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HuggingFace

Transformer pretrained models

HuggingFace Transformers

HFExperiment dataclass

Source code in molfeat/trans/pretrained/hf_transformers.py
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@dataclass
class HFExperiment:
    model: PreTrainedModel
    tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
    notation: str = "smiles"

    @classmethod
    def save(cls, model: HFExperiment, path: str, clean_up: bool = False):
        """Save a hugging face model to a specific path

        Args:
            model: model to save
            path: path to the folder root where to save the model
            clean_up: whether to clean up the local path after saving
        """
        local_path = tempfile.mkdtemp()
        # we can save both the tokenizer and the model to the same path
        model.model.save_pretrained(local_path)
        model.tokenizer.save_pretrained(local_path)

        # With transformers>=4.35.0, models are by default saved as safetensors.
        # For backwards compatibility, we also save the model as the older pickle-based format.
        model.model.save_pretrained(local_path, safe_serialization=False)

        dm.fs.copy_dir(local_path, path, force=True, progress=True, leave_progress=False)
        logger.info(f"Model saved to {path}")
        # clean up now
        if clean_up:
            mapper = dm.fs.get_mapper(local_path)
            mapper.fs.delete(local_path, recursive=True)
        return path

    @classmethod
    def load(cls, path: str, model_class=None, device: str = "cpu"):
        """Load a model from the given path
        Args:
            path: Path to the model to load
            model_class: optional model class to provide if the model should be loaded with a specific class
            device: the device to load the model on ("cpu" or "cuda")
        """
        if not dm.fs.is_local_path(path):
            local_path = tempfile.mkdtemp()
            dm.fs.copy_dir(path, local_path, force=True, progress=True, leave_progress=False)
        else:
            local_path = path

        if model_class is None:
            model_config = AutoConfig.from_pretrained(local_path)
            architectures = getattr(model_config, "architectures", [])
            if len(architectures) > 0:
                model_class = MODEL_MAPPING._load_attr_from_module(
                    model_config.model_type, architectures[0]
                )
            else:
                model_class = AutoModel
        model = model_class.from_pretrained(local_path).to(device)
        tokenizer = AutoTokenizer.from_pretrained(local_path)
        return cls(model, tokenizer)

load(path, model_class=None, device='cpu') classmethod

Load a model from the given path Args: path: Path to the model to load model_class: optional model class to provide if the model should be loaded with a specific class device: the device to load the model on ("cpu" or "cuda")

Source code in molfeat/trans/pretrained/hf_transformers.py
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@classmethod
def load(cls, path: str, model_class=None, device: str = "cpu"):
    """Load a model from the given path
    Args:
        path: Path to the model to load
        model_class: optional model class to provide if the model should be loaded with a specific class
        device: the device to load the model on ("cpu" or "cuda")
    """
    if not dm.fs.is_local_path(path):
        local_path = tempfile.mkdtemp()
        dm.fs.copy_dir(path, local_path, force=True, progress=True, leave_progress=False)
    else:
        local_path = path

    if model_class is None:
        model_config = AutoConfig.from_pretrained(local_path)
        architectures = getattr(model_config, "architectures", [])
        if len(architectures) > 0:
            model_class = MODEL_MAPPING._load_attr_from_module(
                model_config.model_type, architectures[0]
            )
        else:
            model_class = AutoModel
    model = model_class.from_pretrained(local_path).to(device)
    tokenizer = AutoTokenizer.from_pretrained(local_path)
    return cls(model, tokenizer)

save(model, path, clean_up=False) classmethod

Save a hugging face model to a specific path

Parameters:

Name Type Description Default
model HFExperiment

model to save

required
path str

path to the folder root where to save the model

required
clean_up bool

whether to clean up the local path after saving

False
Source code in molfeat/trans/pretrained/hf_transformers.py
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@classmethod
def save(cls, model: HFExperiment, path: str, clean_up: bool = False):
    """Save a hugging face model to a specific path

