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README.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- en
+language_creators:
+- found
+license:
+- other
+multilinguality:
+- monolingual
+pretty_name: FabNER is a manufacturing text dataset for Named Entity Recognition.
+size_categories:
+- 10K<n<100K
+source_datasets: []
+tags:
+- manufacturing
+- 2000-2020
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+dataset_info:
+  - config_name: fabner
+    features:
+      - name: id
+        dtype: string
+      - name: tokens
+        sequence: string
+      - name: ner_tags
+        sequence:
+          class_label:
+            names:
+              '0': O
+              '1': B-MATE
+              '2': I-MATE
+              '3': E-MATE
+              '4': S-MATE
+              '5': B-MANP
+              '6': I-MANP
+              '7': E-MANP
+              '8': S-MANP
+              '9': B-MACEQ
+              '10': I-MACEQ
+              '11': E-MACEQ
+              '12': S-MACEQ
+              '13': B-APPL
+              '14': I-APPL
+              '15': E-APPL
+              '16': S-APPL
+              '17': B-FEAT
+              '18': I-FEAT
+              '19': E-FEAT
+              '20': S-FEAT
+              '21': B-PRO
+              '22': I-PRO
+              '23': E-PRO
+              '24': S-PRO
+              '25': B-CHAR
+              '26': I-CHAR
+              '27': E-CHAR
+              '28': S-CHAR
+              '29': B-PARA
+              '30': I-PARA
+              '31': E-PARA
+              '32': S-PARA
+              '33': B-ENAT
+              '34': I-ENAT
+              '35': E-ENAT
+              '36': S-ENAT
+              '37': B-CONPRI
+              '38': I-CONPRI
+              '39': E-CONPRI
+              '40': S-CONPRI
+              '41': B-MANS
+              '42': I-MANS
+              '43': E-MANS
+              '44': S-MANS
+              '45': B-BIOP
+              '46': I-BIOP
+              '47': E-BIOP
+              '48': S-BIOP
+    splits:
+      - name: train
+        num_bytes: 4394010
+        num_examples: 9435
+      - name: validation
+        num_bytes: 934347
+        num_examples: 2183
+      - name: test
+        num_bytes: 940136
+        num_examples: 2064
+    download_size: 1265830
+    dataset_size: 6268493
+  - config_name: fabner_bio
+    features:
+      - name: id
+        dtype: string
+      - name: tokens
+        sequence: string
+      - name: ner_tags
+        sequence:
+          class_label:
+            names:
+              '0': O
+              '1': B-MATE
+              '2': I-MATE
+              '3': B-MANP
+              '4': I-MANP
+              '5': B-MACEQ
+              '6': I-MACEQ
+              '7': B-APPL
+              '8': I-APPL
+              '9': B-FEAT
+              '10': I-FEAT
+              '11': B-PRO
+              '12': I-PRO
+              '13': B-CHAR
+              '14': I-CHAR
+              '15': B-PARA
+              '16': I-PARA
+              '17': B-ENAT
+              '18': I-ENAT
+              '19': B-CONPRI
+              '20': I-CONPRI
+              '21': B-MANS
+              '22': I-MANS
+              '23': B-BIOP
+              '24': I-BIOP
+    splits:
+      - name: train
+        num_bytes: 4394010
+        num_examples: 9435
+      - name: validation
+        num_bytes: 934347
+        num_examples: 2183
+      - name: test
+        num_bytes: 940136
+        num_examples: 2064
+    download_size: 1258672
+    dataset_size: 6268493
+  - config_name: fabner_simple
+    features:
+      - name: id
+        dtype: string
+      - name: tokens
+        sequence: string
+      - name: ner_tags
+        sequence:
+          class_label:
+            names:
+              '0': O
+              '1': MATE
+              '2': MANP
+              '3': MACEQ
+              '4': APPL
+              '5': FEAT
+              '6': PRO
+              '7': CHAR
+              '8': PARA
+              '9': ENAT
+              '10': CONPRI
+              '11': MANS
+              '12': BIOP
+    splits:
+      - name: train
+        num_bytes: 4394010
+        num_examples: 9435
+      - name: validation
+        num_bytes: 934347
+        num_examples: 2183
+      - name: test
+        num_bytes: 940136
+        num_examples: 2064
+    download_size: 1233960
+    dataset_size: 6268493
+  - config_name: text2tech
+    features:
+      - name: id
+        dtype: string
+      - name: tokens
+        sequence: string
+      - name: ner_tags
+        sequence:
+          class_label:
+            names:
+              '0': O
+              '1': Technological System
+              '2': Method
+              '3': Material
+              '4': Technical Field
+    splits:
+      - name: train
+        num_bytes: 4394010
+        num_examples: 9435
+      - name: validation
+        num_bytes: 934347
+        num_examples: 2183
+      - name: test
+        num_bytes: 940136
+        num_examples: 2064
+    download_size: 1192966
+    dataset_size: 6268493
+---
+
+# Dataset Card for FabNER
+
+## Table of Contents
+- [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)
+  - [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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407](https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407)
+- **Paper:** ["FabNER": information extraction from manufacturing process science domain literature using named entity recognition](https://par.nsf.gov/servlets/purl/10290810)
+- **Size of downloaded dataset files:** 3.79 MB
+- **Size of the generated dataset:** 6.27 MB
+
+### Dataset Summary
+
+FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition.
