Repository: flairNLP/flair

Issue:
Not able to download the new datasets of flair 
While trying to download the dataset using the command mentioned below, I am not able to do so: sentiment_140 = flair.datasets.SENTIMENT_140()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-23-5e8cfec20052> in <module>
----> 1 sentiment_140 = flair.datasets.SENTIMENT_140()

AttributeError: module 'flair.datasets' has no attribute 'SENTIMENT_140'


Discussion:
Are you sure you're on version 0.5?
Yes.. version is 0.5 only

On Mon, Jun 8, 2020, 9:11 PM Alan Akbik <notifications@github.com> wrote:

> Are you sure you're on version 0.5?
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub
> <https://github.com/flairNLP/flair/issues/1664#issuecomment-640708860>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/APGJ6PEX665OREOTY667FLTRVUBBJANCNFSM4NS6G3UQ>
> .
>

@DivyaGupta1989 thanks for reporting this! This is a bug - we didn't add the new datasets to the `datasets.__init__.py` file. You can still load them like this: 

```python
from flair.datasets.document_classification import SENTIMENT_140
sentiment_140 = SENTIMENT_140()
```

I'll also put in a PR to fix this!

Resolving patch:
diff --git a/flair/datasets/__init__.py b/flair/datasets/__init__.py
--- a/flair/datasets/__init__.py
+++ b/flair/datasets/__init__.py
@@ -7,14 +7,20 @@
 # Expose all sequence labeling datasets
 from .sequence_labeling import ColumnCorpus
 from .sequence_labeling import ColumnDataset
+from .sequence_labeling import BIOFID
 from .sequence_labeling import CONLL_03
 from .sequence_labeling import CONLL_03_GERMAN
 from .sequence_labeling import CONLL_03_DUTCH
-from .sequence_labeling import NER_SWEDISH
 from .sequence_labeling import CONLL_03_SPANISH
 from .sequence_labeling import CONLL_2000
 from .sequence_labeling import DANE
 from .sequence_labeling import GERMEVAL_14
+from .sequence_labeling import INSPEC
+from .sequence_labeling import NER_BASQUE
+from .sequence_labeling import NER_FINNISH
+from .sequence_labeling import NER_SWEDISH
+from .sequence_labeling import SEMEVAL2010
+from .sequence_labeling import SEMEVAL2017
 from .sequence_labeling import WIKINER_ENGLISH
 from .sequence_labeling import WIKINER_GERMAN
 from .sequence_labeling import WIKINER_DUTCH
@@ -25,19 +31,16 @@
 from .sequence_labeling import WIKINER_POLISH
 from .sequence_labeling import WIKINER_RUSSIAN
 from .sequence_labeling import WNUT_17
-from .sequence_labeling import BIOFID
-from .sequence_labeling import NER_FINNISH
-from .sequence_labeling import INSPEC
-from .sequence_labeling import SEMEVAL2010
-from .sequence_labeling import SEMEVAL2017
 
 # Expose all document classification datasets
 from .document_classification import ClassificationCorpus
 from .document_classification import ClassificationDataset
 from .document_classification import CSVClassificationCorpus
 from .document_classification import CSVClassificationDataset
+from .document_classification import AMAZON_REVIEWS
 from .document_classification import IMDB
 from .document_classification import NEWSGROUPS
+from .document_classification import SENTIMENT_140
 from .document_classification import SENTEVAL_CR
 from .document_classification import SENTEVAL_MR
 from .document_classification import SENTEVAL_MPQA
diff --git a/flair/datasets/sequence_labeling.py b/flair/datasets/sequence_labeling.py
--- a/flair/datasets/sequence_labeling.py
+++ b/flair/datasets/sequence_labeling.py
@@ -267,6 +267,38 @@ def __getitem__(self, index: int = 0) -> Sentence:
         return sentence
 