    Args:
        model: model to save
        path: path to the folder root where to save the model
        clean_up: whether to clean up the local path after saving
    """
    local_path = tempfile.mkdtemp()
    # we can save both the tokenizer and the model to the same path
    model.model.save_pretrained(local_path)
    model.tokenizer.save_pretrained(local_path)

    # With transformers>=4.35.0, models are by default saved as safetensors.
    # For backwards compatibility, we also save the model as the older pickle-based format.
    model.model.save_pretrained(local_path, safe_serialization=False)

    dm.fs.copy_dir(local_path, path, force=True, progress=True, leave_progress=False)
    logger.info(f"Model saved to {path}")
    # clean up now
    if clean_up:
        mapper = dm.fs.get_mapper(local_path)
        mapper.fs.delete(local_path, recursive=True)
    return path

HFModel

Bases: PretrainedStoreModel

Transformer model loading model loading

Source code in molfeat/trans/pretrained/hf_transformers.py
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class HFModel(PretrainedStoreModel):
    """Transformer model loading model loading"""

    def __init__(
        self,
        name: str,
        cache_path: Optional[os.PathLike] = None,
        store: Optional[ModelStore] = None,
    ):
        """Model loader initializer

        Args:
            name (str, optional): Name of the model for ada.
            cache_path (os.PathLike, optional): Local cache path for faster loading. This is the cache_path parameter for ADA loading !
        """

        super().__init__(name, cache_path=cache_path, store=store)
        self._model = None

    @classmethod
    def _ensure_local(cls, object_path: Union[str, os.PathLike]):
        """Make sure the input path is a local path otherwise download it

        Args:
            object_path: Path to the object

        """
        if dm.fs.is_local_path(object_path):
            return object_path
        local_path = tempfile.mkdtemp()
        if dm.fs.is_file(object_path):
            local_path = os.path.join(local_path, os.path.basename(object_path))
            dm.fs.copy_file(object_path, local_path)
        else:
            dm.fs.copy_dir(object_path, local_path)
        return local_path

    @classmethod
    def from_pretrained(
        cls,
        model: Union[str, PreTrainedModel],
        tokenizer: Union[str, PreTrainedTokenizer, PreTrainedTokenizerFast],
        model_class=None,
        model_name: Optional[str] = None,
    ):
        """Load model using huggingface pretrained model loader hook

        Args:
            model: Model to load. Can also be the name on the hub or the path to the model
            tokenizer: Tokenizer to load. Can also be the name on the hub or the path to the tokenizer
            model_class: optional model class to provide if the model should be loaded with a specific class
            model_name: optional model name to give to this model.
        """

        # load the model
        if isinstance(model, PreTrainedModel):
            model_obj = model
        else:
            if dm.fs.exists(model):
                model = cls._ensure_local(model)
            if model_class is None:
                model_config = AutoConfig.from_pretrained(model)
                architectures = getattr(model_config, "architectures", [])
                if len(architectures) > 0:
                    model_class = MODEL_MAPPING._load_attr_from_module(
                        model_config.model_type, architectures[0]
                    )
                else:
                    model_class = AutoModel
            model_obj = model_class.from_pretrained(model)

        if isinstance(tokenizer, (PreTrainedTokenizer, PreTrainedTokenizerFast)):
            tokenizer_obj = tokenizer
        else:
            if dm.fs.exists(tokenizer):
                tokenizer = cls._ensure_local(tokenizer)
            tokenizer_obj = AutoTokenizer.from_pretrained(tokenizer)
        name = model_name or f"hf_model_{uuid.uuid4().hex[:8]}"
        model = HFModel(name=name, store=ModelStore())
        model._model = HFExperiment(model=model_obj, tokenizer=tokenizer_obj)
        return model

