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Dataset

InMemoryPerTokenValueDataset

Bases: InMemoryProteinDataset

An in-memory dataset of labeled strings, which are tokenized on demand.

Source code in bionemo/esm2/model/finetune/dataset.py 
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class InMemoryPerTokenValueDataset(InMemoryProteinDataset):
    """An in-memory dataset of labeled strings, which are tokenized on demand."""

    def __init__(
        self,
        sequences: pd.Series,
        labels: pd.Series,
        task_type: Literal["classification", "regression"] = "classification",
        tokenizer: tokenizer.BioNeMoESMTokenizer = tokenizer.get_tokenizer(),
        seed: int = np.random.SeedSequence().entropy,  # type: ignore
    ):
        """Initializes a dataset for per-token classification fine-tuning.

        This is an in-memory dataset that does not apply masking to the sequence. But keeps track of <mask> in the
        dataset sequences provided.

        Args:
            sequences (pd.Series): A pandas Series containing protein sequences.
            labels (pd.Series, optional): A pandas Series containing labels. Defaults to None.
            task_type (str): Fine-tuning task type. Defaults to classification. Regression per-token values are not supported.
            tokenizer (tokenizer.BioNeMoESMTokenizer, optional): The tokenizer to use. Defaults to tokenizer.get_tokenizer().
            seed: Random seed for reproducibility. This seed is mixed with the index of the sample to retrieve to ensure
                that __getitem__ is deterministic, but can be random across different runs. If None, a random seed is
                generated.
        """
        super().__init__(sequences, labels, task_type, tokenizer, seed)

        self.task_type = task_type
        if not task_type == "classification":
            raise ValueError(f"{task_type} task type is not supported with {self.__class__.__name__}")

        label_tokenizer = Label2IDTokenizer()
        self.label_tokenizer = label_tokenizer.build_vocab(self.labels.sort_values(inplace=False).values)
        self.label_cls_eos_id = MLM_LOSS_IGNORE_INDEX

    def transform_label(self, label: str) -> Tensor:
        """Transform the sequence label by tokenizing them.

        This method tokenizes a sequence of labels into a tensor of tokens and adds CLS/EOS tokens.

        Args:
            label: label sequence to be transformed

        Returns:
            tokenized label
        """
        tokenized_labels = torch.tensor(self.label_tokenizer.text_to_ids(label))

        # for multi-class (mutually exclusive) classification with CrossEntropyLoss
        cls_eos = torch.tensor([self.label_cls_eos_id], dtype=tokenized_labels.dtype)

        # add cls / eos label ids with padding value -100 to have the same shape as tokenized_sequence
        labels = torch.cat((cls_eos, tokenized_labels, cls_eos))
        return labels

__init__(sequences, labels, task_type='classification', tokenizer=tokenizer.get_tokenizer(), seed=np.random.SeedSequence().entropy)

Initializes a dataset for per-token classification fine-tuning.

This is an in-memory dataset that does not apply masking to the sequence. But keeps track of in the dataset sequences provided.

Parameters:

Name Type Description Default
sequences Series

A pandas Series containing protein sequences.

 required
labels Series

A pandas Series containing labels. Defaults to None.

 required
task_type str

Fine-tuning task type. Defaults to classification. Regression per-token values are not supported.

 'classification'
tokenizer BioNeMoESMTokenizer

The tokenizer to use. Defaults to tokenizer.get_tokenizer().

 get_tokenizer()
seed int

Random seed for reproducibility. This seed is mixed with the index of the sample to retrieve to ensure that getitem is deterministic, but can be random across different runs. If None, a random seed is generated.

 entropy
Source code in bionemo/esm2/model/finetune/dataset.py 
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def __init__(
    self,
    sequences: pd.Series,
    labels: pd.Series,
    task_type: Literal["classification", "regression"] = "classification",
    tokenizer: tokenizer.BioNeMoESMTokenizer = tokenizer.get_tokenizer(),
    seed: int = np.random.SeedSequence().entropy,  # type: ignore
):
    """Initializes a dataset for per-token classification fine-tuning.

    This is an in-memory dataset that does not apply masking to the sequence. But keeps track of <mask> in the
    dataset sequences provided.

