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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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.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
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- en
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+- structure-prediction
+- text-classification
+task_ids:
+- named-entity-recognition
+- slot-filling
+- topic-classification
+---
+
+
+# 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-fields)
+  - [Data Splits](#data-splits)
+- [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:**  [boschresearch/sofc-exp_textmining_resources](https://github.com/boschresearch/sofc-exp_textmining_resources)
+- **Repository:** [boschresearch/sofc-exp_textmining_resources](https://github.com/boschresearch/sofc-exp_textmining_resources)
+- **Paper:** [The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain](https://arxiv.org/abs/2006.03039)
+- **Leaderboard:**
+- **Point of Contact:** [Annemarie Friedrich](annemarie.friedrich@de.bosch.com)
+
+### Dataset Summary
+
+> The SOFC-Exp corpus contains 45 scientific publications about solid oxide fuel cells (SOFCs), published between 2013 and 2019 as open-access articles all with a CC-BY license. The dataset was manually annotated by domain experts with the following information:
+> 
+> * Mentions of relevant experiments have been marked using a graph structure corresponding to instances of an Experiment frame (similar to the ones used in FrameNet.) We assume that an Experiment frame is introduced to the discourse by mentions of words such as report, test or measure (also called the frame-evoking elements). The nodes corresponding to the respective tokens are the heads of the graphs representing the Experiment frame.
+> * The Experiment frame related to SOFC-Experiments defines a set of 16 possible participant slots. Participants are annotated as dependents of links between the frame-evoking element and the participant node.
+> * In addition, we provide coarse-grained entity/concept types for all frame participants, i.e, MATERIAL, VALUE or DEVICE. Note that this annotation has not been performed on the full texts but only on sentences containing information about relevant experiments, and a few sentences in addition. In the paper, we run experiments for both tasks only on the set of sentences marked as experiment-describing in the gold standard, which is admittedly a slightly simplified setting. Entity types are only partially annotated on other sentences. Slot filling could of course also be evaluated in a fully automatic setting with automatic experiment sentence detection as a first step.
+
+### Supported Tasks and Leaderboards
+
+- `topic-classification`: The dataset can be used to train a model for topic-classification, to identify sentences that mention SOFC-related experiments. 
+- `named-entity-recognition`: The dataset can be used to train a named entity recognition model to detect `MATERIAL`, `VALUE`, `DEVICE`, and `EXPERIMENT` entities. 
+- `slot-filling`: The slot-filling task is approached as fine-grained entity-typing-in-context, assuming that each sentence represents a single experiment frame. Sequence tagging architectures are utilized for tagging the tokens of each experiment-describing sentence with the set of slot types.
+
+
+The paper experiments with BiLSTM architectures with `BERT`- and `SciBERT`- generated token embeddings, as well as with `BERT` and `SciBERT` directly for the modeling task. A simple CRF architecture is used as a baseline for sequence-tagging tasks. Implementations of the transformer-based architectures can be found in the `huggingface/transformers` library: [BERT](https://huggingface.co/bert-base-uncased), [SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased)
+
+### Languages
+
+This corpus is in English. 
+
+## Dataset Structure
+
+### Data Instances
+
+As each example is a full text of an academic paper, plus annotations, a json formatted example is space-prohibitive for this README. 
+
+### Data Fields
+
+- `text`: The full text of the paper
+- `sentence_offsets`: Start and end character offsets for each sentence in the text. 
+  - `begin_char_offset`: a `int64` feature.
+  - `end_char_offset`: a `int64` feature.
+- `sentences`: A sequence of the sentences in the text (using `sentence_offsets`)
+- `sentence_labels`: Sequence of binary labels for whether a sentence contains information of interest.
+- `token_offsets`: Sequence of sequences containing start and end character offsets for each token in each sentence in the text. 
+  - `offsets`: a dictionary feature containing:
+    - `begin_char_offset`: a `int64` feature.
+    - `end_char_offset`: a `int64` feature.
+- `tokens`: Sequence of sequences containing the tokens for each sentence in the text.
