upload dataset

.gitattributes

@@ -25,3 +25,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+test.conll filter=lfs diff=lfs merge=lfs -text
+train.conll filter=lfs diff=lfs merge=lfs -text
+dev.conll filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+annotations_creators:
+- expert-generated
+languages:
+- es
+multilinguality:
+- monolingual
+task_categories:
+- text-classification
+- multi-label-text-classification
+task_ids:
+- named-entity-recognition
+---
+
+# CANTEMIST Corpus
+
+## BibTeX  citation
+If you use these resources in your work, please cite the following paper:
+
+```bibtex
+@inproceedings{miranda2020named,
+  title={Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results},
+  author={Miranda-Escalada, A and Farr{\'e}, E and Krallinger, M},
+  booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings},
+  year={2020}
+}
+```
+
+## Digital Object Identifier (DOI) and access to dataset files
+
+TO DO: link to zenodo
+
+## Introduction
+
+TO DO: This is a dataset for Named Entity Recognition (NER) from...
+
+### Supported Tasks and Leaderboards
+
+Named Entities Recognition, Language Model
+
+### Languages
+
+ES - Spanish
+
+### Directory structure
+
+* cantemist-ner.py
+* dev.conll
+* test.conll
+* train.conll
+* README.md
+
+## Dataset Structure
+
+### Data Instances
+
+Three four-column files, one for each split.
+
+### Data Fields
+
+Every file has four columns:
+* 1st column: Word form or punctuation symbol 
+* 2nd column: Original BRAT file name
+* 3rd column: Spans
+* 4th column: IOB tag
+
+### Example:
+<pre>
+El                  cc_onco101	662_664	O
+informe             cc_onco101	665_672	O
+HP                  cc_onco101	673_675	O
+es                  cc_onco101	676_678	O
+compatible          cc_onco101	679_689	O
+con                 cc_onco101	690_693	O
+adenocarcinoma      cc_onco101	694_708	B-MORFOLOGIA_NEOPLASIA
+moderadamente       cc_onco101	709_722	I-MORFOLOGIA_NEOPLASIA
+diferenciado        cc_onco101	723_735	I-MORFOLOGIA_NEOPLASIA
+que                 cc_onco101	736_739	O
+afecta              cc_onco101	740_746	O
+a                   cc_onco101	747_748	O
+grasa               cc_onco101	749_754	O
+peripancreática     cc_onco101	755_770	O
+sobrepasando        cc_onco101	771_783	O
+la                  cc_onco101	784_786	O
+serosa              cc_onco101	787_793	O
+,                   cc_onco101	793_794	O
+infiltración        cc_onco101	795_807	O
+perineural          cc_onco101	808_818	O
+.                   cc_onco101	818_819	O
+</pre>
+
+### Data Splits
+
+* train: 18,916 tokens
+* development: 17,656 tokens
+* test: 10,886 tokens
+
+## Dataset Creation
+
+### Methodology
+
+TO DO
+
+### Curation Rationale
+
+For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+TO DO
+
+#### Who are the source language producers?
+
+TO DO
+
+### Annotations
+
+#### Annotation process
+
+TO DO
+
+#### Who are the annotators?
+
+TO DO
+
+### Dataset Curators
+
+TO DO: Martin?
+
+### Personal and Sensitive Information
+
+No personal or sensitive information included.
+
+## Contact
+
+TO DO: Casimiro?
+
+## License
+
+<a rel="license" href="https://creativecommons.org/licenses/by/4.0/"><img alt="Attribution 4.0 International License" style="border-width:0" src="https://chriszabriskie.com/img/cc-by.png" width="100"/></a><br />This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Attribution 4.0 International License</a>.

cantemist-ner.py

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+# Loading script for the Cantemist NER dataset. 
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{miranda2020named,
+  title={Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results},
+  author={Miranda-Escalada, A and Farr{\'e}, E and Krallinger, M},
+  booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings},
+  year={2020}
+}"""
+
+_DESCRIPTION = """\
+https://temu.bsc.es/cantemist/
+"""
+
+_URL = "https://huggingface.co/datasets/PlanTL-GOB-ES/pharmaconer/resolve/main/"
+# _URL = "./"
+_TRAINING_FILE = "train.conll"
+_DEV_FILE = "dev.conll"
+_TEST_FILE = "test.conll"
+
+class CantemistNerConfig(datasets.BuilderConfig):
+    """BuilderConfig for Cantemist Ner dataset"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for CantemistNer.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(CantemistNerConfig, self).__init__(**kwargs)
+
+
+class CantemistNer(datasets.GeneratorBasedBuilder):
+    """Cantemist Ner dataset."""
+
+    BUILDER_CONFIGS = [
+        CantemistNerConfig(
+            name="CantemistNer", 
+            version=datasets.Version("1.0.0"), 
+            description="CantemistNer dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-MORFOLOGIA_NEOPLASIA",
+                                "I-MORFOLOGIA_NEOPLASIA",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://temu.bsc.es/cantemist/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            pos_tags = []
+            ner_tags = []
+            for line in f:
+                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ner_tags = []
+                else:
+                    # Cantemist tokens are tab separated
+                    splits = line.split("\t")
+                    tokens.append(splits[0])
+                    ner_tags.append(splits[-1].rstrip())
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }

dev.conll

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test.conll

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train.conll

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