Update files from the datasets library (from 1.2.0)

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

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- en
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-retrieval
+task_ids:
+- fact-checking-retrieval
+---
+# Dataset Card Creation Guide
+
+## 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
+
+- **Homepage:** []()
+- **Repository:** [link]()
+- **Paper:** []()
+- **Leaderboard:** []()
+- **Point of Contact:** []()
+
+### Dataset Summary
+
+Data and code from our "Inferring Which Medical Treatments Work from Reports of Clinical Trials", NAACL 2019. This work concerns inferring the results reported in clinical trials from text.
+
+The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator.
+
+The dataset could be used for automatic data extraction of the results of a given RCT. This would enable readers to discover the effectiveness of different treatments without needing to read the paper.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+[More Information Needed]
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### 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
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@inproceedings{lehman-etal-2019-inferring,
+    title = "Inferring Which Medical Treatments Work from Reports of Clinical Trials",
+    author = "Lehman, Eric  and
+      DeYoung, Jay  and
+      Barzilay, Regina  and
+      Wallace, Byron C.",
+    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
+    month = jun,
+    year = "2019",
+    address = "Minneapolis, Minnesota",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/N19-1371",
+    pages = "3705--3717",
+}
+```

dataset_infos.json

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+{"2.0": {"description": "Data and code from our \"Inferring Which Medical Treatments Work from Reports of Clinical Trials\", NAACL 2019. This work concerns inferring the results reported in clinical trials from text.\n\nThe dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator.\n\nThe dataset could be used for automatic data extraction of the results of a given RCT. This would enable readers to discover the effectiveness of different treatments without needing to read the paper.\n", "citation": "@inproceedings{lehman-etal-2019-inferring,\n    title = \"Inferring Which Medical Treatments Work from Reports of Clinical Trials\",\n    author = \"Lehman, Eric  and\n      DeYoung, Jay  and\n      Barzilay, Regina  and\n      Wallace, Byron C.\",\n    booktitle = \"Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)\",\n    month = jun,\n    year = \"2019\",\n    address = \"Minneapolis, Minnesota\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/N19-1371\",\n    pages = \"3705--3717\",\n}\n", "homepage": "https://github.com/jayded/evidence-inference", "license": "", "features": {"Text": {"dtype": "string", "id": null, "_type": "Value"}, "PMCID": {"dtype": "int32", "id": null, "_type": "Value"}, "Prompts": {"feature": {"PromptID": {"dtype": "int32", "id": null, "_type": "Value"}, "PMCID": {"dtype": "int32", "id": null, "_type": "Value"}, "Outcome": {"dtype": "string", "id": null, "_type": "Value"}, "Intervention": {"dtype": "string", "id": null, "_type": "Value"}, "Comparator": {"dtype": "string", "id": null, "_type": "Value"}, "Annotations": {"feature": {"UserID": {"dtype": "int32", "id": null, "_type": "Value"}, "PromptID": {"dtype": "int32", "id": null, "_type": "Value"}, "PMCID": {"dtype": "int32", "id": null, "_type": "Value"}, "Valid Label": {"dtype": "bool", "id": null, "_type": "Value"}, "Valid Reasoning": {"dtype": "bool", "id": null, "_type": "Value"}, "Label": {"dtype": "string", "id": null, "_type": "Value"}, "Annotations": {"dtype": "string", "id": null, "_type": "Value"}, "Label Code": {"dtype": "int32", "id": null, "_type": "Value"}, "In