 
+class BIOFID(ColumnCorpus):
+    def __init__(
+            self,
+            base_path: Union[str, Path] = None,
+            tag_to_bioes: str = "ner",
+            in_memory: bool = True,
+    ):
+        if type(base_path) == str:
+            base_path: Path = Path(base_path)
+
+        # column format
+        columns = {0: "text", 1: "lemma", 2: "pos", 3: "ner"}
+
+        # this dataset name
+        dataset_name = self.__class__.__name__.lower()
+
+        # default dataset folder is the cache root
+        if not base_path:
+            base_path = Path(flair.cache_root) / "datasets"
+        data_folder = base_path / dataset_name
+
+        # download data if necessary
+        biofid_path = "https://raw.githubusercontent.com/texttechnologylab/BIOfid/master/BIOfid-Dataset-NER/"
+        cached_path(f"{biofid_path}train.conll", Path("datasets") / dataset_name)
+        cached_path(f"{biofid_path}dev.conll", Path("datasets") / dataset_name)
+        cached_path(f"{biofid_path}test.conll", Path("datasets") / dataset_name)
+
+        super(BIOFID, self).__init__(
+            data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
+        )
+
+
 class CONLL_03(ColumnCorpus):
     def __init__(
             self,
@@ -417,55 +449,6 @@ def __init__(
         )
         
         
-class NER_SWEDISH(ColumnCorpus):
-    def __init__(
-            self,
-            base_path: Union[str, Path] = None,
-            tag_to_bioes: str = "ner",
-            in_memory: bool = True,
-    ):
-        """
-        Initialize the NER_SWEDISH corpus for Swedish. The first time you call this constructor it will automatically
-        download the dataset.
-        :param base_path: Default is None, meaning that corpus gets auto-downloaded and loaded. You can override this
-        to point to a different folder but typically this should not be necessary.
-        :param in_memory: If True, keeps dataset in memory giving speedups in training.
-        :param document_as_sequence: If True, all sentences of a document are read into a single Sentence object
-        """
-
-        if type(base_path) == str:
-            base_path: Path = Path(base_path)
-
-        # column format
-        columns = {0: "text", 1: "ner"}
-
-        # this dataset name
-        dataset_name = self.__class__.__name__.lower()
-
-        # default dataset folder is the cache root
-        if not base_path:
-            base_path = Path(flair.cache_root) / "datasets"
-        data_folder = base_path / dataset_name
-
-        # download data if necessary
-        ner_spraakbanken_path = "https://raw.githubusercontent.com/klintan/swedish-ner-corpus/master/"
-        cached_path(f"{ner_spraakbanken_path}test_corpus.txt", Path("datasets") / dataset_name)
-        cached_path(f"{ner_spraakbanken_path}train_corpus.txt", Path("datasets") / dataset_name)
-        
-       
-        #data is not in IOB2 format. Thus we transform it to IOB2
-        add_IOB2_tags(data_file=Path(data_folder / "test_corpus.txt"))
-        add_IOB2_tags(data_file=Path(data_folder / "train_corpus.txt"))
-        
-
-        super(NER_SWEDISH, self).__init__(
-            data_folder,
-            columns,
-            tag_to_bioes=tag_to_bioes,
-            in_memory=in_memory,
-        )
-        
-        
 def add_IOB2_tags(data_file: Union[str, Path], encoding: str = "utf8"):
         """
     Function that adds IOB2 tags if only chunk names are provided (e.g. words are tagged PER instead
@@ -723,6 +706,41 @@ def __init__(
         )
 
 
+class INSPEC(ColumnCorpus):
+    def __init__(
+        self,
+        base_path: Union[str, Path] = None,
+        tag_to_bioes: str = "keyword",
+        in_memory: bool = True,
+    ):
+
+        if type(base_path) == str:
+            base_path: Path = Path(base_path)
+
+        # column format
+        columns = {0: "text", 1: "keyword"}
+
+        # this dataset name
+        dataset_name = self.__class__.__name__.lower()
+
+        # default dataset folder is the cache root
+        if not base_path:
+            base_path = Path(flair.cache_root) / "datasets"
+        data_folder = base_path / dataset_name
+
+        inspec_path = "https://raw.githubusercontent.com/midas-research/keyphrase-extraction-as-sequence-labeling-data/master/Inspec"
+        cached_path(f"{inspec_path}/train.txt", Path("datasets") / dataset_name)
+        cached_path(f"{inspec_path}/test.txt", Path("datasets") / dataset_name)
+        if not "dev.txt" in os.listdir(data_folder):
+            cached_path(f"{inspec_path}/valid.txt", Path("datasets") / dataset_name)
+            #rename according to train - test - dev - convention
+            os.rename(data_folder / "valid.txt", data_folder / "dev.txt")
+
+        super(INSPEC, self).__init__(
+            data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
+        )
+
+
 class NER_BASQUE(ColumnCorpus):
     def __init__(
             self,
@@ -813,19 +831,27 @@ def _remove_lines_without_annotations(data_file: Union[str, Path] = None):
                     f.write(line)
 
