DNABERT-S¶

Pre-trained model on multi-species genome using a contrastive learning objective for species-aware DNA embeddings.

Disclaimer¶

This is an UNOFFICIAL implementation of the DNABERT-S: pioneering species differentiation with species-aware DNA embeddings by Zhihan Zhou, et al.

The OFFICIAL repository of DNABERT-S is at MAGICS-LAB/DNABERT_S.

Tip

The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.

The team releasing DNABERT-S did not write this model card for this model so this model card has been written by the MultiMolecule team.

Model Details¶

DNABERT-S is a bert-style model built upon DNABERT-2 and fine-tuned with contrastive learning for species-aware DNA embeddings. The model was trained using the proposed Curriculum Contrastive Learning (C²LR) strategy with the Manifold Instance Mixup (MI-Mix) training objective.

DNABERT-S shares the same architecture as DNABERT-2: it uses Byte Pair Encoding (BPE) tokenization, Attention with Linear Biases (ALiBi) instead of learned position embeddings, and incorporates a Gated Linear Unit (GeGLU) MLP and FlashAttention for improved efficiency.

Model Specification¶

Num Layers	Hidden Size	Num Heads	Intermediate Size	Num Parameters (M)	FLOPs (G)	MACs (G)	Max Num Tokens
12	768	12	3072	117.07	125.83	62.92	512

Links¶

Code: multimolecule.dnaberts
Data: GenBank
Paper: DNABERT-S: pioneering species differentiation with species-aware DNA embeddings
Developed by: Zhihan Zhou, Weimin Wu, Harrison Ho, Jiayi Wang, Lizhen Shi, Ramana V Davuluri, Zhong Wang, Han Liu
Model type: BERT - MosaicBERT
Original Repository: MAGICS-LAB/DNABERT_S

Usage¶

The model file depends on the multimolecule library. You can install it using pip:

Bash
1	`pip install multimolecule`

Direct Use¶

Feature Extraction¶

You can use this model directly with a pipeline for feature extraction:

Python
import multimolecule  # you must import multimolecule to register models
from transformers import pipeline

predictor = pipeline("feature-extraction", model="multimolecule/dnaberts")
output = predictor("ATCGATCGATCG")

Downstream Use¶

Extract Features¶

Here is how to use this model to get the features of a given sequence in PyTorch:

Python
from multimolecule import DnaBertSModel
from transformers import AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("multimolecule/dnaberts")
model = DnaBertSModel.from_pretrained("multimolecule/dnaberts")

text = "ATCGATCGATCGATCG"
input = tokenizer(text, return_tensors="pt")

output = model(**input)

Sequence Classification / Regression¶

Note

This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression.

Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch:

Python
import torch
from multimolecule import DnaBertSForSequencePrediction
from transformers import AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("multimolecule/dnaberts")
model = DnaBertSForSequencePrediction.from_pretrained("multimolecule/dnaberts")

text = "ATCGATCGATCGATCG"
input = tokenizer(text, return_tensors="pt")
label = torch.tensor([1])

output = model(**input, labels=label)

Token Classification / Regression¶

Note

This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression.

Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch:

Python
import torch
from multimolecule import DnaBertSForTokenPrediction
from transformers import AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("multimolecule/dnaberts")
model = DnaBertSForTokenPrediction.from_pretrained("multimolecule/dnaberts")

text = "ATCGATCGATCGATCG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), ))

output = model(**input, labels=label)

Contact Classification / Regression¶

Note

This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression.

Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch:

Python
import torch
from multimolecule import DnaBertSForContactPrediction
from transformers import AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("multimolecule/dnaberts")
model = DnaBertSForContactPrediction.from_pretrained("multimolecule/dnaberts")

text = "ATCGATCGATCGATCG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), len(text)))

output = model(**input, labels=label)

Training Details¶

DNABERT-S uses a two-phase Curriculum Contrastive Learning (C²LR) strategy. In phase I, the model is trained with Weighted SimCLR for one epoch. In phase II, the model is further trained with Manifold Instance Mixup (MI-Mix) for two epochs. The training starts from the pre-trained DNABERT-2 checkpoint.

