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AbLang

Pre-trained antibody language model using a masked language modeling (MLM) objective.

Disclaimer

This is an UNOFFICIAL implementation of AbLang: an antibody language model for completing antibody sequences by Tobias H. Olsen, et al.

The OFFICIAL repository of AbLang is at oxpig/AbLang.

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 AbLang did not write this model card for this model so this model card has been written by the MultiMolecule team.

Model Details

AbLang v1 is an encoder-only Transformer trained on antibody sequences from the Observed Antibody Space (OAS). The official release provides separate heavy-chain and light-chain checkpoints. Both variants use the same architecture and vocabulary, but they were trained on chain-specific data and are represented as separate MultiMolecule variants.

Variants

Model Specification

Variant Chain Type Num Layers Hidden Size Num Heads Intermediate Size Num Parameters (M) FLOPs (G) MACs (G) Max Num Tokens
AbLang-Heavy Heavy 12 768 12 3072 85.83 28.18 14.06 159
AbLang-Light Light

Usage

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

Bash
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pip install multimolecule

Direct Use

Masked Language Modeling

You can use this model directly with a pipeline for masked language modeling:

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

predictor = pipeline("fill-mask", model="multimolecule/ablang-heavy")
output = predictor("EVQLVESGGGLVQPGGSLRLSCAASGFTFSSY<mask>MSWVRQAPGKGLEWVSA")

Downstream Use

Extract Features

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

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from multimolecule import AbLangModel, ProteinTokenizer


tokenizer = ProteinTokenizer.from_pretrained("multimolecule/ablang-heavy")
model = AbLangModel.from_pretrained("multimolecule/ablang-heavy")

text = "EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSA"
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:

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import torch
from multimolecule import AbLangForSequencePrediction, ProteinTokenizer


tokenizer = ProteinTokenizer.from_pretrained("multimolecule/ablang-heavy")
model = AbLangForSequencePrediction.from_pretrained("multimolecule/ablang-heavy")

text = "EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSA"
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 residue-level task in PyTorch:

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import torch
from multimolecule import AbLangForTokenPrediction, ProteinTokenizer


tokenizer = ProteinTokenizer.from_pretrained("multimolecule/ablang-heavy")
model = AbLangForTokenPrediction.from_pretrained("multimolecule/ablang-heavy")

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

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

Training Details

AbLang was trained with masked language modeling (MLM) as the pre-training objective.

Training Data

AbLang was trained on antibody sequences from the Observed Antibody Space. The heavy-chain model was trained on 14,126,724 sequences, and the light-chain model was trained on 187,068 sequences.

Training Procedure

Pre-training

The heavy-chain and light-chain checkpoints were trained separately on chain-specific OAS sequences. Please refer to the original paper for details on the training setup.

Citation

BibTeX
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@article{olsen2022ablang,
  title   = {AbLang: an antibody language model for completing antibody sequences},
  author  = {Olsen, Tobias H. and Moal, Iain H. and Deane, Charlotte M.},
  journal = {Bioinformatics Advances},
  volume  = {2},
  number  = {1},
  pages   = {vbac046},
  year    = {2022},
  doi     = {10.1093/bioadv/vbac046},
  url     = {https://doi.org/10.1093/bioadv/vbac046},
}

Note

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

BibTeX
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@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 AbLang 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
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SPDX-License-Identifier: AGPL-3.0-or-later

API Reference

AbLangConfig

Bases: PreTrainedConfig

This is the configuration class to store the configuration of an AbLangModel. It is used to instantiate an AbLang v1 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a configuration similar to the official AbLang v1 heavy/light checkpoints.

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 AbLang model. Defines the number of different tokens that can be represented by the input_ids passed when calling [AbLangModel].

 37

hidden_size

int

Dimensionality of the encoder layers and the pooler output.

 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 feed-forward layer in the Transformer encoder.

 3072

hidden_act

str

Non-linear activation function used by the feed-forward layer and masked language modeling head.