+  - `feature`: a `string` feature.
+- `entity_labels`: a dictionary feature containing:
+  - `feature`: a classification label, with possible values including `B-DEVICE`, `B-EXPERIMENT`, `B-MATERIAL`, `B-VALUE`, `I-DEVICE`.
+- `slot_labels`: a dictionary feature containing:
+  - `feature`: a classification label, with possible values including `B-anode_material`, `B-cathode_material`, `B-conductivity`, `B-current_density`, `B-degradation_rate`.
+- `links`: a dictionary feature containing:
+  - `relation_label`: a classification label, with possible values including `coreference`, `experiment_variation`, `same_experiment`, `thickness`.
+  - `start_span_id`: a `int64` feature.
+  - `end_span_id`: a `int64` feature.
+- `slots`: a dictionary feature containing:
+  - `frame_participant_label`: a classification label, with possible values including `anode_material`, `cathode_material`, `current_density`, `degradation_rate`, `device`.
+  - `slot_id`: a `int64` feature.
+- `spans`: a dictionary feature containing:
+  - `span_id`: a `int64` feature.
+  - `entity_label`: a classification label, with possible values including ``, `DEVICE`, `MATERIAL`, `VALUE`.
+  - `sentence_id`: a `int64` feature.
+  - `experiment_mention_type`: a classification label, with possible values including ``, `current_exp`, `future_work`, `general_info`, `previous_work`.
+  - `begin_char_offset`: a `int64` feature.
+  - `end_char_offset`: a `int64` feature.
+- `experiments`: a dictionary feature containing:
+  - `experiment_id`: a `int64` feature.
+  - `span_id`: a `int64` feature.
+  - `slots`: a dictionary feature containing:
+    - `frame_participant_label`: a classification label, with possible values including `anode_material`, `cathode_material`, `current_density`, `degradation_rate`, `conductivity`.
+    - `slot_id`: a `int64` feature.
+
+Very detailed information for each of the fields can be found in the [corpus file formats section](https://github.com/boschresearch/sofc-exp_textmining_resources#corpus-file-formats) of the associated dataset repo
+
+### Data Splits
+
+This dataset consists of three splits:
+
+|                            | Train  | Valid | Test |
+| -----                      | ------ | ----- | ---- |
+| Input Examples             |  26    |   8   |  11  |
+
+
+The authors propose the experimental setting of using the training data in a 5-fold cross validation setting for development and tuning, and finally applying tte model(s) to the independent test set.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+The corpus consists of 45
+open-access scientific publications about SOFCs
+and related research, annotated by domain experts.
+
+### Annotations
+
+#### Annotation process
+
+For manual annotation, the authors use the InCeption annotation tool (Klie et al., 2018).
+
+#### 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
+
+The manual annotations created for the SOFC-Exp corpus are licensed under a [Creative Commons Attribution 4.0 International License (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/).