 
-
-class WIKINER_ENGLISH(ColumnCorpus):
+class NER_SWEDISH(ColumnCorpus):
     def __init__(
             self,
             base_path: Union[str, Path] = None,
             tag_to_bioes: str = "ner",
-            in_memory: bool = False,
+            in_memory: bool = True,
     ):
+        """
+        Initialize the NER_SWEDISH corpus for Swedish. The first time you call this constructor it will automatically
+        download the dataset.
+        :param base_path: Default is None, meaning that corpus gets auto-downloaded and loaded. You can override this
+        to point to a different folder but typically this should not be necessary.
+        :param in_memory: If True, keeps dataset in memory giving speedups in training.
+        :param document_as_sequence: If True, all sentences of a document are read into a single Sentence object
+        """
+
         if type(base_path) == str:
             base_path: Path = Path(base_path)
 
         # column format
-        columns = {0: "text", 1: "pos", 2: "ner"}
+        columns = {0: "text", 1: "ner"}
 
         # this dataset name
         dataset_name = self.__class__.__name__.lower()
@@ -836,25 +862,35 @@ def __init__(
         data_folder = base_path / dataset_name
 
         # download data if necessary
-        _download_wikiner("en", dataset_name)
+        ner_spraakbanken_path = "https://raw.githubusercontent.com/klintan/swedish-ner-corpus/master/"
+        cached_path(f"{ner_spraakbanken_path}test_corpus.txt", Path("datasets") / dataset_name)
+        cached_path(f"{ner_spraakbanken_path}train_corpus.txt", Path("datasets") / dataset_name)
 
-        super(WIKINER_ENGLISH, self).__init__(
-            data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
+        # data is not in IOB2 format. Thus we transform it to IOB2
+        add_IOB2_tags(data_file=Path(data_folder / "test_corpus.txt"))
+        add_IOB2_tags(data_file=Path(data_folder / "train_corpus.txt"))
+
+        super(NER_SWEDISH, self).__init__(
+            data_folder,
+            columns,
+            tag_to_bioes=tag_to_bioes,
+            in_memory=in_memory,
         )
 
 
-class WIKINER_GERMAN(ColumnCorpus):
+class SEMEVAL2017(ColumnCorpus):
     def __init__(
-            self,
-            base_path: Union[str, Path] = None,
-            tag_to_bioes: str = "ner",
-            in_memory: bool = False,
+        self,
+        base_path: Union[str, Path] = None,
+        tag_to_bioes: str = "keyword",
+        in_memory: bool = True,
     ):
+
         if type(base_path) == str:
             base_path: Path = Path(base_path)
 
         # column format
-        columns = {0: "text", 1: "pos", 2: "ner"}
+        columns = {0: "text", 1: "keyword"}
 
         # this dataset name
         dataset_name = self.__class__.__name__.lower()
@@ -864,26 +900,29 @@ def __init__(
             base_path = Path(flair.cache_root) / "datasets"
         data_folder = base_path / dataset_name
 
-        # download data if necessary
-        _download_wikiner("de", dataset_name)
+        semeval2017_path = "https://raw.githubusercontent.com/midas-research/keyphrase-extraction-as-sequence-labeling-data/master/SemEval-2017"
+        cached_path(f"{semeval2017_path}/train.txt", Path("datasets") / dataset_name)
+        cached_path(f"{semeval2017_path}/test.txt", Path("datasets") / dataset_name)
+        cached_path(f"{semeval2017_path}/dev.txt", Path("datasets") / dataset_name)
 