 'gelu'

hidden_dropout

float

Dropout probability applied after embeddings, self-attention, and feed-forward projections.

 0.1

attention_dropout

float

Dropout probability applied to attention probabilities.

 0.1

max_position_embeddings

int

Size of the learned absolute position embedding table. Position id 0 is reserved for padding.

 160

initializer_range

float

Standard deviation of the normal initializer for embedding and linear layers.

 0.02

layer_norm_eps

float

Epsilon used by layer normalization layers.

 1e-12

chain

str | None

Optional antibody chain label for converted checkpoints. AbLang v1 provides separate heavy and light checkpoints trained on different data.

 None

head

HeadConfig | None

The configuration of the downstream prediction head.

 None

lm_head

MaskedLMHeadConfig | None

The configuration of the masked language model head.

 None

Examples:

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>>> from multimolecule.models.ablang import AbLangConfig, AbLangModel
>>> configuration = AbLangConfig()
>>> model = AbLangModel(configuration)
>>> configuration = model.config
Source code in multimolecule/models/ablang/configuration_ablang.py
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class AbLangConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of an
    [`AbLangModel`][multimolecule.models.AbLangModel]. It is used to instantiate an AbLang v1 model according to the
    specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a
    configuration similar to the official AbLang v1 heavy/light checkpoints.

    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 AbLang model. Defines the number of different tokens that can be represented by the
            `input_ids` passed when calling [`AbLangModel`].
        hidden_size:
            Dimensionality of the encoder layers and the pooler output.
        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 feed-forward layer in the Transformer encoder.
        hidden_act:
            Non-linear activation function used by the feed-forward layer and masked language modeling head.
        hidden_dropout:
            Dropout probability applied after embeddings, self-attention, and feed-forward projections.
        attention_dropout:
            Dropout probability applied to attention probabilities.
        max_position_embeddings:
            Size of the learned absolute position embedding table. Position id `0` is reserved for padding.
        initializer_range:
            Standard deviation of the normal initializer for embedding and linear layers.
        layer_norm_eps:
            Epsilon used by layer normalization layers.
        chain:
            Optional antibody chain label for converted checkpoints. AbLang v1 provides separate `heavy` and `light`
            checkpoints trained on different data.
        head:
            The configuration of the downstream prediction head.
        lm_head:
            The configuration of the masked language model head.

    Examples:
        >>> from multimolecule.models.ablang import AbLangConfig, AbLangModel
        >>> configuration = AbLangConfig()
        >>> model = AbLangModel(configuration)
        >>> configuration = model.config
    """

    model_type = "ablang"
    position_embedding_type = "absolute"

    def __init__(
        self,
        vocab_size: int = 37,
        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 = 0.1,
        max_position_embeddings: int = 160,
        initializer_range: float = 0.02,
        layer_norm_eps: float = 1.0e-12,
        chain: str | None = None,
        pad_token_id: int = 0,
        bos_token_id: int = 1,
        eos_token_id: int = 2,
        unk_token_id: int = 3,
        mask_token_id: int = 4,
        null_token_id: int = 5,
        head: HeadConfig | None = None,
        lm_head: MaskedLMHeadConfig | None = None,
        **kwargs,
    ):
        kwargs.setdefault("tie_word_embeddings", False)
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            unk_token_id=unk_token_id,
            mask_token_id=mask_token_id,
            null_token_id=null_token_id,
            **kwargs,
        )
        validate_attention_dimensions(hidden_size, num_attention_heads)
        hidden_act = hidden_act.lower()
        if max_position_embeddings <= 1:
            raise ValueError("max_position_embeddings must be greater than 1 because position id 0 is padding.")

        self.vocab_size = vocab_size
        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.chain = chain
        self.position_embedding_type = "absolute"
        self.head = HeadConfig(**head) if head is not None else None
        self.lm_head = (
            MaskedLMHeadConfig(**lm_head)
            if lm_head is not None
            else MaskedLMHeadConfig(
                transform="nonlinear",
                transform_act=hidden_act,
                bias=True,
                layer_norm_eps=layer_norm_eps,
            )
        )