Training Data¶

The DNABERT-S model was trained on pairs of non-overlapping DNA sequences from the same species, sourced from GenBank. The dataset consists of 47,923 pairs from 17,636 viral genomes, 1 million pairs from 5,011 fungi genomes, and 1 million pairs from 6,402 bacteria genomes. From the total of 2,047,923 pairs, 2 million were randomly selected for training and the rest were used as validation data. All DNA sequences are 10,000 bp in length.

Training Procedure¶

Pre-training¶

The model was trained on 8 NVIDIA A100 80GB GPUs.

Temperature (τ): 0.05
Hyperparameter (α): 1.0
Epochs: 1 (phase I, Weighted SimCLR) + 2 (phase II, MI-Mix)
Optimizer: Adam
Learning rate: 3e-6
Batch size: 48
Checkpointing: Every 10,000 steps, best selected on validation loss
Training time: ~48 hours

Citation¶

BibTeX
@article{zhou2025dnaberts,
  title={{DNABERT-S}: pioneering species differentiation with species-aware {DNA} embeddings},
  author={Zhou, Zhihan and Wu, Weimin and Ho, Harrison and Wang, Jiayi and Shi, Lizhen and Davuluri, Ramana V and Wang, Zhong and Liu, Han},
  journal={Bioinformatics},
  volume={41},
  pages={i255--i264},
  year={2025},
  doi={10.1093/bioinformatics/btaf188}
}

Note

The artifacts distributed in this repository are part of the MultiMolecule project. If you use MultiMolecule in your research, you must cite the MultiMolecule project as follows:

BibTeX
@software{chen_2024_12638419,
  author    = {Chen, Zhiyuan and Zhu, Sophia Y.},
  title     = {MultiMolecule},
  doi       = {10.5281/zenodo.12638419},
  publisher = {Zenodo},
  url       = {https://doi.org/10.5281/zenodo.12638419},
  year      = 2024,
  month     = may,
  day       = 4
}

Contact¶

Please use GitHub issues of MultiMolecule for any questions or comments on the model card.

Please contact the authors of the DNABERT-S paper for questions or comments on the paper/model.

License¶

This model implementation is licensed under the GNU Affero General Public License.

For additional terms and clarifications, please refer to our License FAQ.

Text Only
1	`SPDX-License-Identifier: AGPL-3.0-or-later`

multimolecule.models.dnaberts ¶

DnaBertSConfig ¶

Bases: PreTrainedConfig

This is the configuration class to store the configuration of a DnaBertSModel. It is used to instantiate a DNABERT-S model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the DNABERT-S zhihan1996/DNABERT-S architecture.

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

Parameters:

Name	Type	Description	Default
`vocab_size` ¶	`int`	Vocabulary size of the DnaBertS model. Defines the number of different tokens that can be represented by the `input_ids` passed when calling [`DnaBertSModel`].	`4096`
`hidden_size` ¶	`int`	Dimensionality of the encoder layers and the pooler layer.	`768`
`num_hidden_layers` ¶	`int`	Number of hidden layers in the Transformer encoder.	`12`
`num_attention_heads` ¶	`int`	Number of attention heads for each attention layer in the Transformer encoder.	`12`
`intermediate_size` ¶	`int`	Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.	`3072`
`hidden_act` ¶	`str`	The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.	`'gelu'`
`hidden_dropout` ¶	`float`	The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.	`0.1`
`attention_dropout` ¶	`float`	The dropout ratio for the attention probabilities.	`0.0`
`max_position_embeddings` ¶	`int`	The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).	`512`
`initializer_range` ¶	`float`	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.	`0.02`
`layer_norm_eps` ¶	`float`	The epsilon used by the layer normalization layers.	`1e-12`
`position_embedding_type` ¶	`str`	Type of position embedding. DNABERT-S uses `"alibi"` (Attention with Linear Biases).	`'alibi'`
`alibi_starting_size` ¶	`int`	The starting size for the ALiBi position bias tensor.	`512`
`is_decoder` ¶	`bool`	Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.	`False`
`use_cache` ¶	`bool`	Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`.	`True`
`head` ¶	`HeadConfig \| None`	The configuration of the head.	`None`
`lm_head` ¶	`MaskedLMHeadConfig \| None`	The configuration of the masked language model head.	`None`
`add_cross_attention` ¶	`bool`	Whether to add cross-attention layers when the model is used as a decoder.	`False`

Examples:

Python Console Session
>>> from multimolecule import DnaBertSConfig, DnaBertSModel
>>> # Initializing a DNABERT-S multimolecule/dnaberts style configuration
>>> configuration = DnaBertSConfig()
>>> # Initializing a model (with random weights) from the multimolecule/dnaberts style configuration
>>> model = DnaBertSModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config

Source code in multimolecule/models/dnaberts/configuration_dnaberts.py

Python
class DnaBertSConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a
    [`DnaBertSModel`][multimolecule.models.DnaBertSModel]. It is used to instantiate a DNABERT-S model according
    to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will
    yield a similar configuration to that of the DNABERT-S
    [zhihan1996/DNABERT-S](https://huggingface.co/zhihan1996/DNABERT-S) architecture.