    @classmethod
    def register_pretrained(
        cls,
        model: Union[str, PreTrainedModel],
        tokenizer: Union[str, PreTrainedTokenizer, PreTrainedTokenizerFast],
        model_card: ModelInfo,
        model_class=None,
    ):
        """Register a pretrained huggingface model to the model store
        Args:
            model: Model to load. Can also be the name on the hub or the path to the model
            tokenizer: Tokenizer to load. Can also be the name on the hub or the path to the tokenizer
            model_class: optional model class to provide if the model should be loaded with a specific class
            model_card: optional model card to provide for registering this model
        """
        model = cls.from_pretrained(model, tokenizer, model_class, model_name=model_card.name)
        model.store.register(model_card, model._model, save_fn=HFExperiment.save)
        return model

    def get_notation(self, default_notation: Optional[str] = None):
        """Get the notation of the model"""
        notation = default_notation
        try:
            modelcard = self.store.search(name=self.name)[0]
            notation = modelcard.inputs
        except Exception:
            pass
        return notation

    def load(self):
        """Load Transformer Pretrained featurizer model"""
        if self._model is not None:
            return self._model
        download_output_dir = self._artifact_load(
            name=self.name, download_path=self.cache_path, store=self.store
        )
        model_path = dm.fs.join(download_output_dir, self.store.MODEL_PATH_NAME)
        self._model = HFExperiment.load(model_path)
        return self._model

__init__(name, cache_path=None, store=None)

Model loader initializer

Parameters:

Name Type Description Default
name str

Name of the model for ada.

required
cache_path PathLike

Local cache path for faster loading. This is the cache_path parameter for ADA loading !

None
Source code in molfeat/trans/pretrained/hf_transformers.py
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def __init__(
    self,
    name: str,
    cache_path: Optional[os.PathLike] = None,
    store: Optional[ModelStore] = None,
):
    """Model loader initializer

    Args:
        name (str, optional): Name of the model for ada.
        cache_path (os.PathLike, optional): Local cache path for faster loading. This is the cache_path parameter for ADA loading !
    """

    super().__init__(name, cache_path=cache_path, store=store)
    self._model = None

from_pretrained(model, tokenizer, model_class=None, model_name=None) classmethod

Load model using huggingface pretrained model loader hook

Parameters:

Name Type Description Default
model Union[str, PreTrainedModel]

Model to load. Can also be the name on the hub or the path to the model

required
tokenizer Union[str, PreTrainedTokenizer, PreTrainedTokenizerFast]

Tokenizer to load. Can also be the name on the hub or the path to the tokenizer

required
model_class

optional model class to provide if the model should be loaded with a specific class

None
model_name Optional[str]

optional model name to give to this model.

None
Source code in molfeat/trans/pretrained/hf_transformers.py
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@classmethod
def from_pretrained(
    cls,
    model: Union[str, PreTrainedModel],
    tokenizer: Union[str, PreTrainedTokenizer, PreTrainedTokenizerFast],
    model_class=None,
    model_name: Optional[str] = None,
):
    """Load model using huggingface pretrained model loader hook

    Args:
        model: Model to load. Can also be the name on the hub or the path to the model
        tokenizer: Tokenizer to load. Can also be the name on the hub or the path to the tokenizer
        model_class: optional model class to provide if the model should be loaded with a specific class
        model_name: optional model name to give to this model.
    """

    # load the model
    if isinstance(model, PreTrainedModel):
        model_obj = model
    else:
        if dm.fs.exists(model):
            model = cls._ensure_local(model)
        if model_class is None:
            model_config = AutoConfig.from_pretrained(model)
            architectures = getattr(model_config, "architectures", [])
            if len(architectures) > 0:
                model_class = MODEL_MAPPING._load_attr_from_module(
                    model_config.model_type, architectures[0]
                )
            else:
                model_class = AutoModel
        model_obj = model_class.from_pretrained(model)

    if isinstance(tokenizer, (PreTrainedTokenizer, PreTrainedTokenizerFast)):
        tokenizer_obj = tokenizer
    else:
        if dm.fs.exists(tokenizer):
            tokenizer = cls._ensure_local(tokenizer)
        tokenizer_obj = AutoTokenizer.from_pretrained(tokenizer)
    name = model_name or f"hf_model_{uuid.uuid4().hex[:8]}"
    model = HFModel(name=name, store=ModelStore())
    model._model = HFExperiment(model=model_obj, tokenizer=tokenizer_obj)
    return model

get_notation(default_notation=None)