+It is a collection of abstracts obtained from Web of Science through known journals available in manufacturing process 
+science research.
+For every word, there were categories/entity labels defined, namely Material (MATE), Manufacturing Process (MANP), 
+Machine/Equipment (MACEQ), Application (APPL), Features (FEAT), Mechanical Properties (PRO), Characterization (CHAR), 
+Parameters (PARA), Enabling Technology (ENAT), Concept/Principles (CONPRI), Manufacturing Standards (MANS) and 
+BioMedical (BIOP). Annotation was performed in all categories along with the output tag in 'BIOES' format: 
+B=Beginning, I-Intermediate, O=Outside, E=End, S=Single.
+
+For details about the dataset, please refer to the paper: ["FabNER": information extraction from manufacturing process science domain literature using named entity recognition](https://par.nsf.gov/servlets/purl/10290810)
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+The language in the dataset is English.
+
+## Dataset Structure
+
+### Data Instances
+
+- **Size of downloaded dataset files:** 3.79 MB
+- **Size of the generated dataset:** 6.27 MB
+  
+An example of 'train' looks as follows:
+```json
+{
+  "id": "0", 
+  "tokens": ["Revealed", "the", "location-specific", "flow", "patterns", "and", "quantified", "the", "speeds", "of", "various", "types", "of", "flow", "."], 
+  "ner_tags": [0, 0, 0, 46, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+}
+```
+
+### Data Fields
+
+#### fabner
+- `id`: the instance id of this sentence, a `string` feature.
+- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
+- `ner_tags`: the list of entity tags, a `list` of classification labels.
+
+```json
+{"O": 0, "B-MATE": 1, "I-MATE": 2, "O-MATE": 3, "E-MATE": 4, "S-MATE": 5, "B-MANP": 6, "I-MANP": 7, "O-MANP": 8, "E-MANP": 9, "S-MANP": 10, "B-MACEQ": 11, "I-MACEQ": 12, "O-MACEQ": 13, "E-MACEQ": 14, "S-MACEQ": 15, "B-APPL": 16, "I-APPL": 17, "O-APPL": 18, "E-APPL": 19, "S-APPL": 20, "B-FEAT": 21, "I-FEAT": 22, "O-FEAT": 23, "E-FEAT": 24, "S-FEAT": 25, "B-PRO": 26, "I-PRO": 27, "O-PRO": 28, "E-PRO": 29, "S-PRO": 30, "B-CHAR": 31, "I-CHAR": 32, "O-CHAR": 33, "E-CHAR": 34, "S-CHAR": 35, "B-PARA": 36, "I-PARA": 37, "O-PARA": 38, "E-PARA": 39, "S-PARA": 40, "B-ENAT": 41, "I-ENAT": 42, "O-ENAT": 43, "E-ENAT": 44, "S-ENAT": 45, "B-CONPRI": 46, "I-CONPRI": 47, "O-CONPRI": 48, "E-CONPRI": 49, "S-CONPRI": 50, "B-MANS": 51, "I-MANS": 52, "O-MANS": 53, "E-MANS": 54, "S-MANS": 55, "B-BIOP": 56, "I-BIOP": 57, "O-BIOP": 58, "E-BIOP": 59, "S-BIOP": 60}
+```
+
+#### fabner_bio
+- `id`: the instance id of this sentence, a `string` feature.
+- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
+- `ner_tags`: the list of entity tags, a `list` of classification labels.
+
+```json
+{"O": 0, "B-MATE": 1, "I-MATE": 2, "B-MANP": 3, "I-MANP": 4, "B-MACEQ": 5, "I-MACEQ": 6, "B-APPL": 7, "I-APPL": 8, "B-FEAT": 9, "I-FEAT": 10, "B-PRO": 11, "I-PRO": 12, "B-CHAR": 13, "I-CHAR": 14, "B-PARA": 15, "I-PARA": 16, "B-ENAT": 17, "I-ENAT": 18, "B-CONPRI": 19, "I-CONPRI": 20, "B-MANS": 21, "I-MANS": 22, "B-BIOP": 23, "I-BIOP": 24}
+```
+
+#### fabner_simple
+- `id`: the instance id of this sentence, a `string` feature.
+- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
+- `ner_tags`: the list of entity tags, a `list` of classification labels.
+
+```json
+{"O": 0, "MATE": 1, "MANP": 2, "MACEQ": 3, "APPL": 4, "FEAT": 5, "PRO": 6, "CHAR": 7, "PARA": 8, "ENAT": 9, "CONPRI": 10, "MANS": 11, "BIOP": 12}
+```
+
+#### text2tech
+- `id`: the instance id of this sentence, a `string` feature.
+- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
+- `ner_tags`: the list of entity tags, a `list` of classification labels.
+
+```json
+{"O": 0, "Technological System": 1, "Method": 2, "Material": 3, "Technical Field": 4}
+```
+
+### Data Splits
+
+|        | Train | Dev  | Test |
+|--------|-------|------|------|
+| fabner | 9435  | 2183 | 2064 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@article{DBLP:journals/jim/KumarS22,
+  author    = {Aman Kumar and
+               Binil Starly},
+  title     = {"FabNER": information extraction from manufacturing process science
+               domain literature using named entity recognition},
+  journal   = {J. Intell. Manuf.},
+  volume    = {33},
+  number    = {8},
+  pages     = {2393--2407},
+  year      = {2022},
+  url       = {https://doi.org/10.1007/s10845-021-01807-x},
+  doi       = {10.1007/s10845-021-01807-x},
+  timestamp = {Sun, 13 Nov 2022 17:52:57 +0100},
+  biburl    = {https://dblp.org/rec/journals/jim/KumarS22.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+### Contributions
+
+Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.

fabner.py

@@ -0,0 +1,230 @@
+# 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.
+"""FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition."""
+
+import datasets
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{DBLP:journals/jim/KumarS22,
+  author    = {Aman Kumar and
+               Binil Starly},
+  title     = {"FabNER": information extraction from manufacturing process science
+               domain literature using named entity recognition},
+  journal   = {J. Intell. Manuf.},
+  volume    = {33},
+  number    = {8},
+  pages     = {2393--2407},
+  year      = {2022},
+  url       = {https://doi.org/10.1007/s10845-021-01807-x},
+  doi       = {10.1007/s10845-021-01807-x},
+  timestamp = {Sun, 13 Nov 2022 17:52:57 +0100},
+  biburl    = {https://dblp.org/rec/journals/jim/KumarS22.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition.
+It is a collection of abstracts obtained from Web of Science through known journals available in manufacturing process 
+science research.
+For every word, there were categories/entity labels defined namely Material (MATE), Manufacturing Process (MANP), 
+Machine/Equipment (MACEQ), Application (APPL), Features (FEAT), Mechanical Properties (PRO), Characterization (CHAR), 
+Parameters (PARA), Enabling Technology (ENAT), Concept/Principles (CONPRI), Manufacturing Standards (MANS) and 
+BioMedical (BIOP). Annotation was performed in all categories along with the output tag in 'BIOES' format: 
+B=Beginning, I-Intermediate, O=Outside, E=End, S=Single.