    Configuration objects inherit from [`PreTrainedConfig`][multimolecule.models.PreTrainedConfig] and can be used to
    control the model outputs. Read the documentation from [`PreTrainedConfig`][multimolecule.models.PreTrainedConfig]
    for more information.

    Args:
        vocab_size:
            Vocabulary size of the DnaBertS model. Defines the number of different tokens that can be represented by
            the `input_ids` passed when calling [`DnaBertSModel`].
        hidden_size:
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers:
            Number of hidden layers in the Transformer encoder.
        num_attention_heads:
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size:
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act:
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        hidden_dropout:
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout:
            The dropout ratio for the attention probabilities.
        max_position_embeddings:
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        initializer_range:
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps:
            The epsilon used by the layer normalization layers.
        position_embedding_type:
            Type of position embedding. DNABERT-S uses `"alibi"` (Attention with Linear Biases).
        alibi_starting_size:
            The starting size for the ALiBi position bias tensor.
        is_decoder:
            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
        use_cache:
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        head:
            The configuration of the head.
        lm_head:
            The configuration of the masked language model head.
        add_cross_attention:
            Whether to add cross-attention layers when the model is used as a decoder.

    Examples:
        >>> from multimolecule import DnaBertSConfig, DnaBertSModel
        >>> # Initializing a DNABERT-S multimolecule/dnaberts style configuration
        >>> configuration = DnaBertSConfig()
        >>> # Initializing a model (with random weights) from the multimolecule/dnaberts style configuration
        >>> model = DnaBertSModel(configuration)
        >>> # Accessing the model configuration
        >>> configuration = model.config
    """

    model_type = "dnaberts"

    def __init__(
        self,
        vocab_size: int = 4096,
        hidden_size: int = 768,
        num_hidden_layers: int = 12,
        num_attention_heads: int = 12,
        intermediate_size: int = 3072,
        hidden_act: str = "gelu",
        hidden_dropout: float = 0.1,
        attention_dropout: float = 0.0,
        max_position_embeddings: int = 512,
        initializer_range: float = 0.02,
        layer_norm_eps: float = 1e-12,
        position_embedding_type: str = "alibi",
        alibi_starting_size: int = 512,
        is_decoder: bool = False,
        use_cache: bool = True,
        head: HeadConfig | None = None,
        lm_head: MaskedLMHeadConfig | None = None,
        add_cross_attention: bool = False,
        **kwargs,
    ):
        super().__init__(**kwargs)
        validate_attention_dimensions(hidden_size, num_attention_heads)
        self.vocab_size = vocab_size
        self.type_vocab_size = 2
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout = hidden_dropout
        self.attention_dropout = attention_dropout
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.alibi_starting_size = alibi_starting_size
        self.is_decoder = is_decoder
        self.use_cache = use_cache
        self.head = HeadConfig(**head) if head is not None else None
        self.lm_head = MaskedLMHeadConfig(**lm_head) if lm_head is not None else None
        self.add_cross_attention = add_cross_attention

DnaBertSForContactPrediction ¶

Bases: DnaBertSPreTrainedModel

Examples:

Python Console Session
>>> import torch
>>> from multimolecule import DnaBertSConfig, DnaBertSForContactPrediction
>>> config = DnaBertSConfig()
>>> model = DnaBertSForContactPrediction(config)
>>> input_ids = torch.randint(0, config.vocab_size, (1, 16))
>>> output = model(input_ids, labels=torch.randint(2, (1, 14, 14)))
>>> output["logits"].shape
torch.Size([1, 14, 14, 1])
>>> output["loss"]
tensor(..., grad_fn=<BinaryCrossEntropyWithLogitsBackward0>)