Get the notation of the model

Source code in molfeat/trans/pretrained/hf_transformers.py
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def get_notation(self, default_notation: Optional[str] = None):
    """Get the notation of the model"""
    notation = default_notation
    try:
        modelcard = self.store.search(name=self.name)[0]
        notation = modelcard.inputs
    except Exception:
        pass
    return notation

load()

Load Transformer Pretrained featurizer model

Source code in molfeat/trans/pretrained/hf_transformers.py
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def load(self):
    """Load Transformer Pretrained featurizer model"""
    if self._model is not None:
        return self._model
    download_output_dir = self._artifact_load(
        name=self.name, download_path=self.cache_path, store=self.store
    )
    model_path = dm.fs.join(download_output_dir, self.store.MODEL_PATH_NAME)
    self._model = HFExperiment.load(model_path)
    return self._model

register_pretrained(model, tokenizer, model_card, model_class=None) classmethod

Register a pretrained huggingface model to the model store Args: model: Model to load. Can also be the name on the hub or the path to the model tokenizer: Tokenizer to load. Can also be the name on the hub or the path to the tokenizer model_class: optional model class to provide if the model should be loaded with a specific class model_card: optional model card to provide for registering this model

Source code in molfeat/trans/pretrained/hf_transformers.py
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@classmethod
def register_pretrained(
    cls,
    model: Union[str, PreTrainedModel],
    tokenizer: Union[str, PreTrainedTokenizer, PreTrainedTokenizerFast],
    model_card: ModelInfo,
    model_class=None,
):
    """Register a pretrained huggingface model to the model store
    Args:
        model: Model to load. Can also be the name on the hub or the path to the model
        tokenizer: Tokenizer to load. Can also be the name on the hub or the path to the tokenizer
        model_class: optional model class to provide if the model should be loaded with a specific class
        model_card: optional model card to provide for registering this model
    """
    model = cls.from_pretrained(model, tokenizer, model_class, model_name=model_card.name)
    model.store.register(model_card, model._model, save_fn=HFExperiment.save)
    return model

PretrainedHFTransformer

Bases: PretrainedMolTransformer

HuggingFace Transformer for feature extraction.

Note

For convenience and consistency, this featurizer only accepts as inputs smiles and molecules, then perform the internal conversion, based on the notation provided.

Source code in molfeat/trans/pretrained/hf_transformers.py
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class PretrainedHFTransformer(PretrainedMolTransformer):
    """
    HuggingFace Transformer for feature extraction.

    !!! note
        For convenience and consistency, this featurizer only accepts as inputs
        smiles and molecules, then perform the internal conversion, based on the notation provided.
    """

    NEEDS_RANDOM_SEED = ["bert", "roberta"]

    def __init__(
        self,
        kind: Union[str, HFModel] = "ChemBERTa-77M-MLM",
        notation: Optional[str] = "none",
        pooling: str = "mean",
        concat_layers: Union[List[int], int] = -1,
        prefer_encoder: bool = True,
        dtype=np.float32,
        device="cpu",
        max_length: int = 128,
        ignore_padding: bool = True,
        preload: bool = False,
        n_jobs: int = 0,
        random_seed: Optional[int] = None,
        **params,
    ):
        """
        HuggingFace Transformer for featurizer extraction
        The default behaviour of this feature extractor is to return the last hidden state of the encoder
        similar to what is performed by the pipeline 'feature-extraction' in hugging face.