+"""
+
+_HOMEPAGE = "https://figshare.com/articles/dataset/Dataset_NER_Manufacturing_-_FabNER_Information_Extraction_from_Manufacturing_Process_Science_Domain_Literature_Using_Named_Entity_Recognition/14782407"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "train": "https://figshare.com/ndownloader/files/28405854/S2-train.txt",
+    "validation": "https://figshare.com/ndownloader/files/28405857/S3-val.txt",
+    "test": "https://figshare.com/ndownloader/files/28405851/S1-test.txt",
+}
+
+
+def map_fabner_labels(string_tag):
+    tag = string_tag[2:]
+    # MATERIAL (FABNER)
+    if tag == "MATE":
+        return "Material"
+    # MANUFACTURING PROCESS (FABNER)
+    elif tag == "MANP":
+        return "Method"
+    # MACHINE/EQUIPMENT, MECHANICAL PROPERTIES, CHARACTERIZATION, ENABLING TECHNOLOGY (FABNER)
+    elif tag in ["MACEQ", "PRO", "CHAR", "ENAT"]:
+        return "Technological System"
+    # APPLICATION (FABNER)
+    elif tag == "APPL":
+        return "Technical Field"
+    # FEATURES, PARAMETERS, CONCEPT/PRINCIPLES, MANUFACTURING STANDARDS, BIOMEDICAL, O (FABNER)
+    else:
+        return "O"
+
+
+class FabNER(datasets.GeneratorBasedBuilder):
+    """FabNER is a manufacturing text corpus of 350,000+ words for Named Entity Recognition."""
+
+    VERSION = datasets.Version("1.2.0")
+
+    # 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.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="fabner", version=VERSION,
+                               description="The FabNER dataset with the original BIOES tagging format"),
+        datasets.BuilderConfig(name="fabner_bio", version=VERSION,
+                               description="The FabNER dataset with BIO tagging format"),
+        datasets.BuilderConfig(name="fabner_simple", version=VERSION,
+                               description="The FabNER dataset with no tagging format"),
+        datasets.BuilderConfig(name="text2tech", version=VERSION,
+                               description="The FabNER dataset mapped to the Text2Tech tag set"),
+    ]
+    DEFAULT_CONFIG_NAME = "fabner"
+
+    def _info(self):
+        entity_types = [
+            "MATE",     # Material
+            "MANP",     # Manufacturing Process
+            "MACEQ",    # Machine/Equipment
+            "APPL",     # Application
+            "FEAT",     # Engineering Features
+            "PRO",      # Mechanical Properties
+            "CHAR",     # Process Characterization
+            "PARA",     # Process Parameters
+            "ENAT",     # Enabling Technology
+            "CONPRI",   # Concept/Principles
+            "MANS",     # Manufacturing Standards
+            "BIOP",     # BioMedical
+        ]
+        if self.config.name == "text2tech":
+            class_labels = ["O", "Technological System", "Method",  "Material", "Technical Field"]
+        elif self.config.name == "fabner":
+            class_labels = ["O"]
+            for entity_type in entity_types:
+                class_labels.extend(
+                    [
+                        "B-" + entity_type,
+                        "I-" + entity_type,
+                        "E-" + entity_type,
+                        "S-" + entity_type,
+                    ]
+                )
+        elif self.config.name == "fabner_bio":
+            class_labels = ["O"]
+            for entity_type in entity_types:
+                class_labels.extend(
+                    [
+                        "B-" + entity_type,
+                        "I-" + entity_type,
+                    ]
+                )
+        else:
+            class_labels = ["O"] + entity_types
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "tokens": datasets.Sequence(datasets.Value("string")),
+                "ner_tags": datasets.Sequence(
+                    datasets.features.ClassLabel(
+                        names=class_labels
+                    )
+                ),
+            }
+        )
+        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, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # 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):
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)]})
+                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            ner_tags = []
+            for line in f:
+                if line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                else:
+                    splits = line.split(" ")
+                    tokens.append(splits[0])
+                    ner_tag = splits[1].rstrip()
+                    if self.config.name == "fabner_simple":
+                        if ner_tag == "O":
+                            ner_tag = "O"
+                        else:
+                            ner_tag = ner_tag.split("-")[1]
+                    elif self.config.name == "fabner_bio":
+                        if ner_tag == "O":
+                            ner_tag = "O"
+                        else:
+                            ner_tag = ner_tag.replace("S-", "B-").replace("E-", "I-")
+                    elif self.config.name == "text2tech":
+                        ner_tag = map_fabner_labels(ner_tag)
+                    ner_tags.append(ner_tag)
+            # last example
+            if tokens:
+                yield guid, {
+                    "id": str(guid),
+                    "tokens": tokens,
+                    "ner_tags": ner_tags,
+                }