Source code in multimolecule/models/dnaberts/modeling_dnaberts.py

Python
class DnaBertSForContactPrediction(DnaBertSPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule import DnaBertSConfig, DnaBertSForContactPrediction
        >>> config = DnaBertSConfig()
        >>> model = DnaBertSForContactPrediction(config)
        >>> input_ids = torch.randint(0, config.vocab_size, (1, 16))
        >>> output = model(input_ids, labels=torch.randint(2, (1, 14, 14)))
        >>> output["logits"].shape
        torch.Size([1, 14, 14, 1])
        >>> output["loss"]  # doctest:+ELLIPSIS
        tensor(..., grad_fn=<BinaryCrossEntropyWithLogitsBackward0>)
    """

    def __init__(self, config: DnaBertSConfig):
        super().__init__(config)
        self.model = DnaBertSModel(config, add_pooling_layer=False)
        self.contact_head = ContactPredictionHead(config)
        self.head_config = self.contact_head.config
        self.require_attentions = self.contact_head.require_attentions

        # Initialize weights and apply final processing
        self.post_init()

    @can_return_tuple
    def forward(
        self,
        input_ids: Tensor | NestedTensor | None = None,
        attention_mask: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels: Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[Tensor, ...] | ContactPredictorOutput:
        if self.require_attentions:
            output_attentions = kwargs.get("output_attentions", self.config.output_attentions)
            if output_attentions is False:
                warn("output_attentions must be True since prediction head requires attentions.")
            kwargs["output_attentions"] = True
        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            return_dict=True,
            **kwargs,
        )

        output = self.contact_head(outputs, attention_mask, input_ids, labels)
        logits, loss = output.logits, output.loss

        return ContactPredictorOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

DnaBertSForSequencePrediction ¶

Bases: DnaBertSPreTrainedModel

Examples:

Python Console Session
>>> import torch
>>> from multimolecule import DnaBertSConfig, DnaBertSForSequencePrediction
>>> config = DnaBertSConfig()
>>> model = DnaBertSForSequencePrediction(config)
>>> input_ids = torch.randint(0, config.vocab_size, (1, 16))
>>> output = model(input_ids, labels=torch.tensor([[1]]))
>>> output["logits"].shape
torch.Size([1, 1])
>>> output["loss"]
tensor(..., grad_fn=<BinaryCrossEntropyWithLogitsBackward0>)

Source code in multimolecule/models/dnaberts/modeling_dnaberts.py

Python
class DnaBertSForSequencePrediction(DnaBertSPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule import DnaBertSConfig, DnaBertSForSequencePrediction
        >>> config = DnaBertSConfig()
        >>> model = DnaBertSForSequencePrediction(config)
        >>> input_ids = torch.randint(0, config.vocab_size, (1, 16))
        >>> output = model(input_ids, labels=torch.tensor([[1]]))
        >>> output["logits"].shape
        torch.Size([1, 1])
        >>> output["loss"]  # doctest:+ELLIPSIS
        tensor(..., grad_fn=<BinaryCrossEntropyWithLogitsBackward0>)
    """

    def __init__(self, config: DnaBertSConfig):
        super().__init__(config)
        self.model = DnaBertSModel(config)
        self.sequence_head = SequencePredictionHead(config)
        self.head_config = self.sequence_head.config

        # Initialize weights and apply final processing
        self.post_init()

    @can_return_tuple
    def forward(
        self,
        input_ids: Tensor | NestedTensor | None = None,
        attention_mask: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels: Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[Tensor, ...] | SequencePredictorOutput:
        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            return_dict=True,
            **kwargs,
        )

        output = self.sequence_head(outputs, labels)
        logits, loss = output.logits, output.loss

        return SequencePredictorOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

DnaBertSForTokenPrediction ¶

Bases: DnaBertSPreTrainedModel

Examples:

Python Console Session
>>> import torch
>>> from multimolecule import DnaBertSConfig, DnaBertSForTokenPrediction
>>> config = DnaBertSConfig()
>>> model = DnaBertSForTokenPrediction(config)
>>> input_ids = torch.randint(0, config.vocab_size, (1, 16))
>>> output = model(input_ids, labels=torch.randint(2, (1, 14)))
>>> output["logits"].shape
torch.Size([1, 14, 1])
>>> output["loss"]
tensor(..., grad_fn=<BinaryCrossEntropyWithLogitsBackward0>)