Abstract": {"dtype": "bool", "id": null, "_type": "Value"}, "Evidence Start": {"dtype": "int32", "id": null, "_type": "Value"}, "Evidence End": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "evidence_infer_treatment", "config_name": "2.0", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 76408584, "num_examples": 2674, "dataset_name": "evidence_infer_treatment"}, "test": {"name": "test", "num_bytes": 9408156, "num_examples": 334, "dataset_name": "evidence_infer_treatment"}, "validation": {"name": "validation", "num_bytes": 10085622, "num_examples": 340, "dataset_name": "evidence_infer_treatment"}}, "download_checksums": {"http://evidence-inference.ebm-nlp.com/v2.0.tar.gz": {"num_bytes": 36528800, "checksum": "6abe0d4ec0d331834981c0171c3c79d47515761867f82f1dc6066e43863a1586"}}, "download_size": 36528800, "post_processing_size": null, "dataset_size": 95902362, "size_in_bytes": 132431162}, "1.1": {"description": "Data and code from our \"Inferring Which Medical Treatments Work from Reports of Clinical Trials\", NAACL 2019. This work concerns inferring the results reported in clinical trials from text.\n\nThe dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator.\n\nThe dataset could be used for automatic data extraction of the results of a given RCT. This would enable readers to discover the effectiveness of different treatments without needing to read the paper.\n", "citation": "@inproceedings{lehman-etal-2019-inferring,\n    title = \"Inferring Which Medical Treatments Work from Reports of Clinical Trials\",\n    author = \"Lehman, Eric  and\n      DeYoung, Jay  and\n      Barzilay, Regina  and\n      Wallace, Byron C.\",\n    booktitle = \"Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)\",\n    month = jun,\n    year = \"2019\",\n    address = \"Minneapolis, Minnesota\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/N19-1371\",\n    pages = \"3705--3717\",\n}\n", "homepage": "https://github.com/jayded/evidence-inference", "license": "", "features": {"Text": {"dtype": "string", "id": null, "_type": "Value"}, "PMCID": {"dtype": "int32", "id": null, "_type": "Value"}, "Prompts": {"feature": {"PromptID": {"dtype": "int32", "id": null, "_type": "Value"}, "PMCID": {"dtype": "int32", "id": null, "_type": "Value"}, "Outcome": {"dtype": "string", "id": null, "_type": "Value"}, "Intervention": {"dtype": "string", "id": null, "_type": "Value"}, "Comparator": {"dtype": "string", "id": null, "_type": "Value"}, "Annotations": {"feature": {"UserID": {"dtype": "int32", "id": null, "_type": "Value"}, "PromptID": {"dtype": "int32", "id": null, "_type": "Value"}, "PMCID": {"dtype": "int32", "id": null, "_type": "Value"}, "Valid Label": {"dtype": "bool", "id": null, "_type": "Value"}, "Valid Reasoning": {"dtype": "bool", "id": null, "_type": "Value"}, "Label": {"dtype": "string", "id": null, "_type": "Value"}, "Annotations": {"dtype": "string", "id": null, "_type": "Value"}, "Label Code": {"dtype": "int32", "id": null, "_type": "Value"}, "In Abstract": {"dtype": "bool", "id": null, "_type": "Value"}, "Evidence Start": {"dtype": "int32", "id": null, "_type": "Value"}, "Evidence End": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "evidence_infer_treatment", "config_name": "1.1", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 55361753, "num_examples": 1931, "dataset_name": "evidence_infer_treatment"}, "test": {"name": "test", "num_bytes": 6875650, "num_examples": 240, "dataset_name": "evidence_infer_treatment"}, "validation": {"name": "validation", "num_bytes": 7358118, "num_examples": 248, "dataset_name": "evidence_infer_treatment"}}, "download_checksums": {"https://github.com/jayded/evidence-inference/archive/v1.1.zip": {"num_bytes": 114452688, "checksum": "945a81cf40665cd797504728858da54dbb39e16a7785bda833f8d475a407a952"}}, "download_size": 114452688, "post_processing_size": null, "dataset_size": 69595521, "size_in_bytes": 184048209}}

