Host data file (#4)

- Host data file (ac17d9cd6ea48cc33821b712426771f488f15e51)
- Update loading script (cbbcbbc36fb15481c1fffe834f0dd9b8ca602cd1)
- Update metadata (72b11a4e51bc8c21dbb3599e021eda02ff2d3888)
- Delete legacy dataset_infos.json (e4970ed48d2a5ecce5b8a434afb325cc0b239ffa)

README.md

@@ -76,13 +76,18 @@ dataset_info:
 
 - **Homepage:** [SPECIES](https://species.jensenlab.org/)
 - **Repository:**
-- **Paper:**
+- **Paper:** https://doi.org/10.1371/journal.pone.0065390
 - **Leaderboard:**
-- **Point of Contact:**
+- **Point of Contact:** [Lars Juhl Jensen](mailto:[email protected])
 
 ### Dataset Summary
 
-[More Information Needed]
+S800 Corpus: a novel abstract-based manually annotated corpus. S800 comprises 800 PubMed abstracts in which organism mentions were identified and mapped to the corresponding NCBI Taxonomy identifiers.
+
+To increase the corpus taxonomic mention diversity the S800 abstracts were collected by selecting 100 abstracts from the following 8 categories: bacteriology, botany, entomology, medicine, mycology, protistology, virology and zoology. S800 has been annotated with a focus at the species level; however, higher taxa mentions (such as genera, families and orders) have also been considered.
+
+
+The Species-800 dataset was pre-processed and split based on the dataset of Pyysalo (https://github.com/spyysalo/s800).
 
 ### Supported Tasks and Leaderboards
 
@@ -90,13 +95,34 @@ dataset_info:
 
 ### Languages
 
-[More Information Needed]
+English (`en`).
 
 ## Dataset Structure
 
 ### Data Instances
 
-[More Information Needed]
+```
+{'id': '0',
+ 'tokens': ['Methanoregula',
+  'formicica',
+  'sp',
+  '.',
+  'nov',
+  '.',
+  ',',
+  'a',
+  'methane',
+  '-',
+  'producing',
+  'archaeon',
+  'isolated',
+  'from',
+  'methanogenic',
+  'sludge',
+  '.'],
+ 'ner_tags': [1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
+
+```
 
 ### Data Fields
 
@@ -160,11 +186,46 @@ dataset_info:
 
 ### Licensing Information
 
-[More Information Needed]
+The species-level S800 corpus is subject to Medline restrictions.
 
 ### Citation Information
 
-[More Information Needed]
+Original data:
+```
+@article{pafilis2013species,
+         title={The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text},
+         author={Pafilis, Evangelos and Frankild, Sune P and Fanini, Lucia and Faulwetter, Sarah and Pavloudi, Christina and Vasileiadou, Aikaterini and Arvanitidis, Christos and Jensen, Lars Juhl},
+         journal={PloS one},
+         volume={8},
+         number={6},
+         pages={e65390},
+         year={2013},
+         publisher={Public Library of Science}
+}
+```
+
+Source data of this dataset:
+```
+@article{10.1093/bioinformatics/btz682,
+    author = {Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
+    title = "{BioBERT: a pre-trained biomedical language representation model for biomedical text mining}",
+    journal = {Bioinformatics},
+    volume = {36},
+    number = {4},
+    pages = {1234-1240},
+    year = {2019},
+    month = {09},
+    issn = {1367-4803},
+    doi = {10.1093/bioinformatics/btz682},
+    url = {https://doi.org/10.1093/bioinformatics/btz682},
+    eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/4/1234/48983216/bioinformatics\_36\_4\_1234.pdf},
+}
+```
+and
+```
+https://github.com/spyysalo/s800
+```
+
 ### Contributions
 
 Thanks to [@edugp](https://github.com/edugp) for adding this dataset.

data/s800.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:11df652eb71f016b5918d8230fcac60709610eed1829232d5c2703d68545adc3
+size 463734

dataset_infos.json

@@ -1 +0,0 @@
-{"species_800": {"description": "We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition,\nwhich we here use to identifynames of species and other taxa in text. The tool, SPECIES, is more than an order of\nmagnitude faster and as accurate as existing tools. The precision and recall was assessed both on an existing gold-standard\ncorpus and on a new corpus of 800 abstracts, which were manually annotated after the development of the tool. The corpus\ncomprises abstracts from journals selected to represent many taxonomic groups, which gives insights into which types of\norganism names are hard to detect and which are easy. Finally, we have tagged organism names in the entire Medline database\nand developed a web resource, ORGANISMS, that makes the results accessible to the broad community of biologists.\n", "citation": "@article{pafilis2013species,\n         title={The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text},\n         author={Pafilis, Evangelos and Frankild, Sune P and Fanini, Lucia and Faulwetter, Sarah and Pavloudi, Christina and Vasileiadou, Aikaterini and Arvanitidis, Christos and Jensen, Lars Juhl},\n         journal={PloS one},\n         volume={8},\n         number={6},\n         pages={e65390},\n         year={2013},\n         publisher={Public Library of Science}\n}\n", "homepage": "https://species.jensenlab.org/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 3, "names": ["O", "B", "I"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "species800", "config_name": "species_800", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2579096, "num_examples": 5734, "dataset_name": "species800"}, "validation": {"name": "validation", "num_bytes": 385756, "num_examples": 831, "dataset_name": "species800"}, "test": {"name": "test", "num_bytes": 737760, "num_examples": 1631, "dataset_name": "species800"}}, "download_checksums": {"https://drive.google.com/u/0/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download/": {"num_bytes": 18204624, "checksum": "30522c752fd90e6da05f117a52da13174b246e4980e46840e6e1737dc67e1d27"}}, "download_size": 18204624, "post_processing_size": null, "dataset_size": 3702612, "size_in_bytes": 21907236}}

species_800.py

@@ -21,9 +21,6 @@ import os
 import datasets
 
 
-logger = datasets.logging.get_logger(__name__)
-
-
 _CITATION = """\
 @article{pafilis2013species,
          title={The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text},
@@ -48,7 +45,8 @@ and developed a web resource, ORGANISMS, that makes the results accessible to th
 """
 
 _HOMEPAGE = "https://species.jensenlab.org/"
-_URL = "https://drive.google.com/u/0/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download/"
+# Source data from: http://nlp.dmis.korea.edu/projects/biobert-2020-checkpoints/NERdata.zip
+_URL = "data/s800.zip"
 _BIOBERT_NER_DATASET_DIRECTORY = "s800"
 _TRAINING_FILE = "train.tsv"
 _DEV_FILE = "devel.tsv"
@@ -98,26 +96,22 @@ class Species800(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        urls_to_download = {
-            "biobert_ner_datasets": _URL,
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-        dataset_directory = os.path.join(downloaded_files["biobert_ner_datasets"], _BIOBERT_NER_DATASET_DIRECTORY)
+        dl_dir = dl_manager.download_and_extract(_URL)
+        data_dir = os.path.join(dl_dir, _BIOBERT_NER_DATASET_DIRECTORY)
 
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dataset_directory, _TRAINING_FILE)}
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, _TRAINING_FILE)}
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(dataset_directory, _DEV_FILE)}
+                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, _DEV_FILE)}
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dataset_directory, _TEST_FILE)}
+                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, _TEST_FILE)}
             ),
         ]
 
     def _generate_examples(self, filepath):
-        logger.info("⏳ Generating examples from = %s", filepath)
         with open(filepath, encoding="utf-8") as f:
             guid = 0
             tokens = []