    Args:
        sequences (pd.Series): A pandas Series containing protein sequences.
        labels (pd.Series, optional): A pandas Series containing labels. Defaults to None.
        task_type (str): Fine-tuning task type. Defaults to classification. Regression per-token values are not supported.
        tokenizer (tokenizer.BioNeMoESMTokenizer, optional): The tokenizer to use. Defaults to tokenizer.get_tokenizer().
        seed: Random seed for reproducibility. This seed is mixed with the index of the sample to retrieve to ensure
            that __getitem__ is deterministic, but can be random across different runs. If None, a random seed is
            generated.
    """
    super().__init__(sequences, labels, task_type, tokenizer, seed)

    self.task_type = task_type
    if not task_type == "classification":
        raise ValueError(f"{task_type} task type is not supported with {self.__class__.__name__}")

    label_tokenizer = Label2IDTokenizer()
    self.label_tokenizer = label_tokenizer.build_vocab(self.labels.sort_values(inplace=False).values)
    self.label_cls_eos_id = MLM_LOSS_IGNORE_INDEX

transform_label(label)

Transform the sequence label by tokenizing them.

This method tokenizes a sequence of labels into a tensor of tokens and adds CLS/EOS tokens.

Parameters:

Name Type Description Default
label str

label sequence to be transformed

 required

Returns:

Type Description
Tensor

tokenized label
Source code in bionemo/esm2/model/finetune/dataset.py 
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def transform_label(self, label: str) -> Tensor:
    """Transform the sequence label by tokenizing them.

    This method tokenizes a sequence of labels into a tensor of tokens and adds CLS/EOS tokens.

    Args:
        label: label sequence to be transformed

    Returns:
        tokenized label
    """
    tokenized_labels = torch.tensor(self.label_tokenizer.text_to_ids(label))

    # for multi-class (mutually exclusive) classification with CrossEntropyLoss
    cls_eos = torch.tensor([self.label_cls_eos_id], dtype=tokenized_labels.dtype)

    # add cls / eos label ids with padding value -100 to have the same shape as tokenized_sequence
    labels = torch.cat((cls_eos, tokenized_labels, cls_eos))
    return labels

InMemoryProteinDataset

Bases: Dataset

An in-memory dataset that tokenize strings into BertSample instances.

Source code in bionemo/esm2/model/finetune/dataset.py 
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class InMemoryProteinDataset(Dataset):
    """An in-memory dataset that tokenize strings into BertSample instances."""

    def __init__(
        self,
        sequences: pd.Series,
        labels: pd.Series | None = None,
        task_type: Literal["classification", "regression", None] = None,
        tokenizer: tokenizer.BioNeMoESMTokenizer = tokenizer.get_tokenizer(),
        seed: int = np.random.SeedSequence().entropy,  # type: ignore
    ):
        """Initializes a dataset of protein sequences.

        This is an in-memory dataset that does not apply masking to the sequence. But keeps track of <mask> in the
        dataset sequences provided.

        Args:
            sequences (pd.Series): A pandas Series containing protein sequences.
            labels (pd.Series, optional): A pandas Series containing labels. Defaults to None.
            task_type (str, optional): Fine-tuning task type. Defaults to None.
            tokenizer (tokenizer.BioNeMoESMTokenizer, optional): The tokenizer to use. Defaults to tokenizer.get_tokenizer().
            seed: Random seed for reproducibility. This seed is mixed with the index of the sample to retrieve to ensure
                that __getitem__ is deterministic, but can be random across different runs. If None, a random seed is
                generated.
        """
        self.sequences = sequences
        self.labels = labels
        self.task_type = task_type

        self.seed = seed
        self._len = len(self.sequences)
        self.tokenizer = tokenizer

    @classmethod
    def from_csv(
        cls,
        csv_path: str | os.PathLike,
        task_type: Literal["classification", "regression", None] = None,
        tokenizer: tokenizer.BioNeMoESMTokenizer = tokenizer.get_tokenizer(),
        ignore_labels: bool = False,
        label_column: str = "labels",
    ):
        """Class method to create a ProteinDataset instance from a CSV file.