        !!! warning
            For bert models, the default pooling layers is a neural network. Therefore, do not use the default
            Or provide a random seed for reproducibility (in this case pooling will act as random projection to the same manifold)

        !!! note
            The pooling module of this featurizer is accessible through the `_pooling_obj` attribute.

        Args:
            kind: name of the featurizer as available in the model store
            notation: optional line notation to use. Only use if it cannot be found from the model card.
            pooling: type of pooling to use. One of ['default', 'mean', 'max', 'sum', 'clf', ]. The value "default" corresponds to the default litterature pooling for each model type.
                See `molfeat.utils.pooler.get_default_hf_pooler` for more details.
            concat_layers: Layer to concat to get the representation. By default the last hidden layer is returned.
            prefer_encoder: For an encoder-decoder model, prefer the embeddings provided by the encoder.
            dtype: Data type to output
            device: Torch device on which to run the featurizer.
            max_length: Maximum length of the input sequence to consider. Please update this for large sequences
            ignore_padding: Whether to ignore padding in the representation (default: True) to avoid effect of batching
            preload: Whether to preload the model into memory or not
            n_jobs: number of jobs to use
            random_seed: random seed to use for reproducibility whenever a DNN pooler is used (e.g bert/roberta)
        """

        if not requires.check("transformers"):
            raise ValueError(
                "Cannot find transformers and/or tokenizers. It's required for this featurizer !"
            )

        super().__init__(
            dtype=dtype,
            device=device,
            n_jobs=n_jobs,
            **params,
        )
        if concat_layers is None:
            concat_layers = -1
        if not isinstance(concat_layers, list):
            concat_layers = [concat_layers]
        self.concat_layers = concat_layers
        self.max_length = max_length
        self.ignore_padding = ignore_padding
        self._require_mols = False
        self.random_seed = random_seed
        self.preload = preload
        self.pooling = pooling
        self.prefer_encoder = prefer_encoder
        self.device = torch.device(device)
        self._pooling_obj = None
        if isinstance(kind, HFModel):
            self.kind = kind.name
            self.featurizer = kind
        else:
            self.kind = kind
            self.featurizer = HFModel(name=self.kind)
        self.notation = self.featurizer.get_notation(notation) or "none"
        self.converter = SmilesConverter(self.notation)
        if self.preload:
            self._preload()

    def _update_params(self):
        """Update the parameters of this model"""
        # pylint: disable=no-member
        super()._update_params()

        hf_model = HFModel(
            name=self.kind,
        )
        self.featurizer = hf_model.load()
        config = self.featurizer.model.config.to_dict()
        self._pooling_obj = self._pooling_obj = (
            get_default_hgf_pooler(self.pooling, config, random_seed=self.random_seed)
            if self._pooling_obj is None
            else self._pooling_obj
        )

    def _preload(self):
        """Perform preloading of the model from the store"""
        super()._preload()
        self.featurizer.model.to(self.device)
        self.featurizer.max_length = self.max_length

        # we can be confident that the model has been loaded here
        if self._pooling_obj is not None and self.preload:
            return
        config = self.featurizer.model.config.to_dict()
        cur_tokenizer = self.featurizer.tokenizer
        for special_token_id_name in [
            "pad_token_id",
            "bos_token_id",
            "eos_token_id",
            "unk_token_id",
            "sep_token_id",
            "mask_token_id",
        ]:
            token_id = getattr(cur_tokenizer, special_token_id_name)
            if token_id is not None:
                config[special_token_id_name] = token_id

        self._pooling_obj = (
            get_default_hgf_pooler(self.pooling, config, random_seed=self.random_seed)
            if self._pooling_obj is None
            else self._pooling_obj
        )
        # pooling layer is still none, that means we could not fetch it properly
        if self._pooling_obj is None:
            logger.warning(
                "Cannot confidently find the pooling layer and therefore will not apply pooling"
            )

    def _convert(self, inputs: list, **kwargs):
        """Convert the list of molecules to the right format for embedding

