Upload folder using huggingface_hub

OLD/dev.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c46b5888a4fd7eb14225dd0db7074e40f22d51c5832903b58f14c44d582072f7
+size 513528

OLD/splitter.py

@@ -0,0 +1,41 @@
+import json
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+# both files downloaded from https://zenodo.org/record/4621378
+path_to_teca1 = 'dataset_te1.json'
+path_to_teca2 = 'dataset_te_vilaweb.json'
+
+# load data to pandas dataframes
+teca1 = pd.read_json(path_to_teca1) # Shape: (14997, 4)
+teca2 = pd.read_json(path_to_teca2) # Shape: (6166, 4)
+teca  = pd.concat([teca1, teca2])   # Shape: (21163, 4)
+
+# remove "id" column, now columns are: ['premise', 'hypothesis', 'label']
+teca.drop(['id'], axis=1, inplace=True)
+
+# shuffle rows
+teca = teca.sample(frac=1).reset_index(drop=True)
+
+# stratified split with harcoded percentages: 80% train, 10% dev, 10% test
+train, dev_test = train_test_split(teca, test_size=0.2, random_state=42, stratify=teca['label'])
+dev, test = train_test_split(dev_test, test_size=0.5, random_state=42, stratify=dev_test['label'])
+
+# report some stats
+print('### VALUE COUNTS TECA ###')
+print(teca['label'].value_counts())
+print('### VALUE COUNTS TRAIN ###')
+print(train['label'].value_counts())
+print('###  VALUE COUNTS DEV  ###')
+print(dev['label'].value_counts())
+print('### VALUE COUNTS TEST ###')
+print(test['label'].value_counts())
+print('train shape:', train.shape[0], ', dev shape:', dev.shape[0], ', test shape:', test.shape[0])
+
+# save train/dev/test sets as json files
+sets = {'train': train, 'dev': dev, 'test': test}
+for key in sets:
+    set_dict = sets[key].to_dict('records')
+    json_content = {"version": '1.0.1', "data": set_dict}
+    with open(key+'.json', 'w') as f:
+        json.dump(json_content, f)

OLD/splitter_with_ids.py

@@ -0,0 +1,42 @@
+import json
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+# both files downloaded from https://zenodo.org/record/4621378
+path_to_teca1 = 'dataset_te1.json'
+path_to_teca2 = 'dataset_te_vilaweb.json'
+
+teca1 = pd.read_json(path_to_teca1) # Shape: (14997, 4)
+teca2 = pd.read_json(path_to_teca2) # Shape: (6166, 4)
+
+teca1['id'] = 'te1_' + teca1['id'].astype(str)
+teca2['id'] = 'vila_' + teca2['id'].astype(str)
+
+teca = pd.concat([teca1, teca2])    # Shape: (21163, 4)
+#teca.drop(['id'], axis=1, inplace=True) # now columns are: ['premise', 'hypothesis', 'label']
+teca = teca.sample(frac=1).reset_index(drop=True) # shuffle rows
+
+print('### VALUE COUNTS TECA ###')
+print(teca['label'].value_counts())
+
+# stratified split with harcoded percentages: 80% train, 10% dev, 10% test
+train, dev_test = train_test_split(teca, test_size=0.2, random_state=42, stratify=teca['label'])
+dev, test = train_test_split(dev_test, test_size=0.5, random_state=42, stratify=dev_test['label'])
+
+print('### VALUE COUNTS TRAIN ###')
+print(train['label'].value_counts())
+print('###  VALUE COUNTS DEV  ###')
+print(dev['label'].value_counts())
+print('### VALUE COUNTS TEST ###')
+print(test['label'].value_counts())
+print('train shape:', train.shape[0], ', dev shape:', dev.shape[0], ', test shape:', test.shape[0])
+
+print(train.head())
+
+sets = {'train': train, 'dev': dev, 'test': test, 'full': teca}
+
+for key in sets:
+    set_dict = sets[key].to_dict('records')
+    json_content = {"version": '1.0.1', "data": set_dict}
+    with open(key+'.json', 'w') as f:
+        json.dump(json_content, f)

OLD/teca.py

@@ -0,0 +1,116 @@
+# Loading script for the TECA dataset.
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """
+            @inproceedings{armengol-estape-etal-2021-multilingual,
+                title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
+                author = "Armengol-Estap{\'e}, Jordi  and
+                  Carrino, Casimiro Pio  and
+                  Rodriguez-Penagos, Carlos  and
+                  de Gibert Bonet, Ona  and
+                  Armentano-Oller, Carme  and
+                  Gonzalez-Agirre, Aitor  and
+                  Melero, Maite  and
+                  Villegas, Marta",
+                booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
+                month = aug,
+                year = "2021",
+                address = "Online",
+                publisher = "Association for Computational Linguistics",
+                url = "https://aclanthology.org/2021.findings-acl.437",
+                doi = "10.18653/v1/2021.findings-acl.437",
+                pages = "4933--4946",
+            }
+            """
+
+_DESCRIPTION = """
+               TECA consists of two subsets of textual entailment in Catalan, *catalan_TE1* and *vilaweb_TE*, which contain 14997 and 6166 pairs of premises and hypotheses, annotated according to the inference relation they have (implication, contradiction or neutral). This dataset was developed by BSC TeMU as part of the AINA project and intended as part of the Catalan Language Understanding Benchmark (CLUB).
+               """
+
+_HOMEPAGE = """https://zenodo.org/record/4621378"""
+
+# TODO: upload datasets to github
+_URL = "https://huggingface.co/datasets/projecte-aina/teca/resolve/main/"
+_TRAINING_FILE = "train.json"
+_DEV_FILE = "dev.json"
+_TEST_FILE = "test.json"
+
+
+class tecaConfig(datasets.BuilderConfig):
+    """ Builder config for the TECA dataset """
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for TECA.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(tecaConfig, self).__init__(**kwargs)
+
+
+class teca(datasets.GeneratorBasedBuilder):
+    """ TECA Dataset """
+
+    BUILDER_CONFIGS = [
+        tecaConfig(
+            name="teca",
+            version=datasets.Version("1.0.1"),
+            description="teca dataset",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "premise": datasets.Value("string"),
+                    "hypothesis": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel
+                        (names=
+                    [
+                        "entailment",
+                        "neutral",
+                        "contradiction"
+                    ]
+                    ),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            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):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            data_dict = json.load(f)
+            for id_, article in enumerate(data_dict["data"]):
+                original_id = article["id"]
+                premise     = article["premise"]
+                hypothesis  = article["hypothesis"]
+                label       = article["label"]
+                yield id_, {
+                    "id":           original_id,
+                    "premise":      premise,
+                    "hypothesis":   hypothesis,
+                    "label":        label,
+                }

OLD/test.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe100977ffa0bf228cc0a032f26872374e031c928e0fa4692ddf617690afc83b
+size 509308

OLD/train.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1977a676bb22fdada80241c01dd6a8a52313535be25c6f4ef387d25b8fa2829c
+size 4100267