+
+### Citation Information
+
+```
+@misc{friedrich2020sofcexp,
+      title={The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain},
+      author={Annemarie Friedrich and Heike Adel and Federico Tomazic and Johannes Hingerl and Renou Benteau and Anika Maruscyk and Lukas Lange},
+      year={2020},
+      eprint={2006.03039},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

dataset_infos.json

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+{"default": {"description": "The SOFC-Exp corpus consists of 45 open-access scholarly articles annotated by domain experts.\nA corpus and an inter-annotator agreement study demonstrate the complexity of the suggested\nnamed entity recognition and slot filling tasks as well as high annotation quality is presented\nin the accompanying paper.\n", "citation": "@misc{friedrich2020sofcexp,\n      title={The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain},\n      author={Annemarie Friedrich and Heike Adel and Federico Tomazic and Johannes Hingerl and Renou Benteau and Anika Maruscyk and Lukas Lange},\n      year={2020},\n      eprint={2006.03039},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://arxiv.org/abs/2006.03039", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_offsets": {"feature": {"begin_char_offset": {"dtype": "int64", "id": null, "_type": "Value"}, "end_char_offset": {"dtype": "int64", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "sentences": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "sentence_labels": {"feature": {"dtype": "int64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "token_offsets": {"feature": {"offsets": {"feature": {"begin_char_offset": {"dtype": "int64", "id": null, "_type": "Value"}, "end_char_offset": {"dtype": "int64", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "tokens": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "entity_labels": {"feature": {"feature": {"num_classes": 9, "names": ["B-DEVICE", "B-EXPERIMENT", "B-MATERIAL", "B-VALUE", "I-DEVICE", "I-EXPERIMENT", "I-MATERIAL", "I-VALUE", "O"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "slot_labels": {"feature": {"feature": {"num_classes": 39, "names": ["B-anode_material", "B-cathode_material", "B-conductivity", "B-current_density", "B-degradation_rate", "B-device", "B-electrolyte_material", "B-experiment_evoking_word", "B-fuel_used", "B-interlayer_material", "B-interconnect_material", "B-open_circuit_voltage", "B-power_density", "B-resistance", "B-support_material", "B-thickness", "B-time_of_operation", "B-voltage", "B-working_temperature", "I-anode_material", "I-cathode_material", "I-conductivity", "I-current_density", "I-degradation_rate", "I-device", "I-electrolyte_material", "I-experiment_evoking_word", "I-fuel_used", "I-interlayer_material", "I-interconnect_material", "I-open_circuit_voltage", "I-power_density", "I-resistance", "I-support_material", "I-thickness", "I-time_of_operation", "I-voltage", "I-working_temperature", "O"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "links": {"feature": {"relation_label": {"num_classes": 4, "names": ["coreference", "experiment_variation", "same_experiment", "thickness"], "names_file": null, "id": null, "_type": "ClassLabel"}, "start_span_id": {"dtype": "int64", "id": null, "_type": "Value"}, "end_span_id": {"dtype": "int64", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "slots": {"feature": {"frame_participant_label": {"num_classes": 15, "names": ["anode_material", "cathode_material", "current_density", "degradation_rate", "device", "electrolyte_material", "fuel_used", "interlayer_material", "open_circuit_voltage", "power_density", "resistance", "support_material", "time_of_operation", "voltage", "working_temperature"], "names_file": null, "id": null, "_type": "ClassLabel"}, "slot_id": {"dtype": "int64", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "spans": {"feature": {"span_id": {"dtype": "int64", "id": null, "_type": "Value"}, "entity_label": {"num_classes": 4, "names": ["", "DEVICE", "MATERIAL", "VALUE"], "names_file": null, "id": null, "_type": "ClassLabel"}, "sentence_id": {"dtype": "int64", "id": null, "_type": "Value"}, "experiment_mention_type": {"num_classes": 5, "names": ["", "current_exp", "future_work", "general_info", "previous_work"], "names_file": null, "id": null, "_type": "ClassLabel"}, "begin_char_offset": {"dtype": "int64", "id": null, "_type": "Value"}, "end_char_offset": {"dtype": "int64", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "experiments": {"feature": {"experiment_id": {"dtype": "int64", "id": null, "_type": "Value"}, "span_id": {"dtype": "int64", "id": null, "_type": "Value"}, "slots": {"feature": {"frame_participant_label": {"num_classes": 16, "names": ["anode_material", "cathode_material", "current_density", "degradation_rate", "conductivity", "device", "electrolyte_material", "fuel_used", "interlayer_material", "open_circuit_voltage", "power_density", "resistance", "support_material", "time_of_operation", "voltage", "working_temperature"], "names_file": null, "id": null, "_type": "ClassLabel"}, "slot_id": {"dtype": "int64", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "sofc_materials_articles", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7402373, "num_examples": 26, "dataset_name": "sofc_materials_articles"}, "test": {"name": "test", "num_bytes": 2650700, "num_examples": 11, "dataset_name": "sofc_materials_articles"}, "validation": {"name": "validation", "num_bytes": 1993857, "num_examples": 8, "dataset_name": "sofc_materials_articles"}}, "download_checksums": {"https://github.com/boschresearch/sofc-exp_textmining_resources/archive/master.zip": {"num_bytes": 3733137, "checksum": "8f55da5c16b4e91e484b4f6e4eee89f3b2773fa586a649c639eb1c66e3323deb"}}, "download_size": 3733137, "post_processing_size": null, "dataset_size": 12046930, "size_in_bytes": 15780067}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1eba93cb2be57db7576b730571d277b9d8ddef29d7109680b6f3199ad9fc3f79
+size 20887

sofc_materials_articles.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.