AbLangForMaskedLM

Bases: AbLangPreTrainedModel

Examples:

Python Console Session
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>>> import torch
>>> from multimolecule.models.ablang import AbLangConfig, AbLangForMaskedLM
>>> config = AbLangConfig()
>>> model = AbLangForMaskedLM(config)
>>> input_ids = torch.tensor([[1, 9, 23, 21, 15, 2]])
>>> output = model(input_ids, labels=input_ids)
>>> output["logits"].shape
torch.Size([1, 6, 37])
Source code in multimolecule/models/ablang/modeling_ablang.py
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class AbLangForMaskedLM(AbLangPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule.models.ablang import AbLangConfig, AbLangForMaskedLM
        >>> config = AbLangConfig()
        >>> model = AbLangForMaskedLM(config)
        >>> input_ids = torch.tensor([[1, 9, 23, 21, 15, 2]])
        >>> output = model(input_ids, labels=input_ids)
        >>> output["logits"].shape
        torch.Size([1, 6, 37])
    """

    _tied_weights_keys = {
        "lm_head.decoder.bias": "lm_head.bias",
    }

    def get_expanded_tied_weights_keys(self, all_submodels: bool = False) -> dict:
        tied_weights = super().get_expanded_tied_weights_keys(all_submodels=all_submodels)
        if all_submodels:
            return tied_weights
        return tied_weights | self._tied_weights_keys

    def __init__(self, config: AbLangConfig):
        super().__init__(config)
        self.model = AbLangModel(config, add_pooling_layer=False)
        self.lm_head = MaskedLMHead(config)

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

    def get_output_embeddings(self):
        return self.lm_head.decoder

    def set_output_embeddings(self, embeddings):
        self.lm_head.decoder = embeddings
        if hasattr(self.lm_head, "bias"):
            self.lm_head.bias = embeddings.bias

    @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, ...] | MaskedLMOutput:
        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            return_dict=True,
            **kwargs,
        )

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

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

AbLangForSequencePrediction

Bases: AbLangPreTrainedModel

Examples:

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>>> import torch
>>> from multimolecule.models.ablang import AbLangConfig, AbLangForSequencePrediction
>>> config = AbLangConfig()
>>> model = AbLangForSequencePrediction(config)
>>> input_ids = torch.tensor([[1, 9, 23, 21, 15, 2]])
>>> output = model(input_ids, labels=torch.tensor([[1]]))
>>> output["logits"].shape
torch.Size([1, 1])
Source code in multimolecule/models/ablang/modeling_ablang.py
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class AbLangForSequencePrediction(AbLangPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule.models.ablang import AbLangConfig, AbLangForSequencePrediction
        >>> config = AbLangConfig()
        >>> model = AbLangForSequencePrediction(config)
        >>> input_ids = torch.tensor([[1, 9, 23, 21, 15, 2]])
        >>> output = model(input_ids, labels=torch.tensor([[1]]))
        >>> output["logits"].shape
        torch.Size([1, 1])
    """

    def __init__(self, config: AbLangConfig):
        super().__init__(config)
        self.model = AbLangModel(config)
        self.num_labels = config.num_labels
        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,
        )

AbLangForTokenPrediction

Bases: AbLangPreTrainedModel

Examples:

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>>> import torch
>>> from multimolecule.models.ablang import AbLangConfig, AbLangForTokenPrediction
>>> config = AbLangConfig()
>>> model = AbLangForTokenPrediction(config)
>>> input_ids = torch.tensor([[1, 9, 23, 21, 15, 2]])
>>> output = model(input_ids, labels=torch.randint(2, (1, 4)))
>>> output["logits"].shape
torch.Size([1, 4, 1])
Source code in multimolecule/models/ablang/modeling_ablang.py
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class AbLangForTokenPrediction(AbLangPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule.models.ablang import AbLangConfig, AbLangForTokenPrediction
        >>> config = AbLangConfig()
        >>> model = AbLangForTokenPrediction(config)
        >>> input_ids = torch.tensor([[1, 9, 23, 21, 15, 2]])
        >>> output = model(input_ids, labels=torch.randint(2, (1, 4)))
        >>> output["logits"].shape
        torch.Size([1, 4, 1])
    """

    def __init__(self, config: AbLangConfig):
        super().__init__(config)
        self.model = AbLangModel(config, add_pooling_layer=False)
        self.num_labels = config.num_labels
        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,
        )

