How to use the flair.datasets.ClassificationCorpus function in flair

def test_train_resume_text_classification_training(results_base_path, tasks_base_path):
    corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb")
    label_dict = corpus.make_label_dictionary()

    embeddings: TokenEmbeddings = FlairEmbeddings("news-forward-fast")
    document_embeddings: DocumentRNNEmbeddings = DocumentRNNEmbeddings(
        [embeddings], 128, 1, False
    )

    model = TextClassifier(document_embeddings, label_dict, False)

    trainer = ModelTrainer(model, corpus)
    trainer.train(results_base_path, max_epochs=2, shuffle=False, checkpoint=True)

    trainer = ModelTrainer.load_checkpoint(results_base_path / "checkpoint.pt", corpus)
    trainer.train(results_base_path, max_epochs=2, shuffle=False, checkpoint=True)

    # clean up results directory

def test_train_load_use_classifier_with_prob(results_base_path, tasks_base_path):
    corpus = flair.datasets.ClassificationCorpus(tasks_base_path / "imdb")
    label_dict = corpus.make_label_dictionary()

    word_embedding: WordEmbeddings = WordEmbeddings("turian")
    document_embeddings: DocumentRNNEmbeddings = DocumentRNNEmbeddings(
        [word_embedding], 128, 1, False, 64, False, False
    )

    model: TextClassifier = TextClassifier(document_embeddings, label_dict, False)

    trainer = ModelTrainer(model, corpus)
    trainer.train(results_base_path, max_epochs=2, shuffle=False)

    sentence = Sentence("Berlin is a really nice city.")

    for s in model.predict(sentence, multi_class_prob=True):
        for l in s.labels:

def test_load_imdb_data_max_tokens(tasks_base_path):
    # get training, test and dev data
    corpus = flair.datasets.ClassificationCorpus(
        tasks_base_path / "imdb", in_memory=True, max_tokens_per_doc=3
    )

    assert len(corpus.train[0]) <= 3
    assert len(corpus.dev[0]) <= 3
    assert len(corpus.test[0]) <= 3

def test_load_imdb_data_streaming(tasks_base_path):
    # get training, test and dev data
    corpus = flair.datasets.ClassificationCorpus(
        tasks_base_path / "imdb", in_memory=False
    )

    assert len(corpus.train) == 5
    assert len(corpus.dev) == 5
    assert len(corpus.test) == 5

if split == "test":
                url = f"http://saifmohammad.com/WebDocs/EmoInt%20Test%20Gold%20Data/{emotion}-ratings-0to1.test.gold.txt"

            path = cached_path(url, Path("datasets") / dataset_name)

            with open(path, "r") as f:
                with open(data_file, "w") as out:
                    next(f)
                    for line in f:
                        fields = line.split("\t")
                        out.write(f"__label__{fields[3].rstrip()} {fields[1]}\n")

            os.remove(path)


class WASSA_ANGER(ClassificationCorpus):
    def __init__(self, base_path: Union[str, Path] = None, in_memory: bool = False):

        if type(base_path) == str:
            base_path: Path = Path(base_path)

        # 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
        _download_wassa_if_not_there("anger", data_folder, dataset_name)

dev: Dataset = ClassificationDataset(
                dev_file,
                tokenizer=tokenizer,
                max_tokens_per_doc=max_tokens_per_doc,
                max_chars_per_doc=max_chars_per_doc,
                in_memory=in_memory,
            )
        # otherwise, sample dev data from dev data
        else:
            train_length = len(train)
            dev_size: int = round(train_length / 10)
            splits = random_split(train, [train_length - dev_size, dev_size])
            train = splits[0]
            dev = splits[1]

        super(ClassificationCorpus, self).__init__(
            train, dev, test, name=data_folder.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
        _download_wassa_if_not_there("joy", data_folder, dataset_name)

        super(WASSA_JOY, self).__init__(
            data_folder, tokenizer=space_tokenizer, in_memory=in_memory
        )


class WASSA_SADNESS(ClassificationCorpus):
    def __init__(self, base_path: Union[str, Path] = None, in_memory: bool = False):

        if type(base_path) == str:
            base_path: Path = Path(base_path)

        # 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
        _download_wassa_if_not_there("sadness", data_folder, 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
        _download_wassa_if_not_there("fear", data_folder, dataset_name)

        super(WASSA_FEAR, self).__init__(
            data_folder, tokenizer=space_tokenizer, in_memory=in_memory
        )


class WASSA_JOY(ClassificationCorpus):
    def __init__(self, base_path: Union[str, Path] = None, in_memory: bool = False):

        if type(base_path) == str:
            base_path: Path = Path(base_path)

        # 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
        _download_wassa_if_not_there("joy", data_folder, dataset_name)

question = " ".join(fields[1:])

                            # Create flair compatible labels
                            # TREC-6 : NUM:dist -> __label__NUM
                            # TREC-50: NUM:dist -> __label__NUM:dist
                            new_label = "__label__"
                            new_label += old_label

                            write_fp.write(f"{new_label} {question}\n")

        super(TREC_50, self).__init__(
            data_folder, tokenizer=space_tokenizer, in_memory=in_memory
        )


class TREC_6(ClassificationCorpus):
    def __init__(self, base_path: Union[str, Path] = None, in_memory: bool = True):

        if type(base_path) == str:
            base_path: Path = Path(base_path)

        # 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
        trec_path = "https://cogcomp.seas.upenn.edu/Data/QA/QC/"