Source code in multimolecule/models/dnaberts/modeling_dnaberts.py

Python
class DnaBertSForTokenPrediction(DnaBertSPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule import DnaBertSConfig, DnaBertSForTokenPrediction
        >>> config = DnaBertSConfig()
        >>> model = DnaBertSForTokenPrediction(config)
        >>> input_ids = torch.randint(0, config.vocab_size, (1, 16))
        >>> output = model(input_ids, labels=torch.randint(2, (1, 14)))
        >>> output["logits"].shape
        torch.Size([1, 14, 1])
        >>> output["loss"]  # doctest:+ELLIPSIS
        tensor(..., grad_fn=<BinaryCrossEntropyWithLogitsBackward0>)
    """

    def __init__(self, config: DnaBertSConfig):
        super().__init__(config)
        self.model = DnaBertSModel(config, add_pooling_layer=False)
        self.token_head = TokenPredictionHead(config)
        self.head_config = self.token_head.config

        # Initialize weights and apply final processing
        self.post_init()

    @can_return_tuple
    def forward(
        self,
        input_ids: Tensor | NestedTensor | None = None,
        attention_mask: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        labels: Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[Tensor, ...] | TokenPredictorOutput:
        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            return_dict=True,
            **kwargs,
        )

        output = self.token_head(outputs, attention_mask, input_ids, labels)
        logits, loss = output.logits, output.loss

        return TokenPredictorOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

DnaBertSModel ¶

Bases: DnaBertSPreTrainedModel

Examples:

Python Console Session
>>> import torch
>>> from multimolecule import DnaBertSConfig, DnaBertSModel
>>> config = DnaBertSConfig()
>>> model = DnaBertSModel(config)
>>> input_ids = torch.randint(0, config.vocab_size, (1, 16))
>>> output = model(input_ids)
>>> output["last_hidden_state"].shape
torch.Size([1, 16, 768])
>>> output["pooler_output"].shape
torch.Size([1, 768])

Source code in multimolecule/models/dnaberts/modeling_dnaberts.py

Python
class DnaBertSModel(DnaBertSPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule import DnaBertSConfig, DnaBertSModel
        >>> config = DnaBertSConfig()
        >>> model = DnaBertSModel(config)
        >>> input_ids = torch.randint(0, config.vocab_size, (1, 16))
        >>> output = model(input_ids)
        >>> output["last_hidden_state"].shape
        torch.Size([1, 16, 768])
        >>> output["pooler_output"].shape
        torch.Size([1, 768])
    """

    def __init__(self, config: DnaBertSConfig, add_pooling_layer: bool = True):
        super().__init__(config)
        self.pad_token_id = config.pad_token_id
        self.gradient_checkpointing = False
        self.embeddings = DnaBertSEmbeddings(config)
        self.encoder = DnaBertSEncoder(config)
        self.pooler = DnaBertSPooler(config) if add_pooling_layer else None

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    @merge_with_config_defaults
    @capture_outputs
    def forward(
        self,
        input_ids: Tensor | NestedTensor | None = None,
        attention_mask: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        encoder_hidden_states: Tensor | None = None,
        encoder_attention_mask: Tensor | None = None,
        past_key_values: Cache | None = None,
        use_cache: bool | None = None,
        cache_position: Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[Tensor, ...] | BaseModelOutputWithPoolingAndCrossAttentions:
        r"""
        Args:
            encoder_hidden_states:
                Shape: `(batch_size, sequence_length, hidden_size)`

                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
                the model is configured as a decoder.
            encoder_attention_mask:
                Shape: `(batch_size, sequence_length)`

                Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
                in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.
            past_key_values:
                Tuple of length `config.n_layers` with each tuple having 4 tensors of shape
                `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)

                Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up
                decoding.

