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:
+- found
+languages:
+- en
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- question-answering-other-generative-reading-comprehension-metric
+---
+
+# Dataset Card for Mocha
+
+## Table of Contents
+
+- [Dataset Card for Mocha](#dataset-card-for-mocha)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [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
+
+- **Homepage:[Mocha](https://allennlp.org/mocha)**
+- **Repository:[https://github.com/anthonywchen/MOCHA](https://github.com/anthonywchen/MOCHA)**
+- **Paper:[MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://www.aclweb.org/anthology/2020.emnlp-main.528/)**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, we train a Learned Evaluation metric for Reading Comprehension, LERC, to mimic human judgement scores. LERC outperforms baseline metrics by 10 to 36 absolute Pearson points on held-out annotations. When we evaluate robustness on minimal pairs, LERC achieves 80% accuracy, outperforming baselines by 14 to 26 absolute percentage points while leaving significant room for improvement. MOCHA presents a challenging problem for developing accurate and robust generative reading comprehension metrics.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. MOCHA pairs reading comprehension instances, which consists of a passage, question, and reference, with candidates and human judgement scores.
+
+### Data Fields
+
+- `constituent_dataset`: the original QA dataset which the data instance came from.
+- `id`
+- `context`: the passage content.
+- `question`: the question related to the passage content.
+- `reference`: the correct answer for the question.
+- `candidate`: the answer generated from the `reference` by `source`
+- `score`: the human judgement score for the `candidate`. Not included in test split, defaults to `-1`
+- `metadata`: Not included in minimal pairs split.
+  - `scores`: list of scores from difference judges, averaged out to get final `score`. defaults to `[]`
+  - `source`: the generative model to generate the `candidate`
+
+In minimal pairs, we'll have an additional candidate for robust evaluation.
+
+- `candidate2`
+- `score2`
+
+### Data Splits
+
+Dataset Split | Number of Instances in Split
+--------------|--------------------------------------------
+Train | 31,069
+Validation | 4,009
+Test | 6,321
+Minimal Pairs | 200
+
+## 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?
+
+[More Information Needed]
+
+### 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
+
+[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
+
+### Citation Information
+
+```bitex
+@inproceedings{Chen2020MOCHAAD,
+    author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
+    title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics},
+    booktitle={EMNLP},
+    year={2020}
+}
+```

dataset_infos.json

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+{"default": {"description": "Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, we train an evaluation metric: LERC, a Learned Evaluation metric for Reading Comprehension, to mimic human judgement scores.\n", "citation": "@inproceedings{Chen2020MOCHAAD,\n    author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner},\n    title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics},\n    booktitle={EMNLP},\n    year={2020}\n}\n", "homepage": "https://allennlp.org/mocha", "license": "https://creativecommons.org/licenses/by-sa/4.0/legalcode", "features": {"constituent_dataset": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "reference": {"dtype": "string", "id": null, "_type": "Value"}, "candidate": {"dtype": "string", "id": null, "_type": "Value"}, "score": {"dtype": "float32", "id": null, "_type": "Value"}, "metadata": {"scores": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "source": {"dtype": "string", "id": null, "_type": "Value"}}, "candidate2": {"dtype": "string", "id": null, "_type": "Value"}, "score2": {"dtype": "float32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "mocha", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 33292592, "num_examples": 31069, "dataset_name": "mocha"}, "validation": {"name": "validation", "num_bytes": 4236883, "num_examples": 4009, "dataset_name": "mocha"}, "test": {"name": "test", "num_bytes": 6767409, "num_examples": 6321, "dataset_name": "mocha"}, "minimal_pairs": {"name": "minimal_pairs", "num_bytes": 193560, "num_examples": 200, "dataset_name": "mocha"}}, "download_checksums": {"https://github.com/anthonywchen/MOCHA/raw/main/data/mocha.tar.gz": {"num_bytes": 14452311, "checksum": "036a14c1ad2eb554b77d32051940cb2808f20557f5edcc24b658df703d3c4302"}}, "download_size": 14452311, "post_processing_size": null, "dataset_size": 44490444, "size_in_bytes": 58942755}}

dummy/0.0.0/dummy_data.zip

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+oid sha256:3ecc25b7e076917531e64ccc8a9afd5a97e8b934a0f904d99da885ec424d2615
+size 2523

mocha.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import logging
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{Chen2020MOCHAAD,
+    author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
+    title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics},
+    booktitle={EMNLP},
+    year={2020}
+}
+"""
+
+_DESCRIPTION = """\
+Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for \
+open-ended questions with few restrictions on possible answers. However, progress is impeded by existing \
+generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. \
+To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: \
+MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from \
+6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, \
+we train an evaluation metric: LERC, a Learned Evaluation metric for Reading Comprehension, to mimic human \
+judgement scores.
+"""
+
+_HOMEPAGE = "https://allennlp.org/mocha"
+
+_LICENSE = "https://creativecommons.org/licenses/by-sa/4.0/legalcode"
+
+_URL = "https://github.com/anthonywchen/MOCHA/raw/main/data/mocha.tar.gz"
+
+_MINIMAL_PAIRS_SPLIT = "minimal_pairs"
+
+SPLIT_FILENAMES = {
+    datasets.Split.TRAIN: "train.json",
+    datasets.Split.VALIDATION: "dev.json",
+    datasets.Split.TEST: "test_no_labels.json",
+    _MINIMAL_PAIRS_SPLIT: "minimal_pairs.json",
+}
+
+
+class Mocha(datasets.GeneratorBasedBuilder):
+    """MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "constituent_dataset": datasets.Value("string"),
+                    "id": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "reference": datasets.Value("string"),
+                    "candidate": datasets.Value("string"),
+                    "score": datasets.Value("float"),
+                    "metadata": {
+                        "scores": datasets.features.Sequence(datasets.Value("int32")),
+                        "source": datasets.Value("string"),
+                    },
+                    # features for minimal pairs
+                    "candidate2": datasets.Value("string"),
+                    "score2": datasets.Value("float"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_path = os.path.join(dl_manager.download_and_extract(_URL), "mocha")
+
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={"filepath": os.path.join(dl_path, SPLIT_FILENAMES[split]), "split": split},
+            )
+            for split in SPLIT_FILENAMES
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """This function returns the examples in the raw (text) form."""
+        logging.info("Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            mocha = json.load(f)
+            for constituent_dataset, samples in mocha.items():
+                for id_, sample in samples.items():
+                    sample["id"] = id_
+                    sample["constituent_dataset"] = constituent_dataset
+
+                    # Add default values
+                    if split == _MINIMAL_PAIRS_SPLIT:
+                        sample["candidate"] = sample["candidate1"]
+                        sample["score"] = sample["score1"]
+                        del sample["candidate1"], sample["score1"]
+                        sample["metadata"] = {"scores": [], "source": ""}
+                    else:
+                        if "score" not in sample:
+                            sample["score"] = -1.0
+                            sample["metadata"]["scores"] = []
+                        sample["candidate2"] = ""
+                        sample["score2"] = -1.0
+
+                    yield id_, sample