        Args:
            csv_path: path to CSV file containing sequences and optionally labels column.
            task_type (str, optional): Fine-tuning task type. Defaults to None.
            tokenizer (tokenizer.BioNeMoESMTokenizer, optional): The tokenizer to use. Defaults to tokenizer.get_tokenizer().
            ignore_labels (bool): ignore labels column if exist (to avoid reading labels during inference)
            label_column (str): label column name in CSV file. Defaults to `labels`.
        """
        df = pd.read_csv(csv_path)

        # Validate presence of required columns
        if "sequences" not in df.columns:
            raise KeyError("The CSV must contain a 'sequences' column.")

        sequences = df["sequences"]
        labels = None
        if not ignore_labels:
            labels = df[label_column]

        return cls(sequences, labels=labels, task_type=task_type, tokenizer=tokenizer)

    def __len__(self) -> int:
        """The size of the dataset."""
        return self._len

    def __getitem__(self, index: int) -> BertSample:
        """Obtains the BertSample at the given index."""
        sequence = self.sequences[index]
        tokenized_sequence = self._tokenize(sequence)

        label = tokenized_sequence if self.labels is None else self.transform_label(self.labels.iloc[index])
        # Overall mask for a token being masked in some capacity - either mask token, random token, or left as-is
        loss_mask = ~torch.isin(tokenized_sequence, Tensor(self.tokenizer.all_special_ids))

        return {
            "text": tokenized_sequence,
            "types": torch.zeros_like(tokenized_sequence, dtype=torch.int64),
            "attention_mask": torch.ones_like(tokenized_sequence, dtype=torch.int64),
            "labels": label,
            "loss_mask": loss_mask,
            "is_random": torch.zeros_like(tokenized_sequence, dtype=torch.int64),
        }

    def _tokenize(self, sequence: str) -> Tensor:
        """Tokenize a protein sequence.

        Args:
            sequence: The protein sequence.

        Returns:
            The tokenized sequence.
        """
        tensor = self.tokenizer.encode(sequence, add_special_tokens=True, return_tensors="pt")
        return tensor.flatten()  # type: ignore

    def transform_label(self, label):
        """Transform the label.

        This method should be implemented by subclass if label needs additional transformation.

        Args:
            label: label to be transformed

        Returns:
            transformed_label
        """
        return label

__getitem__(index)

Obtains the BertSample at the given index.

Source code in bionemo/esm2/model/finetune/dataset.py 
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def __getitem__(self, index: int) -> BertSample:
    """Obtains the BertSample at the given index."""
    sequence = self.sequences[index]
    tokenized_sequence = self._tokenize(sequence)

    label = tokenized_sequence if self.labels is None else self.transform_label(self.labels.iloc[index])
    # Overall mask for a token being masked in some capacity - either mask token, random token, or left as-is
    loss_mask = ~torch.isin(tokenized_sequence, Tensor(self.tokenizer.all_special_ids))

    return {
        "text": tokenized_sequence,
        "types": torch.zeros_like(tokenized_sequence, dtype=torch.int64),
        "attention_mask": torch.ones_like(tokenized_sequence, dtype=torch.int64),
        "labels": label,
        "loss_mask": loss_mask,
        "is_random": torch.zeros_like(tokenized_sequence, dtype=torch.int64),
    }

__init__(sequences, labels=None, task_type=None, tokenizer=tokenizer.get_tokenizer(), seed=np.random.SeedSequence().entropy)

Initializes a dataset of protein sequences.

This is an in-memory dataset that does not apply masking to the sequence. But keeps track of in the dataset sequences provided.

Parameters:

Name Type Description Default
sequences Series

A pandas Series containing protein sequences.

 required
labels Series

A pandas Series containing labels. Defaults to None.

 None
task_type str

Fine-tuning task type. Defaults to None.

 None
tokenizer BioNeMoESMTokenizer

The tokenizer to use. Defaults to tokenizer.get_tokenizer().

 get_tokenizer()
seed int

Random seed for reproducibility. This seed is mixed with the index of the sample to retrieve to ensure that getitem is deterministic, but can be random across different runs. If None, a random seed is generated.

 entropy
Source code in bionemo/esm2/model/finetune/dataset.py 
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def __init__(
    self,
    sequences: pd.Series,
    labels: pd.Series | None = None,
    task_type: Literal["classification", "regression", None] = None,
    tokenizer: tokenizer.BioNeMoESMTokenizer = tokenizer.get_tokenizer(),
    seed: int = np.random.SeedSequence().entropy,  # type: ignore
):
    """Initializes a dataset of protein sequences.

    This is an in-memory dataset that does not apply masking to the sequence. But keeps track of <mask> in the
    dataset sequences provided.

    Args:
        sequences (pd.Series): A pandas Series containing protein sequences.
        labels (pd.Series, optional): A pandas Series containing labels. Defaults to None.
        task_type (str, optional): Fine-tuning task type. Defaults to None.
        tokenizer (tokenizer.BioNeMoESMTokenizer, optional): The tokenizer to use. Defaults to tokenizer.get_tokenizer().
        seed: Random seed for reproducibility. This seed is mixed with the index of the sample to retrieve to ensure
            that __getitem__ is deterministic, but can be random across different runs. If None, a random seed is
            generated.
    """
    self.sequences = sequences
    self.labels = labels
    self.task_type = task_type

    self.seed = seed
    self._len = len(self.sequences)
    self.tokenizer = tokenizer

__len__()

The size of the dataset.