        Args:
            inputs: inputs to preprocess

        Returns:
            processed: pre-processed input list
        """
        self._preload()

        if isinstance(inputs, (str, dm.Mol)):
            inputs = [inputs]

        def _to_smiles(x):
            return dm.to_smiles(x) if not isinstance(x, str) else x

        parallel_kwargs = getattr(self, "parallel_kwargs", {})

        if len(inputs) > 1:
            smiles = dm.utils.parallelized(
                _to_smiles,
                inputs,
                n_jobs=self.n_jobs,
                **parallel_kwargs,
            )
            inputs = dm.utils.parallelized(
                self.converter.encode,
                smiles,
                n_jobs=self.n_jobs,
                **parallel_kwargs,
            )
        else:
            inputs = self.converter.encode(_to_smiles(inputs[0]))
        # this check is necessary for some tokenizers
        if isinstance(inputs, str):
            inputs = [inputs]
        encoded = self.featurizer.tokenizer(
            list(inputs),
            truncation=True,
            padding=True,
            max_length=self.max_length,
            return_tensors="pt",
        )
        return encoded

    def _embed(self, inputs, **kwargs):
        """
        Perform embedding of inputs using the pretrained model

        Args:
            inputs: smiles or seqs
            kwargs: any additional parameters
        """
        self._preload()

        # Move inputs to the correct device
        inputs = {key: value.to(self.device) for key, value in inputs.items()}

        attention_mask = inputs.get("attention_mask", None)
        if attention_mask is not None and self.ignore_padding:
            attention_mask = attention_mask.unsqueeze(-1).to(self.device)  # B, S, 1
        else:
            attention_mask = None
        with torch.no_grad():
            if (
                isinstance(self.featurizer.model, EncoderDecoderModel)
                or hasattr(self.featurizer.model, "encoder")
            ) and self.prefer_encoder:
                out_dict = self.featurizer.model.encoder(output_hidden_states=True, **inputs)
            else:
                out_dict = self.featurizer.model(output_hidden_states=True, **inputs)
            hidden_state = out_dict["hidden_states"]
            emb_layers = []
            for layer in self.concat_layers:
                emb = hidden_state[layer].detach()  # B, S, D
                emb = self._pooling_obj(
                    emb,
                    inputs["input_ids"],
                    mask=attention_mask,
                    ignore_padding=self.ignore_padding,
                )
                emb_layers.append(emb)
            emb = torch.cat(emb_layers, dim=1)
        return emb.cpu().numpy()  # Move the final tensor to CPU before converting to numpy array

    def set_max_length(self, max_length: int):
        """Set the maximum length for this featurizer"""
        self.max_length = max_length
        self._preload()

__init__(kind='ChemBERTa-77M-MLM', notation='none', pooling='mean', concat_layers=-1, prefer_encoder=True, dtype=np.float32, device='cpu', max_length=128, ignore_padding=True, preload=False, n_jobs=0, random_seed=None, **params)

HuggingFace Transformer for featurizer extraction The default behaviour of this feature extractor is to return the last hidden state of the encoder similar to what is performed by the pipeline 'feature-extraction' in hugging face.

Warning

For bert models, the default pooling layers is a neural network. Therefore, do not use the default Or provide a random seed for reproducibility (in this case pooling will act as random projection to the same manifold)

Note

The pooling module of this featurizer is accessible through the _pooling_obj attribute.

Parameters:

Name Type Description Default
kind Union[str, HFModel]

name of the featurizer as available in the model store

'ChemBERTa-77M-MLM'
notation Optional[str]

optional line notation to use. Only use if it cannot be found from the model card.

'none'
pooling str

type of pooling to use. One of ['default', 'mean', 'max', 'sum', 'clf', ]. The value "default" corresponds to the default litterature pooling for each model type. See molfeat.utils.pooler.get_default_hf_pooler for more details.

'mean'
concat_layers Union[List[int], int]

Layer to concat to get the representation. By default the last hidden layer is returned.