AbLangModel

Bases: AbLangPreTrainedModel

Examples:

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>>> import torch
>>> from multimolecule.models.ablang import AbLangConfig, AbLangModel
>>> config = AbLangConfig()
>>> model = AbLangModel(config)
>>> input_ids = torch.tensor([[1, 9, 23, 21, 15, 2]])
>>> output = model(input_ids)
>>> output["last_hidden_state"].shape
torch.Size([1, 6, 768])
>>> output["pooler_output"].shape
torch.Size([1, 768])
Source code in multimolecule/models/ablang/modeling_ablang.py
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class AbLangModel(AbLangPreTrainedModel):
    """
    Examples:
        >>> import torch
        >>> from multimolecule.models.ablang import AbLangConfig, AbLangModel
        >>> config = AbLangConfig()
        >>> model = AbLangModel(config)
        >>> input_ids = torch.tensor([[1, 9, 23, 21, 15, 2]])
        >>> output = model(input_ids)
        >>> output["last_hidden_state"].shape
        torch.Size([1, 6, 768])
        >>> output["pooler_output"].shape
        torch.Size([1, 768])
    """

    def __init__(self, config: AbLangConfig, add_pooling_layer: bool = True):
        super().__init__(config)
        self.pad_token_id = config.pad_token_id
        self.embeddings = AbLangEmbeddings(config)
        self.encoder = AbLangEncoder(config)
        self.pooler = AbLangPooler(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
    def forward(
        self,
        input_ids: Tensor | NestedTensor | None = None,
        attention_mask: Tensor | None = None,
        inputs_embeds: Tensor | NestedTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[Tensor, ...] | BaseModelOutputWithPoolingAndCrossAttentions:
        if isinstance(input_ids, NestedTensor):
            if attention_mask is None:
                attention_mask = input_ids.mask
            input_ids = input_ids.tensor
        if isinstance(inputs_embeds, NestedTensor):
            if attention_mask is None:
                attention_mask = inputs_embeds.mask
            inputs_embeds = inputs_embeds.tensor
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if attention_mask is None:
            if input_ids is not None and self.pad_token_id is not None:
                attention_mask = input_ids.ne(self.pad_token_id)
            else:
                if inputs_embeds is None:
                    raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
                input_shape = inputs_embeds.shape[:2]
                attention_mask = torch.ones(input_shape, dtype=torch.bool, device=inputs_embeds.device)
        else:
            attention_mask = attention_mask.to(torch.bool)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
        )
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=attention_mask,
            output_hidden_states=kwargs.get("output_hidden_states", self.config.output_hidden_states),
            output_attentions=kwargs.get("output_attentions", self.config.output_attentions),
        )
        sequence_output = encoder_outputs.last_hidden_state
        pooled_output = (
            self.pooler(sequence_output, attention_mask=attention_mask, input_ids=input_ids) if self.pooler else None
        )

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

AbLangPreTrainedModel

Bases: PreTrainedModel

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

Source code in multimolecule/models/ablang/modeling_ablang.py
Python
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class AbLangPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = AbLangConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _can_record_outputs: dict[str, Any] | None = None
    _no_split_modules = ["AbLangLayer"]

    @torch.no_grad()
    def _init_weights(self, module: nn.Module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            init.normal_(module.weight, mean=0.0, std=std)
            if module.bias is not None:
                init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            init.normal_(module.weight, mean=0.0, std=std)
            if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False):
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            init.ones_(module.weight)
            init.zeros_(module.bias)