Source code in bionemo/esm2/model/finetune/dataset.py 
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def __len__(self) -> int:
    """The size of the dataset."""
    return self._len

_tokenize(sequence)

Tokenize a protein sequence.

Parameters:

Name Type Description Default
sequence str

The protein sequence.

 required

Returns:

Type Description
Tensor

The tokenized sequence.
Source code in bionemo/esm2/model/finetune/dataset.py 
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def _tokenize(self, sequence: str) -> Tensor:
    """Tokenize a protein sequence.

    Args:
        sequence: The protein sequence.

    Returns:
        The tokenized sequence.
    """
    tensor = self.tokenizer.encode(sequence, add_special_tokens=True, return_tensors="pt")
    return tensor.flatten()  # type: ignore

from_csv(csv_path, task_type=None, tokenizer=tokenizer.get_tokenizer(), ignore_labels=False, label_column='labels') classmethod

Class method to create a ProteinDataset instance from a CSV file.

Parameters:

Name Type Description Default
csv_path str | PathLike

path to CSV file containing sequences and optionally labels column.

 required
task_type str

Fine-tuning task type. Defaults to None.

 None
tokenizer BioNeMoESMTokenizer

The tokenizer to use. Defaults to tokenizer.get_tokenizer().

 get_tokenizer()
ignore_labels bool

ignore labels column if exist (to avoid reading labels during inference)

 False
label_column str

label column name in CSV file. Defaults to labels.

 'labels'
Source code in bionemo/esm2/model/finetune/dataset.py 
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@classmethod
def from_csv(
    cls,
    csv_path: str | os.PathLike,
    task_type: Literal["classification", "regression", None] = None,
    tokenizer: tokenizer.BioNeMoESMTokenizer = tokenizer.get_tokenizer(),
    ignore_labels: bool = False,
    label_column: str = "labels",
):
    """Class method to create a ProteinDataset instance from a CSV file.

    Args:
        csv_path: path to CSV file containing sequences and optionally labels column.
        task_type (str, optional): Fine-tuning task type. Defaults to None.
        tokenizer (tokenizer.BioNeMoESMTokenizer, optional): The tokenizer to use. Defaults to tokenizer.get_tokenizer().
        ignore_labels (bool): ignore labels column if exist (to avoid reading labels during inference)
        label_column (str): label column name in CSV file. Defaults to `labels`.
    """
    df = pd.read_csv(csv_path)

    # Validate presence of required columns
    if "sequences" not in df.columns:
        raise KeyError("The CSV must contain a 'sequences' column.")

    sequences = df["sequences"]
    labels = None
    if not ignore_labels:
        labels = df[label_column]

    return cls(sequences, labels=labels, task_type=task_type, tokenizer=tokenizer)

transform_label(label)

Transform the label.

This method should be implemented by subclass if label needs additional transformation.

Parameters:

Name Type Description Default
label

label to be transformed

 required

Returns:

Type Description

transformed_label
Source code in bionemo/esm2/model/finetune/dataset.py 
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def transform_label(self, label):
    """Transform the label.

    This method should be implemented by subclass if label needs additional transformation.

    Args:
        label: label to be transformed

    Returns:
        transformed_label
    """
    return label

InMemorySingleValueDataset

Bases: InMemoryProteinDataset

An in-memory dataset that tokenizes strings into BertSample instances.

Source code in bionemo/esm2/model/finetune/dataset.py 
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class InMemorySingleValueDataset(InMemoryProteinDataset):
    """An in-memory dataset that tokenizes strings into BertSample instances."""

    def __init__(
        self,
        sequences: pd.Series,
        labels: pd.Series,
        task_type: Literal["classification", "regression"] = "regression",
        tokenizer: tokenizer.BioNeMoESMTokenizer = tokenizer.get_tokenizer(),
        seed: int = np.random.SeedSequence().entropy,  # type: ignore
    ):
        """Initializes a dataset for single-value fine-tuning.

        This is an in-memory dataset that does not apply masking to the sequence. But keeps track of <mask> in the
        dataset sequences provided.