dummy/1.1/1.1.0/dummy_data.zip

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+size 33694

dummy/2.0/2.0.0/dummy_data.zip

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+size 32255

evidence_infer_treatment.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.
+"""Inferring Which Medical Treatments Work from Reports of Clinical Trials"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{lehman-etal-2019-inferring,
+    title = "Inferring Which Medical Treatments Work from Reports of Clinical Trials",
+    author = "Lehman, Eric  and
+      DeYoung, Jay  and
+      Barzilay, Regina  and
+      Wallace, Byron C.",
+    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
+    month = jun,
+    year = "2019",
+    address = "Minneapolis, Minnesota",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/N19-1371",
+    pages = "3705--3717",
+}
+"""
+
+_DESCRIPTION = """\
+Data and code from our "Inferring Which Medical Treatments Work from Reports of Clinical Trials", NAACL 2019. This work concerns inferring the results reported in clinical trials from text.
+
+The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator.
+
+The dataset could be used for automatic data extraction of the results of a given RCT. This would enable readers to discover the effectiveness of different treatments without needing to read the paper.
+"""
+
+
+class EvidenceInferenceConfig(datasets.BuilderConfig):
+    """ BuilderConfig for NewDataset"""
+
+    def __init__(self, zip_file, **kwargs):
+        """
+
+        Args:
+            zip_file: The location of zip file containing original data
+            **kwargs: keyword arguments forwarded to super.
+        """
+        self.zip_file = zip_file
+        super().__init__(**kwargs)
+
+
+class EvidenceInferTreatment(datasets.GeneratorBasedBuilder):
+    f"""{_DESCRIPTION}"""
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+    BUILDER_CONFIG_CLASS = EvidenceInferenceConfig
+    BUILDER_CONFIGS = [
+        EvidenceInferenceConfig(
+            name="2.0",
+            description="EvidenceInference V2",
+            version=datasets.Version("2.0.0"),
+            zip_file="http://evidence-inference.ebm-nlp.com/v2.0.tar.gz",
+        ),
+        EvidenceInferenceConfig(
+            name="1.1",
+            description="EvidenceInference V1.1",
+            version=datasets.Version("1.1.0"),
+            zip_file="https://github.com/jayded/evidence-inference/archive/v1.1.zip",
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "Text": datasets.Value("string"),
+                "PMCID": datasets.Value("int32"),
+                "Prompts": datasets.Sequence(
+                    datasets.Features(
+                        {
+                            "PromptID": datasets.Value("int32"),
+                            "PMCID": datasets.Value("int32"),
+                            "Outcome": datasets.Value("string"),
+                            "Intervention": datasets.Value("string"),
+                            "Comparator": datasets.Value("string"),
+                            "Annotations": datasets.Sequence(
+                                datasets.Features(
+                                    {
+                                        "UserID": datasets.Value("int32"),
+                                        "PromptID": datasets.Value("int32"),
+                                        "PMCID": datasets.Value("int32"),
+                                        "Valid Label": datasets.Value("bool"),
+                                        "Valid Reasoning": datasets.Value("bool"),
+                                        "Label": datasets.Value("string"),
+                                        "Annotations": datasets.Value("string"),
+                                        "Label Code": datasets.Value("int32"),
+                                        "In Abstract": datasets.Value("bool"),
+                                        "Evidence Start": datasets.Value("int32"),
+                                        "Evidence End": datasets.Value("int32"),
+                                    }
+                                )
+                            ),
+                        }
+                    )
+                ),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=features,
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="https://github.com/jayded/evidence-inference",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_dir = dl_manager.download_and_extract(self.config.zip_file)
+        if self.config.name == "1.1":
+            dl_dir = os.path.join(dl_dir, "evidence-inference-1.1", "annotations")
+
+        SPLITS = {}
+        for split in ["train", "test", "validation"]:
+            filename = os.path.join(dl_dir, "splits", f"{split}_article_ids.txt")
+            with open(filename, "r", encoding="utf-8") as f:
+                for line in f:
+                    id_ = int(line.strip())
+                    SPLITS[id_] = split
+
+        ALL_PROMPTS = {}
+        prompts_filename = os.path.join(dl_dir, "prompts_merged.csv")
+        with open(prompts_filename, "r", encoding="utf-8") as f:
+            data = csv.DictReader(f)
+            for item in data:
+                prompt_id = int(item["PromptID"])
+                ALL_PROMPTS[prompt_id] = {"Prompt": item, "Annotations": []}
+
+        annotations_filename = os.path.join(dl_dir, "annotations_merged.csv")
+        with open(annotations_filename, "r", encoding="utf-8") as f:
+            data = csv.DictReader(f)
+            for item in data:
+                prompt_id = int(item["PromptID"])
+
+                if "Annotations" not in ALL_PROMPTS[prompt_id]:
+                    ALL_PROMPTS[prompt_id]["Annotations"] = []
+
+                ALL_PROMPTS[prompt_id]["Annotations"].append(item)
+
+        # Simplify everything
+        directory = os.path.join(dl_dir, "txt_files")
+        ALL_IDS = {"train": [], "test": [], "validation": []}
+        for prompt_id, item in ALL_PROMPTS.items():
+            pmcid = int(item["Prompt"]["PMCID"])
+            if pmcid not in SPLITS:
+                if os.path.isfile(os.path.join(directory, f"PMC{pmcid}.txt")):
+                    split = "train"
+                else:
+                    continue
+            else:
+                split = SPLITS[pmcid]
+
+            values = ALL_IDS[split]
+
+            filtered = [v for v in values if v["PMCID"] == pmcid]
+            if len(filtered) == 1:
+                value = filtered[0]
+            else:
+                value = {"PMCID": pmcid, "Prompts": []}
+                values.append(value)
+
+            new_item = item["Prompt"]
+            new_item["Annotations"] = item["Annotations"]
+            value["Prompts"].append(new_item)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "directory": os.path.join(dl_dir, "txt_files"),
+                    "items": ALL_IDS["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "directory": directory,
+                    "items": ALL_IDS["test"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "directory": os.path.join(dl_dir, "txt_files"),
+                    "items": ALL_IDS["validation"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, directory, items):
+        """ Yields examples. """
+        for id_, item in enumerate(items):
+            pmcid = item["PMCID"]
+            filename = os.path.join(directory, f"PMC{pmcid}.txt")
+            with open(filename, "r", encoding="utf-8") as f:
+                text = f.read()
+
+            yield id_, {"Text": text, **item}