],
-                        "model_0_probs": d["model_0_probs"],
-                        "text_id": d["text_id"],
-                        "review_id": d["review_id"],
-                        "review_rating": d["review_rating"],
-                        "label_distribution": d["label_distribution"],
-                        "gold_label": d["gold_label"],
-                        # Metadata.
-                        "metadata": {
-                            "split": split,
-                            "round": round,
-                            "subset": subset,
-                            "model_in_the_loop": model_in_the_loop
-                        },
-                    }
+                    if round == 1:
+                        # Construct DynaSent features.
+                        yield d["text_id"], {
+                            "id": d["text_id"],
+                            # DynaSent Example.
+                            "hit_ids": d["hit_ids"],
+                            "sentence": d["sentence"],
+                            "indices_into_review_text": d["indices_into_review_text"],
+                            "model_0_label": d["model_0_label"],
+                            "model_0_probs": d["model_0_probs"],
+                            "text_id": d["text_id"],
+                            "review_id": d["review_id"],
+                            "review_rating": d["review_rating"],
+                            "label_distribution": d["label_distribution"],
+                            "gold_label": d["gold_label"],
+                            # Metadata.
+                            "metadata": {
+                                "split": split,
+                                "round": round,
+                                "subset": subset,
+                                "model_in_the_loop": model_in_the_loop
+                            }
+                        }
+                    elif round == 2:
+                        # Construct DynaSent features.
+                        if d["has_prompt"]:
+                            if "indices_into_review_text" in d["prompt_data"]:
+                                indices_into_review_text = d["prompt_data"]["indices_into_review_text"]
+                            else:
+                                indices_into_review_text = []
+                            if "review_rating" in d["prompt_data"]:
+                                review_rating = d["prompt_data"]["review_rating"]
+                            else:
+                                review_rating = -1 # -1 means unknown.
+                            if "review_id" in d["prompt_data"]:
+                                review_id = d["prompt_data"]["review_id"]
+                            else:
+                                review_id = ""
+                            if "prompt_sentence" in d["prompt_data"]:
+                                prompt_sentence = d["prompt_data"]["prompt_sentence"]
+                            else:
+                                prompt_sentence = ""                  
+                            prompt_data = {
+                                "indices_into_review_text": indices_into_review_text,
+                                "review_rating": review_rating,
+                                "prompt_sentence": prompt_sentence,
+                                "review_id": review_id,
+                            }
+                        else:
+                            prompt_data = {
+                                "indices_into_review_text": [],
+                                "review_rating": -1, # -1 means unknown.
+                                "prompt_sentence": "",
+                                "review_id": "",
+                            }
+                        yield d["text_id"], {
+                            "id": d["text_id"],
+                            # DynaSent Example.
+                            "hit_ids": d["hit_ids"],
+                            "sentence": d["sentence"],
+                            "sentence_author": d["sentence_author"],
+                            "has_prompt": d["has_prompt"],
+                            "prompt_data": prompt_data,
+                            "model_1_label": d["model_1_label"],
+                            "model_1_probs": d["model_1_probs"],
+                            "text_id": d["text_id"],
+                            "label_distribution": d["label_distribution"],
+                            "gold_label": d["gold_label"],
+                            # Metadata.
+                            "metadata": {
+                                "split": split,
+                                "round": round,
+                                "subset": subset,
+                                "model_in_the_loop": model_in_the_loop
+                            }
+                        }