+"""TODO: Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import glob
+import os
+
+import pandas as pd
+
+import datasets
+
+
+_CITATION = """\
+@misc{friedrich2020sofcexp,
+      title={The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain},
+      author={Annemarie Friedrich and Heike Adel and Federico Tomazic and Johannes Hingerl and Renou Benteau and Anika Maruscyk and Lukas Lange},
+      year={2020},
+      eprint={2006.03039},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+The SOFC-Exp corpus consists of 45 open-access scholarly articles annotated by domain experts.
+A corpus and an inter-annotator agreement study demonstrate the complexity of the suggested
+named entity recognition and slot filling tasks as well as high annotation quality is presented
+in the accompanying paper.
+"""
+
+_HOMEPAGE = "https://arxiv.org/abs/2006.03039"
+
+_LICENSE = ""
+
+_URL = "https://github.com/boschresearch/sofc-exp_textmining_resources/archive/master.zip"
+
+
+class SOFCMaterialsArticles(datasets.GeneratorBasedBuilder):
+    """"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+                "sentence_offsets": datasets.features.Sequence(
+                    {"begin_char_offset": datasets.Value("int64"), "end_char_offset": datasets.Value("int64")}
+                ),
+                "sentences": datasets.features.Sequence(datasets.Value("string")),
+                "sentence_labels": datasets.features.Sequence(datasets.Value("int64")),
+                "token_offsets": datasets.features.Sequence(
+                    {
+                        "offsets": datasets.features.Sequence(
+                            {"begin_char_offset": datasets.Value("int64"), "end_char_offset": datasets.Value("int64")}
+                        )
+                    }
+                ),
+                "tokens": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
+                "entity_labels": datasets.features.Sequence(
+                    datasets.features.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "B-DEVICE",
+                                "B-EXPERIMENT",
+                                "B-MATERIAL",
+                                "B-VALUE",
+                                "I-DEVICE",
+                                "I-EXPERIMENT",
+                                "I-MATERIAL",
+                                "I-VALUE",
+                                "O",
+                            ]
+                        )
+                    )
+                ),
+                "slot_labels": datasets.features.Sequence(
+                    datasets.features.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "B-anode_material",
+                                "B-cathode_material",
+                                "B-conductivity",
+                                "B-current_density",
+                                "B-degradation_rate",
+                                "B-device",
+                                "B-electrolyte_material",
+                                "B-experiment_evoking_word",
+                                "B-fuel_used",
+                                "B-interlayer_material",
+                                "B-interconnect_material",
+                                "B-open_circuit_voltage",
+                                "B-power_density",
+                                "B-resistance",
+                                "B-support_material",
+                                "B-thickness",
+                                "B-time_of_operation",
+                                "B-voltage",
+                                "B-working_temperature",
+                                "I-anode_material",
+                                "I-cathode_material",
+                                "I-conductivity",
+                                "I-current_density",
+                                "I-degradation_rate",
+                                "I-device",
+                                "I-electrolyte_material",
+                                "I-experiment_evoking_word",
+                                "I-fuel_used",
+                                "I-interlayer_material",
+                                "I-interconnect_material",
+                                "I-open_circuit_voltage",
+                                "I-power_density",
+                                "I-resistance",
+                                "I-support_material",
+                                "I-thickness",
+                                "I-time_of_operation",
+                                "I-voltage",
+                                "I-working_temperature",
+                                "O",
+                            ]
+                        )
+                    )
+                ),
+                "links": datasets.Sequence(
+                    {
+                        "relation_label": datasets.features.ClassLabel(