                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
            use_cache:
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
        """
        if self.config.is_decoder:
            use_cache = use_cache if use_cache is not None else self.config.use_cache
        else:
            use_cache = False
        if use_cache and past_key_values is None:
            past_key_values = (
                EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
                if encoder_hidden_states is not None or self.config.is_encoder_decoder
                else DynamicCache(config=self.config)
            )

        if isinstance(input_ids, NestedTensor) and attention_mask is None:
            attention_mask = input_ids.mask
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
        if input_ids is not None:
            device = input_ids.device
            seq_length = input_ids.shape[1]
        else:
            device = inputs_embeds.device  # type: ignore[union-attr]
            seq_length = inputs_embeds.shape[1]  # type: ignore[union-attr]

        # past_key_values_length
        past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
        if cache_position is None:
            cache_position = torch.arange(past_key_values_length, past_key_values_length + seq_length, device=device)

        if attention_mask is None and input_ids is not None and self.pad_token_id is not None:
            attention_mask = input_ids.ne(self.pad_token_id)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            inputs_embeds=inputs_embeds,
        )
        attention_mask, encoder_attention_mask = self._create_attention_masks(
            attention_mask=attention_mask,
            encoder_attention_mask=encoder_attention_mask,
            embedding_output=embedding_output,
            encoder_hidden_states=encoder_hidden_states,
            cache_position=cache_position,
            past_key_values=past_key_values,
        )
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )
        sequence_output = encoder_outputs.last_hidden_state
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
        )

    def _create_attention_masks(
        self,
        attention_mask,
        encoder_attention_mask,
        embedding_output,
        encoder_hidden_states,
        cache_position,
        past_key_values,
    ):
        if self.config.is_decoder:
            attention_mask = create_causal_mask(
                config=self.config,
                inputs_embeds=embedding_output,
                attention_mask=attention_mask,
                cache_position=cache_position,
                past_key_values=past_key_values,
            )
        else:
            attention_mask = create_bidirectional_mask(
                config=self.config, inputs_embeds=embedding_output, attention_mask=attention_mask
            )

        if encoder_attention_mask is not None:
            encoder_attention_mask = create_bidirectional_mask(
                config=self.config,
                inputs_embeds=embedding_output,
                attention_mask=encoder_attention_mask,
                encoder_hidden_states=encoder_hidden_states,
            )

        return attention_mask, encoder_attention_mask

forward ¶

Python

forward(
    input_ids: Tensor | NestedTensor | None = None,
    attention_mask: Tensor | None = None,
    inputs_embeds: Tensor | NestedTensor | None = None,
    encoder_hidden_states: Tensor | None = None,
    encoder_attention_mask: Tensor | None = None,
    past_key_values: Cache | None = None,
    use_cache: bool | None = None,
    cache_position: Tensor | None = None,
    **kwargs: Unpack[TransformersKwargs]
) -> (
    tuple[Tensor, ...]
    | BaseModelOutputWithPoolingAndCrossAttentions
)

Parameters:

Name	Type	Description	Default
`encoder_hidden_states` ¶	`Tensor \| None`	Shape: `(batch_size, sequence_length, hidden_size)` Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.	`None`
`encoder_attention_mask` ¶	`Tensor \| None`	Shape: `(batch_size, sequence_length)` Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: 1 for tokens that are not masked, 0 for tokens that are masked.	`None`
`past_key_values` ¶	`Cache \| None`	Tuple of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head) Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.	`None`
`use_cache` ¶	`bool \| None`	If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).	`None`

Source code in multimolecule/models/dnaberts/modeling_dnaberts.py

Python
@merge_with_config_defaults
@capture_outputs
def forward(
    self,
    input_ids: Tensor | NestedTensor | None = None,
    attention_mask: Tensor | None = None,
    inputs_embeds: Tensor | NestedTensor | None = None,
    encoder_hidden_states: Tensor | None = None,
    encoder_attention_mask: Tensor | None = None,
    past_key_values: Cache | None = None,
    use_cache: bool | None = None,
    cache_position: Tensor | None = None,
    **kwargs: Unpack[TransformersKwargs],
) -> tuple[Tensor, ...] | BaseModelOutputWithPoolingAndCrossAttentions:
    r"""
    Args:
        encoder_hidden_states:
            Shape: `(batch_size, sequence_length, hidden_size)`

            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask:
            Shape: `(batch_size, sequence_length)`

            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
            in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        past_key_values:
            Tuple of length `config.n_layers` with each tuple having 4 tensors of shape
            `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)