        Args:
            sequences (pd.Series): A pandas Series containing protein sequences.
            labels (pd.Series, optional): A pandas Series containing labels. Defaults to None.
            task_type (str): Fine-tuning task type. Defaults to regression.
            tokenizer (tokenizer.BioNeMoESMTokenizer, optional): The tokenizer to use. Defaults to tokenizer.get_tokenizer().
            seed: Random seed for reproducibility. This seed is mixed with the index of the sample to retrieve to ensure
                that __getitem__ is deterministic, but can be random across different runs. If None, a random seed is
                generated.
        """
        super().__init__(sequences, labels, task_type, tokenizer, seed)

        self.task_type = task_type
        if self.task_type == "classification":
            label_tokenizer = Label2IDTokenizer()
            self.label_tokenizer = label_tokenizer.build_vocab(
                self.labels.sort_values(inplace=False).values.reshape(-1, 1)
            )

    def transform_label(self, label: float | str) -> Tensor:
        """Transform the regression label.

        Args:
            label: single regression/classification value

        Returns:
            tokenized label
        """
        if self.task_type == "regression":
            return torch.tensor([label], dtype=torch.float)
        elif self.task_type == "classification":
            tokenized_label = torch.tensor(self.label_tokenizer.text_to_ids([label]))
            return tokenized_label
        else:
            raise ValueError(f"{self.task_type} task type is not supported with {self.__class__.__name__}")

__init__(sequences, labels, task_type='regression', tokenizer=tokenizer.get_tokenizer(), seed=np.random.SeedSequence().entropy)

Initializes a dataset for single-value fine-tuning.

This is an in-memory dataset that does not apply masking to the sequence. But keeps track of in the dataset sequences provided.

Parameters:

Name Type Description Default
sequences Series

A pandas Series containing protein sequences.

 required
labels Series

A pandas Series containing labels. Defaults to None.

 required
task_type str

Fine-tuning task type. Defaults to regression.

 'regression'
tokenizer BioNeMoESMTokenizer

The tokenizer to use. Defaults to tokenizer.get_tokenizer().

 get_tokenizer()
seed int

Random seed for reproducibility. This seed is mixed with the index of the sample to retrieve to ensure that getitem is deterministic, but can be random across different runs. If None, a random seed is generated.

 entropy
Source code in bionemo/esm2/model/finetune/dataset.py 
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def __init__(
    self,
    sequences: pd.Series,
    labels: pd.Series,
    task_type: Literal["classification", "regression"] = "regression",
    tokenizer: tokenizer.BioNeMoESMTokenizer = tokenizer.get_tokenizer(),
    seed: int = np.random.SeedSequence().entropy,  # type: ignore
):
    """Initializes a dataset for single-value fine-tuning.

    This is an in-memory dataset that does not apply masking to the sequence. But keeps track of <mask> in the
    dataset sequences provided.

    Args:
        sequences (pd.Series): A pandas Series containing protein sequences.
        labels (pd.Series, optional): A pandas Series containing labels. Defaults to None.
        task_type (str): Fine-tuning task type. Defaults to regression.
        tokenizer (tokenizer.BioNeMoESMTokenizer, optional): The tokenizer to use. Defaults to tokenizer.get_tokenizer().
        seed: Random seed for reproducibility. This seed is mixed with the index of the sample to retrieve to ensure
            that __getitem__ is deterministic, but can be random across different runs. If None, a random seed is
            generated.
    """
    super().__init__(sequences, labels, task_type, tokenizer, seed)

    self.task_type = task_type
    if self.task_type == "classification":
        label_tokenizer = Label2IDTokenizer()
        self.label_tokenizer = label_tokenizer.build_vocab(
            self.labels.sort_values(inplace=False).values.reshape(-1, 1)
        )

transform_label(label)

Transform the regression label.

Parameters:

Name Type Description Default
label float | str

single regression/classification value

 required

Returns:

Type Description
Tensor

tokenized label
Source code in bionemo/esm2/model/finetune/dataset.py 
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def transform_label(self, label: float | str) -> Tensor:
    """Transform the regression label.

    Args:
        label: single regression/classification value

    Returns:
        tokenized label
    """
    if self.task_type == "regression":
        return torch.tensor([label], dtype=torch.float)
    elif self.task_type == "classification":
        tokenized_label = torch.tensor(self.label_tokenizer.text_to_ids([label]))
        return tokenized_label
    else:
        raise ValueError(f"{self.task_type} task type is not supported with {self.__class__.__name__}")