def METHOD_NAME(self) -> List[Instance]:
    """An example of a message in the dataset:
    {
        'message_id': '6ab24d72-0181-4594-a9cd-deaf170242fb',
        'parent_id': None,
        'user_id': 'c3fe8c76-fc30-4fa7-b7f8-c492f5967d18',
        'created_date': '2023-02-05T14:23:50.983374+00:00',
        'text': 'Can you write a short introduction about the relevance of the term "monopsony" in economics?'
                'Please use examples related to potential monopsonies in the labour market and'
                'cite relevant research.',
        'role': 'prompter',
        'lang': 'en',
        'review_count': 3,
        'review_result': True,
        'deleted': False,
        'rank': None,
        'synthetic': False,
        'model_name': None,
        'detoxify': {
            'toxicity': 0.00044308538781479,
            'severe_toxicity': 3.252684837207198e-05,
            'obscene': 0.00023475120542570949,
            'identity_attack': 0.0001416115992469713,
            'insult': 0.00039489680784754455,
            'threat': 4.075629112776369e-05,
            'sexual_explicit': 2.712695459194947e-05
        },
        'message_tree_id': '6ab24d72-0181-4594-a9cd-deaf170242fb',
        'tree_state': 'ready_for_export',
        'emojis': {
            'name': ['+1', '_skip_reply', '_skip_ranking'],
            'count': [10, 1, 4]
        },
        'labels': {
            'name': ['spam', 'lang_mismatch', 'pii', 'not_appropriate', 'hate_speech', 'sexual_content',
                    'quality', 'toxicity', 'humor', 'creativity','violence'],
            'value': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9166666666666666, 0.16666666666666666, 0.3333333333333333,
                    0.6666666666666666, 0.0],
            'count': [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
        }
    }
    """
    def matches_target_language(msg):
        """Check if the message is in the target language"""
        if self.language == "all":
            return True
        else:
            return msg["lang"] == self.language
    def get_split_instances(split: Dataset, split_tag: str):
        """Get instances from a split (e.g. train) of the dataset"""
        # Step 1: get all initial prompts (roots of the conversation trees)
        initial_prompts: Dict[str, str] = {}
        for msg in split:
            assert msg["model_name"] is None
            if msg["parent_id"] is None and matches_target_language(msg):
                assert msg["role"] == "prompter"
                assert msg["message_id"] not in initial_prompts
                initial_prompts[msg["message_id"]] = msg["text"]
        # Step 2: for each initial prompt, find the corresponding assistant messages.
        # Each initial prompt can have multiple assistant responses.
        prompt_responses: DefaultDict[str, List[str]] = defaultdict(list)
        for msg in split:
            if msg["role"] == "assistant" and msg["parent_id"] in initial_prompts and matches_target_language(msg):
                prompt_responses[msg["parent_id"]].append(msg["text"])
        # Step 3: for each initial prompt, create an instance
        instances: List[Instance] = []
        for msg_id, prompt in initial_prompts.items():
            reference_texts: List[str] = []
            if msg_id in prompt_responses:
                reference_texts = prompt_responses[msg_id]
            instance = Instance(
                input=Input(text=prompt),
                references=[Reference(Output(text=ref), tags=[CORRECT_TAG]) for ref in reference_texts],
                split=split_tag,
            )
            instances.append(instance)
        return instances
    # Download the raw data from Huggingface
    dataset: Any = load_dataset("OpenAssistant/oasst1")
    # Get the instances for each split
    train_instances = get_split_instances(dataset["train"], TRAIN_SPLIT)
    valid_instances = get_split_instances(dataset["validation"], VALID_SPLIT)
    return train_instances + valid_instances