-1
prefer_encoder bool

For an encoder-decoder model, prefer the embeddings provided by the encoder.

True
dtype

Data type to output

float32
device

Torch device on which to run the featurizer.

'cpu'
max_length int

Maximum length of the input sequence to consider. Please update this for large sequences

128
ignore_padding bool

Whether to ignore padding in the representation (default: True) to avoid effect of batching

True
preload bool

Whether to preload the model into memory or not

False
n_jobs int

number of jobs to use

0
random_seed Optional[int]

random seed to use for reproducibility whenever a DNN pooler is used (e.g bert/roberta)

None
Source code in molfeat/trans/pretrained/hf_transformers.py
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def __init__(
    self,
    kind: Union[str, HFModel] = "ChemBERTa-77M-MLM",
    notation: Optional[str] = "none",
    pooling: str = "mean",
    concat_layers: Union[List[int], int] = -1,
    prefer_encoder: bool = True,
    dtype=np.float32,
    device="cpu",
    max_length: int = 128,
    ignore_padding: bool = True,
    preload: bool = False,
    n_jobs: int = 0,
    random_seed: Optional[int] = None,
    **params,
):
    """
    HuggingFace Transformer for featurizer extraction
    The default behaviour of this feature extractor is to return the last hidden state of the encoder
    similar to what is performed by the pipeline 'feature-extraction' in hugging face.

    !!! warning
        For bert models, the default pooling layers is a neural network. Therefore, do not use the default
        Or provide a random seed for reproducibility (in this case pooling will act as random projection to the same manifold)

    !!! note
        The pooling module of this featurizer is accessible through the `_pooling_obj` attribute.

    Args:
        kind: name of the featurizer as available in the model store
        notation: optional line notation to use. Only use if it cannot be found from the model card.
        pooling: type of pooling to use. One of ['default', 'mean', 'max', 'sum', 'clf', ]. The value "default" corresponds to the default litterature pooling for each model type.
            See `molfeat.utils.pooler.get_default_hf_pooler` for more details.
        concat_layers: Layer to concat to get the representation. By default the last hidden layer is returned.
        prefer_encoder: For an encoder-decoder model, prefer the embeddings provided by the encoder.
        dtype: Data type to output
        device: Torch device on which to run the featurizer.
        max_length: Maximum length of the input sequence to consider. Please update this for large sequences
        ignore_padding: Whether to ignore padding in the representation (default: True) to avoid effect of batching
        preload: Whether to preload the model into memory or not
        n_jobs: number of jobs to use
        random_seed: random seed to use for reproducibility whenever a DNN pooler is used (e.g bert/roberta)
    """

    if not requires.check("transformers"):
        raise ValueError(
            "Cannot find transformers and/or tokenizers. It's required for this featurizer !"
        )

    super().__init__(
        dtype=dtype,
        device=device,
        n_jobs=n_jobs,
        **params,
    )
    if concat_layers is None:
        concat_layers = -1
    if not isinstance(concat_layers, list):
        concat_layers = [concat_layers]
    self.concat_layers = concat_layers
    self.max_length = max_length
    self.ignore_padding = ignore_padding
    self._require_mols = False
    self.random_seed = random_seed
    self.preload = preload
    self.pooling = pooling
    self.prefer_encoder = prefer_encoder
    self.device = torch.device(device)
    self._pooling_obj = None
    if isinstance(kind, HFModel):
        self.kind = kind.name
        self.featurizer = kind
    else:
        self.kind = kind
        self.featurizer = HFModel(name=self.kind)
    self.notation = self.featurizer.get_notation(notation) or "none"
    self.converter = SmilesConverter(self.notation)
    if self.preload:
        self._preload()

set_max_length(max_length)

Set the maximum length for this featurizer

Source code in molfeat/trans/pretrained/hf_transformers.py
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def set_max_length(self, max_length: int):
    """Set the maximum length for this featurizer"""
    self.max_length = max_length
    self._preload()