Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+task_ids:
+- conditional-text-generation-other-dialogue-generation
+---
+
+
+# Dataset Card for Deal or No Deal Negotiator
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Repository:** [Dataset Repository](https://github.com/facebookresearch/end-to-end-negotiator) 
+- **Paper:** [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://arxiv.org/abs/1706.05125)
+
+### Dataset Summary
+
+A large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach an agreement (or a deal) via natural language dialogue.
+
+### Supported Tasks and Leaderboards
+
+Train end-to-end models for negotiation
+
+### Languages
+
+The text in the dataset is in English
+
+## Dataset Structure
+
+### Data Instances
+
+{'dialogue': 'YOU: i love basketball and reading <eos> THEM: no . i want the hat and the balls <eos> YOU: both balls ? <eos> THEM: yeah or 1 ball and 1 book <eos> YOU: ok i want the hat and you can have the rest <eos> THEM: okay deal ill take the books and the balls you can have only the hat <eos> YOU: ok <eos> THEM: <selection>',
+ 'input': {'count': [3, 1, 2], 'value': [0, 8, 1]},
+ 'output': 'item0=0 item1=1 item2=0 item0=3 item1=0 item2=2',
+ 'partner_input': {'count': [3, 1, 2], 'value': [1, 3, 2]}}
+
+### Data Fields
+
+`dialogue`: The dialogue between the agents. \
+`input`: The input of the firt agent. \
+`partner_input`: The input of the other agent. \
+`count`: The count of the three available items. \
+`value`: The value of the three available items. \
+`output`: Describes how many of each of the three item typesare assigned to each agent
+ 
+
+### Data Splits
+
+|           | Tain   | Valid | Test |
+| -----     | ------ | ----- | ---- |
+| dialogues |  10095 |  1087 | 1052 |
+| self_play |   8172 |  NA   | NA   |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+Human workers using Amazon Mechanical Turk. They were paid $0.15 per dialogue, with a $0.05 bonus for maximal scores. Only workers based in the United States with a 95% approval rating and at least 5000 previous HITs were used.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+The project is licenced under CC-by-NC
+
+### Citation Information
+```
+@article{lewis2017deal,
+  title={Deal or no deal? end-to-end learning for negotiation dialogues},
+  author={Lewis, Mike and Yarats, Denis and Dauphin, Yann N and Parikh, Devi and Batra, Dhruv},
+  journal={arXiv preprint arXiv:1706.05125},
+  year={2017}
+}
+```

dataset_infos.json

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+{"dialogues": {"description": "A large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other\u2019s reward functions must reach anagreement (o a deal) via natural language dialogue.\n", "citation": "@article{lewis2017deal,\n  title={Deal or no deal? end-to-end learning for negotiation dialogues},\n  author={Lewis, Mike and Yarats, Denis and Dauphin, Yann N and Parikh, Devi and Batra, Dhruv},\n  journal={arXiv preprint arXiv:1706.05125},\n  year={2017}\n}\n", "homepage": "https://github.com/facebookresearch/end-to-end-negotiator", "license": "The project is licenced under CC-by-NC", "features": {"input": {"feature": {"count": {"dtype": "int32", "id": null, "_type": "Value"}, "value": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "dialogue": {"dtype": "string", "id": null, "_type": "Value"}, "output": {"dtype": "string", "id": null, "_type": "Value"}, "partner_input": {"feature": {"count": {"dtype": "int32", "id": null, "_type": "Value"}, "value": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "deal_or_no_dialog", "config_name": "dialogues", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3860624, "num_examples": 10095, "dataset_name": "deal_or_no_dialog"}, "test": {"name": "test", "num_bytes": 396258, "num_examples": 1052, "dataset_name": "deal_or_no_dialog"}, "validation": {"name": "validation", "num_bytes": 418491, "num_examples": 1087, "dataset_name": "deal_or_no_dialog"}}, "download_checksums": {"https://raw.githubusercontent.com/facebookresearch/end-to-end-negotiator/master/src/data/negotiate/train.txt": {"num_bytes": 4325861, "checksum": "aa278f06765463a5767055e1a544d922b39a9bb78f586f85513bf99f74aa2300"}, "https://raw.githubusercontent.com/facebookresearch/end-to-end-negotiator/master/src/data/negotiate/test.txt": {"num_bytes": 444703, "checksum": "37be3150bf656195b61a7547b45cf307acce929f2a8140036890a561c3597c83"}, "https://raw.githubusercontent.com/facebookresearch/end-to-end-negotiator/master/src/data/negotiate/val.txt": {"num_bytes": 468508, "checksum": "5ad7df00b8bc4b1fff565cb4dc38b23afc7874c496b0ed2e1c58c232326ff996"}}, "download_size": 5239072, "post_processing_size": null, "dataset_size": 4675373, "size_in_bytes": 9914445}, "self_play": {"description": "A large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other\u2019s reward functions must reach anagreement (o a deal) via natural language dialogue.\n", "citation": "@article{lewis2017deal,\n  title={Deal or no deal? end-to-end learning for negotiation dialogues},\n  author={Lewis, Mike and Yarats, Denis and Dauphin, Yann N and Parikh, Devi and Batra, Dhruv},\n  journal={arXiv preprint arXiv:1706.05125},\n  year={2017}\n}\n", "homepage": "https://github.com/facebookresearch/end-to-end-negotiator", "license": "The project is licenced under CC-by-NC", "features": {"input": {"feature": {"count": {"dtype": "int32", "id": null, "_type": "Value"}, "value": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "deal_or_no_dialog", "config_name": "self_play", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 261512, "num_examples": 8172, "dataset_name": "deal_or_no_dialog"}}, "download_checksums": {"https://raw.githubusercontent.com/facebookresearch/end-to-end-negotiator/master/src/data/negotiate/selfplay.txt": {"num_bytes": 98304, "checksum": "05b7d66c309617f0f1a5562ab8c1d2de933e712a4c8419fd924f4a2c899ab3aa"}}, "download_size": 98304, "post_processing_size": null, "dataset_size": 261512, "size_in_bytes": 359816}}