-        super(WIKINER_GERMAN, self).__init__(
+        super(SEMEVAL2017, self).__init__(
             data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
         )
 
 
-class WIKINER_DUTCH(ColumnCorpus):
+class SEMEVAL2010(ColumnCorpus):
     def __init__(
-            self,
-            base_path: Union[str, Path] = None,
-            tag_to_bioes: str = "ner",
-            in_memory: bool = False,
+        self,
+        base_path: Union[str, Path] = None,
+        tag_to_bioes: str = "keyword",
+        in_memory: bool = True,
     ):
+
         if type(base_path) == str:
             base_path: Path = Path(base_path)
 
         # column format
-        columns = {0: "text", 1: "pos", 2: "ner"}
+        columns = {0: "text", 1: "keyword"}
 
         # this dataset name
         dataset_name = self.__class__.__name__.lower()
@@ -893,15 +932,16 @@ def __init__(
             base_path = Path(flair.cache_root) / "datasets"
         data_folder = base_path / dataset_name
 
-        # download data if necessary
-        _download_wikiner("nl", dataset_name)
+        semeval2010_path = "https://raw.githubusercontent.com/midas-research/keyphrase-extraction-as-sequence-labeling-data/master/processed_semeval-2010"
+        cached_path(f"{semeval2010_path}/train.txt", Path("datasets") / dataset_name)
+        cached_path(f"{semeval2010_path}/test.txt", Path("datasets") / dataset_name)
 
-        super(WIKINER_DUTCH, self).__init__(
+        super(SEMEVAL2010, self).__init__(
             data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
         )
 
 
-class WIKINER_FRENCH(ColumnCorpus):
+class WIKINER_ENGLISH(ColumnCorpus):
     def __init__(
             self,
             base_path: Union[str, Path] = None,
@@ -923,14 +963,14 @@ def __init__(
         data_folder = base_path / dataset_name
 
         # download data if necessary
-        _download_wikiner("fr", dataset_name)
+        _download_wikiner("en", dataset_name)
 
-        super(WIKINER_FRENCH, self).__init__(
+        super(WIKINER_ENGLISH, self).__init__(
             data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
         )
 
 
-class WIKINER_ITALIAN(ColumnCorpus):
+class WIKINER_GERMAN(ColumnCorpus):
     def __init__(
             self,
             base_path: Union[str, Path] = None,
@@ -952,14 +992,14 @@ def __init__(
         data_folder = base_path / dataset_name
 
         # download data if necessary
-        _download_wikiner("it", dataset_name)
+        _download_wikiner("de", dataset_name)
 
-        super(WIKINER_ITALIAN, self).__init__(
+        super(WIKINER_GERMAN, self).__init__(
             data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
         )
 
 
-class WIKINER_SPANISH(ColumnCorpus):
+class WIKINER_DUTCH(ColumnCorpus):
     def __init__(
             self,
             base_path: Union[str, Path] = None,
@@ -981,14 +1021,14 @@ def __init__(
         data_folder = base_path / dataset_name
 
         # download data if necessary
-        _download_wikiner("es", dataset_name)
+        _download_wikiner("nl", dataset_name)
 
-        super(WIKINER_SPANISH, self).__init__(
+        super(WIKINER_DUTCH, self).__init__(
             data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
         )
 
 
-class WIKINER_PORTUGUESE(ColumnCorpus):
+class WIKINER_FRENCH(ColumnCorpus):
     def __init__(
             self,
             base_path: Union[str, Path] = None,
@@ -1010,14 +1050,14 @@ def __init__(
         data_folder = base_path / dataset_name
 
         # download data if necessary
-        _download_wikiner("pt", dataset_name)
+        _download_wikiner("fr", dataset_name)
 
-        super(WIKINER_PORTUGUESE, self).__init__(
+        super(WIKINER_FRENCH, self).__init__(
             data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
         )
 
 
-class WIKINER_POLISH(ColumnCorpus):
+class WIKINER_ITALIAN(ColumnCorpus):
     def __init__(
             self,
             base_path: Union[str, Path] = None,
@@ -1039,14 +1079,14 @@ def __init__(
         data_folder = base_path / dataset_name
 