+                            names=["coreference", "experiment_variation", "same_experiment", "thickness"]
+                        ),
+                        "start_span_id": datasets.Value("int64"),
+                        "end_span_id": datasets.Value("int64"),
+                    }
+                ),
+                "slots": datasets.features.Sequence(
+                    {
+                        "frame_participant_label": datasets.features.ClassLabel(
+                            names=[
+                                "anode_material",
+                                "cathode_material",
+                                "current_density",
+                                "degradation_rate",
+                                "device",
+                                "electrolyte_material",
+                                "fuel_used",
+                                "interlayer_material",
+                                "open_circuit_voltage",
+                                "power_density",
+                                "resistance",
+                                "support_material",
+                                "time_of_operation",
+                                "voltage",
+                                "working_temperature",
+                            ]
+                        ),
+                        "slot_id": datasets.Value("int64"),
+                    }
+                ),
+                "spans": datasets.features.Sequence(
+                    {
+                        "span_id": datasets.Value("int64"),
+                        "entity_label": datasets.features.ClassLabel(names=["", "DEVICE", "MATERIAL", "VALUE"]),
+                        "sentence_id": datasets.Value("int64"),
+                        "experiment_mention_type": datasets.features.ClassLabel(
+                            names=["", "current_exp", "future_work", "general_info", "previous_work"]
+                        ),
+                        "begin_char_offset": datasets.Value("int64"),
+                        "end_char_offset": datasets.Value("int64"),
+                    }
+                ),
+                "experiments": datasets.features.Sequence(
+                    {
+                        "experiment_id": datasets.Value("int64"),
+                        "span_id": datasets.Value("int64"),
+                        "slots": datasets.features.Sequence(
+                            {
+                                "frame_participant_label": datasets.features.ClassLabel(
+                                    names=[
+                                        "anode_material",
+                                        "cathode_material",
+                                        "current_density",
+                                        "degradation_rate",
+                                        "conductivity",
+                                        "device",
+                                        "electrolyte_material",
+                                        "fuel_used",
+                                        "interlayer_material",
+                                        "open_circuit_voltage",
+                                        "power_density",
+                                        "resistance",
+                                        "support_material",
+                                        "time_of_operation",
+                                        "voltage",
+                                        "working_temperature",
+                                    ]
+                                ),
+                                "slot_id": datasets.Value("int64"),
+                            }
+                        ),
+                    }
+                ),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # 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=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        my_urls = _URL
+        data_dir = dl_manager.download_and_extract(my_urls)
+
+        data_dir = os.path.join(data_dir, "sofc-exp_textmining_resources-master/sofc-exp-corpus")
+
+        metadata = pd.read_csv(os.path.join(data_dir, "SOFC-Exp-Metadata.csv"), sep="\t")
+
+        text_base_path = os.path.join(data_dir, "texts")
+
+        text_files_available = [
+            os.path.split(i.rstrip(".txt"))[-1] for i in glob.glob(os.path.join(text_base_path, "*.txt"))
+        ]
+
+        metadata = metadata[metadata["name"].map(lambda x: x in text_files_available)]
+
+        names = {}
+        splits = ["train", "test", "dev"]
+        for split in splits:
+            names[split] = metadata[metadata["set"] == split]["name"].tolist()
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "names": names["train"],
+                    "data_dir": data_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"names": names["test"], "data_dir": data_dir, "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "names": names["dev"],