            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up
            decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
            that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
            all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        use_cache:
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
            (see `past_key_values`).
    """
    if self.config.is_decoder:
        use_cache = use_cache if use_cache is not None else self.config.use_cache
    else:
        use_cache = False
    if use_cache and past_key_values is None:
        past_key_values = (
            EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
            if encoder_hidden_states is not None or self.config.is_encoder_decoder
            else DynamicCache(config=self.config)
        )

    if isinstance(input_ids, NestedTensor) and attention_mask is None:
        attention_mask = input_ids.mask
    if (input_ids is None) ^ (inputs_embeds is not None):
        raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
    if input_ids is not None:
        device = input_ids.device
        seq_length = input_ids.shape[1]
    else:
        device = inputs_embeds.device  # type: ignore[union-attr]
        seq_length = inputs_embeds.shape[1]  # type: ignore[union-attr]

    # past_key_values_length
    past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
    if cache_position is None:
        cache_position = torch.arange(past_key_values_length, past_key_values_length + seq_length, device=device)

    if attention_mask is None and input_ids is not None and self.pad_token_id is not None:
        attention_mask = input_ids.ne(self.pad_token_id)

    embedding_output = self.embeddings(
        input_ids=input_ids,
        inputs_embeds=inputs_embeds,
    )
    attention_mask, encoder_attention_mask = self._create_attention_masks(
        attention_mask=attention_mask,
        encoder_attention_mask=encoder_attention_mask,
        embedding_output=embedding_output,
        encoder_hidden_states=encoder_hidden_states,
        cache_position=cache_position,
        past_key_values=past_key_values,
    )
    encoder_outputs = self.encoder(
        embedding_output,
        attention_mask,
        encoder_hidden_states,
        encoder_attention_mask=encoder_attention_mask,
        past_key_values=past_key_values,
        use_cache=use_cache,
        cache_position=cache_position,
        **kwargs,
    )
    sequence_output = encoder_outputs.last_hidden_state
    pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

    return BaseModelOutputWithPoolingAndCrossAttentions(
        last_hidden_state=sequence_output,
        pooler_output=pooled_output,
        past_key_values=encoder_outputs.past_key_values,
    )

DnaBertSPreTrainedModel ¶

Bases: PreTrainedModel

An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.

Source code in multimolecule/models/dnaberts/modeling_dnaberts.py

Python
class DnaBertSPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = DnaBertSConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _supports_flash_attn = True
    _supports_sdpa = True
    _supports_flex_attn = True
    _supports_attention_backend = True
    _can_record_outputs: dict[str, Any] | None = None
    _no_split_modules = ["DnaBertSLayer", "DnaBertSEmbeddings"]

    @torch.no_grad()
    def _init_weights(self, module: nn.Module):
        super()._init_weights(module)

DNABERT-S¶

DNABERT-S¶

Disclaimer¶

Model Details¶

Model Specification¶

Links¶

Usage¶

Direct Use¶

Feature Extraction¶

Downstream Use¶

Extract Features¶

Sequence Classification / Regression¶

Token Classification / Regression¶

Contact Classification / Regression¶

Training Details¶

Training Data¶

Training Procedure¶

Pre-training¶

Citation¶

Contact¶

License¶

multimolecule.models.dnaberts ¶

DnaBertSConfig ¶

vocab_size ¶

hidden_size ¶

num_hidden_layers ¶

num_attention_heads ¶

intermediate_size ¶

hidden_act ¶

hidden_dropout ¶

attention_dropout ¶

max_position_embeddings ¶

initializer_range ¶

layer_norm_eps ¶

position_embedding_type ¶

alibi_starting_size ¶

is_decoder ¶

use_cache ¶

head ¶

lm_head ¶

add_cross_attention ¶

DnaBertSForContactPrediction ¶

DnaBertSForSequencePrediction ¶

DnaBertSForTokenPrediction ¶

DnaBertSModel ¶

forward ¶

encoder_hidden_states ¶

encoder_attention_mask ¶

past_key_values ¶

use_cache ¶

DnaBertSPreTrainedModel ¶

`vocab_size` ¶

`hidden_size` ¶

`num_hidden_layers` ¶

`num_attention_heads` ¶

`intermediate_size` ¶

`hidden_act` ¶

`hidden_dropout` ¶

`attention_dropout` ¶

`max_position_embeddings` ¶

`initializer_range` ¶

`layer_norm_eps` ¶

`position_embedding_type` ¶

`alibi_starting_size` ¶

`is_decoder` ¶

`use_cache` ¶

`head` ¶

`lm_head` ¶

`add_cross_attention` ¶

`encoder_hidden_states` ¶

`encoder_attention_mask` ¶

`past_key_values` ¶

`use_cache` ¶