deal_or_no_dialog.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Deal or no deal negotiator"""
+
+from __future__ import absolute_import, division, print_function
+
+import datasets
+
+
+_CITATION = """\
+@article{lewis2017deal,
+  title={Deal or no deal? end-to-end learning for negotiation dialogues},
+  author={Lewis, Mike and Yarats, Denis and Dauphin, Yann N and Parikh, Devi and Batra, Dhruv},
+  journal={arXiv preprint arXiv:1706.05125},
+  year={2017}
+}
+"""
+
+_DESCRIPTION = """\
+A large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach anagreement (o a deal) via natural language dialogue.
+"""
+
+_HOMEPAGE = "https://github.com/facebookresearch/end-to-end-negotiator"
+
+_LICENSE = "The project is licenced under CC-by-NC"
+
+_URLs = {
+    "train": "https://raw.githubusercontent.com/facebookresearch/end-to-end-negotiator/master/src/data/negotiate/train.txt",
+    "test": "https://raw.githubusercontent.com/facebookresearch/end-to-end-negotiator/master/src/data/negotiate/test.txt",
+    "val": "https://raw.githubusercontent.com/facebookresearch/end-to-end-negotiator/master/src/data/negotiate/val.txt",
+    "selfplay": "https://raw.githubusercontent.com/facebookresearch/end-to-end-negotiator/master/src/data/negotiate/selfplay.txt",
+}
+
+
+class DealOrNoDialog(datasets.GeneratorBasedBuilder):
+    """Deal or no deal negotiator"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="dialogues",
+            description="Consists of 5808 dialogues, based on 2236 unique scenarios.",
+            version=VERSION,
+        ),
+        datasets.BuilderConfig(
+            name="self_play", description="Count and values with no dialogues. Used for self playing.", version=VERSION
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "dialogues"
+
+    def _info(self):
+        if self.config.name == "dialogues":
+            features = datasets.Features(
+                {
+                    "input": datasets.Sequence({"count": datasets.Value("int32"), "value": datasets.Value("int32")}),
+                    "dialogue": datasets.Value("string"),
+                    "output": datasets.Value("string"),
+                    "partner_input": datasets.Sequence(
+                        {"count": datasets.Value("int32"), "value": datasets.Value("int32")}
+                    ),
+                }
+            )
+        else:  # self_play
+            features = datasets.Features(
+                {
+                    "input": datasets.Sequence({"count": datasets.Value("int32"), "value": datasets.Value("int32")}),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        if self.config.name == "dialogues":
+            path_train = dl_manager.download_and_extract(_URLs["train"])
+            path_test = dl_manager.download_and_extract(_URLs["test"])
+            path_val = dl_manager.download_and_extract(_URLs["val"])
+
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": path_train,
+                        "split": "train",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"filepath": path_test, "split": "test"},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": path_val,
+                        "split": "val",
+                    },
+                ),
+            ]
+
+        else:
+            path = dl_manager.download_and_extract(_URLs["selfplay"])
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": path,
+                        "split": "train",
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, filepath, split="train"):
+        """ Yields examples. """
+        if self.config.name == "dialogues":
+            with open(filepath, encoding="utf-8") as f:
+                for idx, line in enumerate(f):
+                    tokens = line.split()
+
+                    yield idx, {
+                        "input": {
+                            "count": get_count_value(get_tag(tokens, "input"))[0],
+                            "value": get_count_value(get_tag(tokens, "input"))[1],
+                        },
+                        "dialogue": get_tag(tokens, "dialogue"),
+                        "output": get_tag(tokens, "output"),
+                        "partner_input": {
+                            "count": get_count_value(get_tag(tokens, "partner_input"))[0],
+                            "value": get_count_value(get_tag(tokens, "partner_input"))[1],
+                        },
+                    }
+
+        else:
+            with open(filepath, encoding="utf-8") as f:
+                for idx, line in enumerate(f):
+                    yield idx, {"input": {"count": get_count_value(line)[0], "value": get_count_value(line)[1]}}
+
+
+def get_tag(tokens, tag):
+    return " ".join(tokens[tokens.index("<" + tag + ">") + 1 : tokens.index("</" + tag + ">")])
+
+
+def get_count_value(sequence):
+    seq_list = [int(el) for el in sequence.split()]
+    assert len(seq_list) == 6
+    return [seq_list[idx] for idx in [0, 2, 4]], [seq_list[idx] for idx in [1, 3, 5]]

dummy/dialogues/1.1.0/dummy_data.zip

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dummy/self_play/1.1.0/dummy_data.zip

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