+                    "data_dir": data_dir,
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, names, data_dir, split):
+        """ Yields examples. """
+        # The dataset consists of the original article text as well as annotations
+        textfile_base_path = os.path.join(data_dir, "texts")
+        annotations_base_path = os.path.join(data_dir, "annotations")
+
+        # The annotations are mostly references to offsets in the source text
+        # with corresponding labels, so we'll refer to them as `meta`
+        sentence_meta_base_path = os.path.join(annotations_base_path, "sentences")
+        tokens_meta_base_path = os.path.join(annotations_base_path, "tokens")
+        ets_meta_base_path = os.path.join(annotations_base_path, "entity_types_and_slots")
+        frame_meta_base_path = os.path.join(annotations_base_path, "frames")
+
+        # Define the headers for the sentence and token and entity metadata
+        sentence_meta_header = ["sentence_id", "label", "begin_char_offset", "end_char_offset"]
+        tokens_meta_header = ["sentence_id", "token_id", "begin_char_offset", "end_char_offset"]
+        ets_meta_header = [
+            "sentence_id",
+            "token_id",
+            "begin_char_offset",
+            "end_char_offset",
+            "entity_label",
+            "slot_label",
+        ]
+
+        # Start the processing loop
+        # For each text file, we'll load all of the
+        # associated annotation files
+        for id_, name in enumerate(sorted(names)):
+
+            # Load the main source text
+            textfile_path = os.path.join(textfile_base_path, name + ".txt")
+            text = open(textfile_path, encoding="utf-8").read()
+
+            # Load the sentence offsets file
+            sentence_meta_path = os.path.join(sentence_meta_base_path, name + ".csv")
+            sentence_meta = pd.read_csv(sentence_meta_path, sep="\t", names=sentence_meta_header)
+
+            # Load the tokens offsets file
+            tokens_meta_path = os.path.join(tokens_meta_base_path, name + ".csv")
+            tokens_meta = pd.read_csv(tokens_meta_path, sep="\t", names=tokens_meta_header)
+
+            # Load the entity offsets file
+            ets_meta_path = os.path.join(ets_meta_base_path, name + ".csv")
+            ets_meta = pd.read_csv(ets_meta_path, sep="\t", names=ets_meta_header)
+
+            # Create a list of lists indexed as [sentence][token] for the entity and slot labels
+            entity_labels = ets_meta.groupby("sentence_id").apply(lambda x: x["entity_label"].tolist()).to_list()
+            slot_labels = ets_meta.groupby("sentence_id").apply(lambda x: x["slot_label"].tolist()).to_list()
+
+            # Create a list of lists for the token offsets indexed as [sentence][token]
+            # Each element will contain a dict with beginning and ending character offsets
+            token_offsets = (
+                tokens_meta.groupby("sentence_id")[["begin_char_offset", "end_char_offset"]]
+                .apply(lambda x: x.to_dict(orient="records"))
+                .tolist()
+            )
+
+            # Load the frames metadata. The frames file contains the data for all of the annotations
+            # in a condensed format that varies throughout the file. More information on this format
+            # can be found: https://framenet.icsi.berkeley.edu/fndrupal/
+            frames_meta_path = os.path.join(frame_meta_base_path, name + ".csv")
+            frames_meta = open(frames_meta_path, encoding="utf-8").readlines()
+
+            # Parse the sentence offsets, producing a list of dicts with the
+            # starting and ending position of each sentence in the original text
+            sentence_offsets = (
+                sentence_meta[["begin_char_offset", "end_char_offset"]].apply(lambda x: x.to_dict(), axis=1).tolist()
+            )
+
+            # The sentence labels are a binary label that describes whether the sentence contains
+            # any annotations
+            sentence_labels = sentence_meta["label"].tolist()
+
+            # Materialiaze a list of strings of the actual sentences
+            sentences = [text[ost["begin_char_offset"] : ost["end_char_offset"]] for ost in sentence_offsets]
+
+            # Materialize a list of lists of the tokens in each sentence.
+            # Annotation labels are aligned with these tokens, so be careful with
+            # alignment if using your own tokenization scheme with the sentences above
+            tokens = [
+                [s[tto["begin_char_offset"] : tto["end_char_offset"]] for tto in to]
+                for s, to in zip(sentences, token_offsets)
+            ]
+
+            # The frames file first contains spans annotations (in one format),
+            # then contains experiments annotations (in another format),
+            # then links annotations (in yet another format).