         # download data if necessary
-        _download_wikiner("pl", dataset_name)
+        _download_wikiner("it", dataset_name)
 
-        super(WIKINER_POLISH, self).__init__(
+        super(WIKINER_ITALIAN, self).__init__(
             data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
         )
 
 
-class WIKINER_RUSSIAN(ColumnCorpus):
+class WIKINER_SPANISH(ColumnCorpus):
     def __init__(
             self,
             base_path: Union[str, Path] = None,
@@ -1068,25 +1108,25 @@ def __init__(
         data_folder = base_path / dataset_name
 
         # download data if necessary
-        _download_wikiner("ru", dataset_name)
+        _download_wikiner("es", dataset_name)
 
-        super(WIKINER_RUSSIAN, self).__init__(
+        super(WIKINER_SPANISH, self).__init__(
             data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
         )
 
 
-class WNUT_17(ColumnCorpus):
+class WIKINER_PORTUGUESE(ColumnCorpus):
     def __init__(
             self,
             base_path: Union[str, Path] = None,
             tag_to_bioes: str = "ner",
-            in_memory: bool = True,
+            in_memory: bool = False,
     ):
         if type(base_path) == str:
             base_path: Path = Path(base_path)
 
         # column format
-        columns = {0: "text", 1: "ner"}
+        columns = {0: "text", 1: "pos", 2: "ner"}
 
         # this dataset name
         dataset_name = self.__class__.__name__.lower()
@@ -1097,30 +1137,25 @@ def __init__(
         data_folder = base_path / dataset_name
 
         # download data if necessary
-        wnut_path = "https://noisy-text.github.io/2017/files/"
-        cached_path(f"{wnut_path}wnut17train.conll", Path("datasets") / dataset_name)
-        cached_path(f"{wnut_path}emerging.dev.conll", Path("datasets") / dataset_name)
-        cached_path(
-            f"{wnut_path}emerging.test.annotated", Path("datasets") / dataset_name
-        )
+        _download_wikiner("pt", dataset_name)
 
-        super(WNUT_17, self).__init__(
+        super(WIKINER_PORTUGUESE, self).__init__(
             data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
         )
 
 
-class BIOFID(ColumnCorpus):
+class WIKINER_POLISH(ColumnCorpus):
     def __init__(
             self,
             base_path: Union[str, Path] = None,
             tag_to_bioes: str = "ner",
-            in_memory: bool = True,
+            in_memory: bool = False,
     ):
         if type(base_path) == str:
             base_path: Path = Path(base_path)
 
         # column format
-        columns = {0: "text", 1: "lemma", 2: "pos", 3: "ner"}
+        columns = {0: "text", 1: "pos", 2: "ner"}
 
         # this dataset name
         dataset_name = self.__class__.__name__.lower()
@@ -1131,28 +1166,25 @@ def __init__(
         data_folder = base_path / dataset_name
 
         # download data if necessary
-        biofid_path = "https://raw.githubusercontent.com/texttechnologylab/BIOfid/master/BIOfid-Dataset-NER/"
-        cached_path(f"{biofid_path}train.conll", Path("datasets") / dataset_name)
-        cached_path(f"{biofid_path}dev.conll", Path("datasets") / dataset_name)
-        cached_path(f"{biofid_path}test.conll", Path("datasets") / dataset_name)
+        _download_wikiner("pl", dataset_name)
 
-        super(BIOFID, self).__init__(
+        super(WIKINER_POLISH, self).__init__(
             data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
         )
 
-class INSPEC(ColumnCorpus):
+
+class WIKINER_RUSSIAN(ColumnCorpus):
     def __init__(
-        self,
-        base_path: Union[str, Path] = None,
-        tag_to_bioes: str = "keyword",
-        in_memory: bool = True,
+            self,
+            base_path: Union[str, Path] = None,
+            tag_to_bioes: str = "ner",
+            in_memory: bool = False,
     ):
-
         if type(base_path) == str:
             base_path: Path = Path(base_path)
 