+            # Here we find the beginning of the experiments and links sections of the file
+            # Additionally, each experiment annotation in the experiment annotations begins with a
+            # line starting with the word EXPERIMENT (in one format)
+            # followed by the annotations for that experiment (in yet _another_ format)
+            # Here we get the start positions for each experiment _within_ the experiments
+            # section of the frames data
+            experiment_starts = [i for i, line in enumerate(frames_meta) if line.startswith("EXPERIMENT")]
+            experiment_start = min(experiment_starts)
+            link_start = min([i for i, line in enumerate(frames_meta) if line.startswith("LINK")])
+
+            # Pick out the spans section of the data for parsing
+            spans_raw = frames_meta[:experiment_start]
+
+            # Iterate through the spans data
+            spans = []
+            for span in spans_raw:
+
+                # Split out the elements in each tab-delimited line
+                _, span_id, entity_label_or_exp, sentence_id, begin_char_offset, end_char_offset = span.split("\t")
+
+                # The entity label for experiment spans have a sub-label,
+                # called the experiment mention type,
+                # which is sub-delimited by a ':'
+                # The code below standardizes the fields produced by
+                # each line to a common schema, some fields of which may
+                # be empty depending on the data available in the line
+                if entity_label_or_exp.startswith("EXPERIMENT"):
+                    exp, experiment_mention_type = entity_label_or_exp.split(":")
+                    entity_label = ""
+                else:
+                    entity_label = entity_label_or_exp
+                    exp = ""
+                    experiment_mention_type = ""
+
+                s = {
+                    "span_id": span_id,
+                    "entity_label": entity_label,
+                    "sentence_id": sentence_id,
+                    "experiment_mention_type": experiment_mention_type,
+                    "begin_char_offset": int(begin_char_offset),
+                    "end_char_offset": int(end_char_offset),
+                }
+                spans.append(s)
+
+            # Pull out the links annotations for from the frames data
+            links_raw = [f.rstrip("\n") for f in frames_meta[link_start:]]
+
+            # Iterate through the links data, which is in a simple tab-delimited format
+            links = []
+            for l in links_raw:
+                _, relation_label, start_span_id, end_span_id = l.split("\t")
+
+                link_out = {
+                    "relation_label": relation_label,
+                    "start_span_id": int(start_span_id),
+                    "end_span_id": int(end_span_id),
+                }
+                links.append(link_out)
+
+            # Iterate through the experiments data and parse each experiment
+            experiments = []
+            # Zip the experiment start offsets to get start/end position tuples
+            # for each experiment in the experiments data
+            for start, end in zip(experiment_starts[:-1], experiment_starts[1:]):
+                current_experiment = frames_meta[start:end]
+                # The first line of each experiment annotation contains the
+                # experiment id and the span id
+                _, experiment_id, span_id = current_experiment[0].rstrip("\n").split("\t")
+                exp = {"experiment_id": int(experiment_id), "span_id": int(span_id)}
+
+                # The remaining lines in the experiment annotations contain
+                # slot level information for each experiment.
+                slots = []
+                for e in current_experiment[1:]:
+                    e = e.rstrip("\n")
+                    _, frame_participant_label, slot_id = e.split("\t")
+                    to_add = {"frame_participant_label": frame_participant_label, "slot_id": int(slot_id)}
+                    slots.append(to_add)
+                exp["slots"] = slots
+
+                experiments.append(exp)
+
+            # Yield the final parsed example output
+            # NOTE: the `token_offsets` is converted to a list of
+            # dicts to accommodate processing to the arrow files
+            # in the `features` schema defined above
+            yield id_, {
+                "text": text,
+                "sentence_offsets": sentence_offsets,
+                "sentences": sentences,
+                "sentence_labels": sentence_labels,
+                "token_offsets": [{"offsets": to} for to in token_offsets],
+                "tokens": tokens,
+                "entity_labels": entity_labels,
+                "slot_labels": slot_labels,
+                "links": links,
+                "slots": slots,
+                "spans": spans,
+                "experiments": experiments,
+            }