         # column format
-        columns = {0: "text", 1: "keyword"}
+        columns = {0: "text", 1: "pos", 2: "ner"}
 
         # this dataset name
         dataset_name = self.__class__.__name__.lower()
@@ -1162,32 +1194,26 @@ def __init__(
             base_path = Path(flair.cache_root) / "datasets"
         data_folder = base_path / dataset_name
 
-        inspec_path = "https://raw.githubusercontent.com/midas-research/keyphrase-extraction-as-sequence-labeling-data/master/Inspec"
-        cached_path(f"{inspec_path}/train.txt", Path("datasets") / dataset_name)
-        cached_path(f"{inspec_path}/test.txt", Path("datasets") / dataset_name)
-        if not "dev.txt" in os.listdir(data_folder):
-            cached_path(f"{inspec_path}/valid.txt", Path("datasets") / dataset_name)
-            #rename according to train - test - dev - convention
-            os.rename(data_folder / "valid.txt", data_folder / "dev.txt")
+        # download data if necessary
+        _download_wikiner("ru", dataset_name)
 
-        super(INSPEC, self).__init__(
+        super(WIKINER_RUSSIAN, self).__init__(
             data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
         )
 
 
-class SEMEVAL2017(ColumnCorpus):
+class WNUT_17(ColumnCorpus):
     def __init__(
-        self,
-        base_path: Union[str, Path] = None,
-        tag_to_bioes: str = "keyword",
-        in_memory: bool = True,
+            self,
+            base_path: Union[str, Path] = None,
+            tag_to_bioes: str = "ner",
+            in_memory: bool = True,
     ):
-
         if type(base_path) == str:
             base_path: Path = Path(base_path)
 
         # column format
-        columns = {0: "text", 1: "keyword"}
+        columns = {0: "text", 1: "ner"}
 
         # this dataset name
         dataset_name = self.__class__.__name__.lower()
@@ -1197,45 +1223,19 @@ def __init__(
             base_path = Path(flair.cache_root) / "datasets"
         data_folder = base_path / dataset_name
 
-        semeval2017_path = "https://raw.githubusercontent.com/midas-research/keyphrase-extraction-as-sequence-labeling-data/master/SemEval-2017"
-        cached_path(f"{semeval2017_path}/train.txt", Path("datasets") / dataset_name)
-        cached_path(f"{semeval2017_path}/test.txt", Path("datasets") / dataset_name)
-        cached_path(f"{semeval2017_path}/dev.txt", Path("datasets") / dataset_name)
-
-        super(SEMEVAL2017, self).__init__(
-            data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
+        # download data if necessary
+        wnut_path = "https://noisy-text.github.io/2017/files/"
+        cached_path(f"{wnut_path}wnut17train.conll", Path("datasets") / dataset_name)
+        cached_path(f"{wnut_path}emerging.dev.conll", Path("datasets") / dataset_name)
+        cached_path(
+            f"{wnut_path}emerging.test.annotated", Path("datasets") / dataset_name
         )
 
-class SEMEVAL2010(ColumnCorpus):
-    def __init__(
-        self,
-        base_path: Union[str, Path] = None,
-        tag_to_bioes: str = "keyword",
-        in_memory: bool = True,
-    ):
-
-        if type(base_path) == str:
-            base_path: Path = Path(base_path)
-
-        # column format
-        columns = {0: "text", 1: "keyword"}
-
-        # this dataset name
-        dataset_name = self.__class__.__name__.lower()
-
-        # default dataset folder is the cache root
-        if not base_path:
-            base_path = Path(flair.cache_root) / "datasets"
-        data_folder = base_path / dataset_name
-
-        semeval2010_path = "https://raw.githubusercontent.com/midas-research/keyphrase-extraction-as-sequence-labeling-data/master/processed_semeval-2010"
-        cached_path(f"{semeval2010_path}/train.txt", Path("datasets") / dataset_name)
-        cached_path(f"{semeval2010_path}/test.txt", Path("datasets") / dataset_name)
-
-        super(SEMEVAL2010, self).__init__(
+        super(WNUT_17, self).__init__(
             data_folder, columns, tag_to_bioes=tag_to_bioes, in_memory=in_memory
         )
 
+
 def _download_wikiner(language_code: str, dataset_name: str):
     # download data if necessary
     wikiner_path = (


Test patch: