Transformers documentation
ESMC
This model was contributed to Hugging Face Transformers on 2026-08-19.
ESMC
Overview
ESMC (ESM Cambrian) is a family of protein language models released by BioHub. It is a bidirectional Transformer encoder trained with a masked-language-modelling objective over amino-acid sequences. Like ESM-2, ESMC produces per-residue representations that are useful for downstream protein modelling tasks.
ESMC is suitable for fine-tuning on protein classification or token classification tasks. It is also used as the backbone of ESMFold2, where it generates representations that are used as input to the folding head.
Pre-trained checkpoints are available on the Hugging Face Hub:
Usage example
ESMC is registered with the auto classes (AutoModel, AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification).
import torch
from transformers import pipeline
extractor = pipeline(
task="feature-extraction",
model="biohub/ESMC-300M",
)
# Per-residue representations of shape (batch, sequence_length, hidden_size).
representations = extractor("MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQ", return_tensors="pt")EsmcConfig
class transformers.EsmcConfig
< source >( transformers_version: str | None = Nonearchitectures: list[str] | None = Noneoutput_hidden_states: bool | None = Falsereturn_dict: bool | None = Truedtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = Nonechunk_size_feed_forward: int = 0is_encoder_decoder: bool = Falseid2label: dict[int, str] | dict[str, str] | None = Nonelabel2id: dict[str, int] | dict[str, str] | None = Noneproblem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = Nonevocab_size: int = 64hidden_size: int = 2560intermediate_size: int = 6912num_hidden_layers: int = 80num_attention_heads: int = 40num_key_value_heads: int | None = Nonehidden_act: str = 'silu'max_position_embeddings: int = 2048initializer_range: float = 0.02pad_token_id: int | None = 1bos_token_id: int | None = 0eos_token_id: int | list[int] | None = 2tie_word_embeddings: bool = Falserope_parameters: transformers.modeling_rope_utils.RopeParameters | dict | None = Noneattention_bias: bool = Falseattention_dropout: int | float | None = 0.0mlp_bias: bool = Falsehead_dim: int | None = Nonemask_token_id: int | None = 32classifier_dropout: float | None = 0.1 )
Parameters
- vocab_size (int, optional, defaults to 64) — Vocabulary size of the model. Defines the number of different tokens that can be represented by the input_ids.
- hidden_size (int, optional, defaults to 2560) — Dimension of the hidden representations.
- intermediate_size (int, optional, defaults to 6912) — Dimension of the MLP representations.
- num_hidden_layers (int, optional, defaults to 80) — Number of hidden layers in the Transformer decoder.
- num_attention_heads (int, optional, defaults to 40) — Number of attention heads for each attention layer in the Transformer decoder.
- num_key_value_heads (int, optional) — This is the number of key_value heads that should be used to implement Grouped Query Attention. If num_key_value_heads=num_attention_heads, the model will use Multi Head Attention (MHA), if num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out this paper. If it is not specified, will default to num_attention_heads.
- hidden_act (str, optional, defaults to silu) — The non-linear activation function (function or string) in the decoder. For example, “gelu”, “relu”, “silu”, etc.
- max_position_embeddings (int, optional, defaults to 2048) — The maximum sequence length that this model might ever be used with.
- initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- pad_token_id (int, optional, defaults to 1) — Token id used for padding in the vocabulary.
- bos_token_id (int, optional, defaults to 0) — Token id used for beginning-of-stream in the vocabulary.
- eos_token_id (Union[int, list[int]], optional, defaults to 2) — Token id used for end-of-stream in the vocabulary.
- tie_word_embeddings (bool, optional, defaults to False) — Whether to tie weight embeddings according to model’s tied_weights_keys mapping.
- rope_parameters (Union[~modeling_rope_utils.RopeParameters, dict], optional) — Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for rope_theta and optionally parameters used for scaling in case you want to use RoPE with longer max_position_embeddings.
- attention_bias (bool, optional, defaults to False) — Whether to use a bias in the query, key, value and output projection layers during self-attention.
- attention_dropout (Union[int, float], optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
- mlp_bias (bool, optional, defaults to False) — Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
- head_dim (int, optional) — The attention head dimension. If None, it will default to hidden_size // num_attention_heads
- mask_token_id (int, optional, defaults to 32) —
Index of the mask token in the vocabulary (
"<mask>"), used for masked language modelling. - classifier_dropout (float, optional, defaults to 0.1) — Dropout ratio for the classification head.
This is the configuration class to store the configuration of a EsmcModel. It is used to instantiate a Esmc 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 biohub/ESMC-6B
Configuration objects inherit from [PreTrainedConfig] and can be used to control the model outputs. Read the documentation from [PreTrainedConfig] for more information.
Examples:
>>> from transformers import EsmcConfig, EsmcModel
>>> # Initializing an ESMC biohub/ESMC-6B style configuration
>>> configuration = EsmcConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = EsmcModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.configEsmcTokenizer
class transformers.EsmcTokenizer
< source >( unk_token = '<unk>'cls_token = '<cls>'pad_token = '<pad>'mask_token = '<mask>'eos_token = '<eos>'bos_token = Nonechain_break_token = '|'extra_special_tokens = None**kwargs )
Parameters
- unk_token (str, optional, defaults to ”
” ) — The unknown token. - cls_token (str, optional, defaults to ”
” ) — The classification token (prepended to every sequence). - pad_token (str, optional, defaults to ”
” ) — The padding token. - mask_token (str, optional, defaults to ”
” ) — The mask token, used for masked language modelling. - eos_token (str, optional, defaults to ”
” ) — The end-of-sequence token (appended to every sequence). - bos_token (str, optional, defaults to ”
” ) — The beginning-of-sequence token (prepended to every sequence). When unset, uses cls_token. - chain_break_token (str, optional, defaults to ”|”) — Token inserted between chains in multi-chain protein inputs. Registered as a model-specific special token, so it is exposed as chain_break_token / chain_break_token_id.
- extra_special_tokens (list[str], optional) — Additional special tokens to register with the tokenizer (round-tripped from a saved config).
Construct an ESMC tokenizer.
This tokenizer is a character-level tokenizer backed by the HuggingFace tokenizers library.
It wraps every sequence with <cls> and <eos> tokens and supports a | chain-break
token for multi-chain inputs.
EsmcModel
class transformers.EsmcModel
< source >( config: EsmcConfig )
Parameters
- config (EsmcConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
The bare Esmc Model outputting raw hidden-states without any specific head on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( input_ids: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.Tensor] = Nonesequence_id: typing.Optional[torch.Tensor] = None**kwargs: Unpack ) → [~modeling_outputs.BaseModelOutput] or tuple(torch.FloatTensor)
Parameters
- input_ids (torch.Tensor of shape (batch_size, sequence_length), optional) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
Indices can be obtained using [AutoTokenizer]. See [PreTrainedTokenizer.encode] and [PreTrainedTokenizer.call] for details.
- attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- position_ids (torch.Tensor of shape (batch_size, sequence_length), optional) —
Position of each token in the sequence, used by RoPE. Defaults to
arange(sequence_length).ESMC positions are absolute over the whole input, and keep running across chain boundaries in a multi-chain input. The chain structure is carried separately by
sequence_id, which masks attention: unlike the packed-sequence format used elsewhere in the library, these are therefore not per-chain position ids restarting at 0, and the chain structure is deliberately not inferred from them. - sequence_id (torch.Tensor of shape (batch_size, sequence_length), optional) —
Integer chain-ID tensor for chain-aware attention masking. Tokens with the same
non-negative integer value can attend to each other; tokens with different values
cannot (cross-chain masking). Padding positions should be set to
-1and inputs must be right-padded (RoPE uses absolute positions starting at 0). When provided,attention_maskis ignored. Passingsequence_idbuilds a custom attention mask, which requirestorch>=2.6. Multi-chain inputs additionally require a non-flashattn_implementation('sdpa'/'eager'/'flex_attention'); flash attention only supports the single-chain case.
Returns
[~modeling_outputs.BaseModelOutput] or tuple(torch.FloatTensor)
A [~modeling_outputs.BaseModelOutput] or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration ([EsmcConfig]) and inputs.
The [EsmcModel] forward method, overrides the call special method.
Although the recipe for forward pass needs to be defined within this function, one should call the [Module] instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples:
>>> from transformers import AutoTokenizer, EsmcModel
>>> model = EsmcModel.from_pretrained("biohub/ESMC-300M")
>>> tokenizer = AutoTokenizer.from_pretrained("biohub/ESMC-300M")
>>> inputs = tokenizer(["MLKNVQVQLV"], return_tensors="pt")
>>> outputs = model(**inputs)
>>> outputs.last_hidden_state.shape
torch.Size([1, 12, 960])EsmcForMaskedLM
class transformers.EsmcForMaskedLM
< source >( config: EsmcConfig )
Parameters
- config (EsmcConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
The Esmc Model with a language modeling head on top.”
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( input_ids: typing.Optional[torch.Tensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.Tensor] = Nonesequence_id: typing.Optional[torch.Tensor] = Nonelabels: typing.Optional[torch.Tensor] = None**kwargs: Unpack ) → [~modeling_outputs.MaskedLMOutput] or tuple(torch.FloatTensor)
Parameters
- input_ids (torch.Tensor of shape (batch_size, sequence_length), optional) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
Indices can be obtained using [AutoTokenizer]. See [PreTrainedTokenizer.encode] and [PreTrainedTokenizer.call] for details.
- attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- position_ids (torch.Tensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
- sequence_id (torch.Tensor of shape (batch_size, sequence_length), optional) — Integer chain-ID tensor forwarded to the encoder for chain-aware attention masking. See EsmcModel.forward() for the encoding.
- labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for masked language modelling loss. Positions with label
-100are ignored. Other positions must be in[0, config.vocab_size).
Returns
[~modeling_outputs.MaskedLMOutput] or tuple(torch.FloatTensor)
A [~modeling_outputs.MaskedLMOutput] or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration ([EsmcConfig]) and inputs.
The [EsmcForMaskedLM] forward method, overrides the call special method.
Although the recipe for forward pass needs to be defined within this function, one should call the [Module] instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Masked language modeling (MLM) loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Examples:
>>> from transformers import AutoTokenizer, EsmcForMaskedLM
>>> import torch
>>> model = EsmcForMaskedLM.from_pretrained("biohub/ESMC-300M")
>>> tokenizer = AutoTokenizer.from_pretrained("biohub/ESMC-300M")
>>> inputs = tokenizer(["MLKNVQ<mask>LV"], return_tensors="pt")
>>> outputs = model(**inputs)
>>> outputs.logits.shape
torch.Size([1, 11, 64])EsmcForSequenceClassification
class transformers.EsmcForSequenceClassification
< source >( config: EsmcConfig )
Parameters
- config (EsmcConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
ESMC Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( input_ids: typing.Optional[torch.LongTensor] = Noneattention_mask: typing.Optional[torch.Tensor] = Noneposition_ids: typing.Optional[torch.LongTensor] = Noneinputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = None**kwargs: Unpack ) → SequenceClassifierOutput or tuple(torch.FloatTensor)
Parameters
- input_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- position_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1]. - inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. - labels (
torch.LongTensorof shape(batch_size,), optional) — Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]. Ifconfig.num_labels == 1a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1a classification loss is computed (Cross-Entropy).
Returns
SequenceClassifierOutput or tuple(torch.FloatTensor)
A SequenceClassifierOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (EsmcConfig) and inputs.
The EsmcForSequenceClassification forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
loss (
torch.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Classification (or regression if config.num_labels==1) loss.logits (
torch.FloatTensorof shape(batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (
tuple(torch.FloatTensor), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.FloatTensor(one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length).Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Example of single-label classification:
>>> import torch
>>> from transformers import AutoTokenizer, EsmcForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("biohub/ESMC-6B")
>>> model = EsmcForSequenceClassification.from_pretrained("biohub/ESMC-6B")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
...
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = EsmcForSequenceClassification.from_pretrained("biohub/ESMC-6B", num_labels=num_labels)
>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
...Example of multi-label classification:
>>> import torch
>>> from transformers import AutoTokenizer, EsmcForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("biohub/ESMC-6B")
>>> model = EsmcForSequenceClassification.from_pretrained("biohub/ESMC-6B", problem_type="multi_label_classification")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = EsmcForSequenceClassification.from_pretrained(
... "biohub/ESMC-6B", num_labels=num_labels, problem_type="multi_label_classification"
... )
>>> labels = torch.sum(
... torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
>>> loss = model(**inputs, labels=labels).lossEsmcForTokenClassification
forward
< source >( input_ids: torch.LongTensor | None = Noneattention_mask: torch.Tensor | None = Noneposition_ids: torch.LongTensor | None = Nonepast_key_values: Cache | None = Noneinputs_embeds: torch.FloatTensor | None = Nonelabels: torch.LongTensor | None = Noneuse_cache: bool | None = None**kwargs: Unpack[TransformersKwargs] ) → TokenClassifierOutput
Parameters
- input_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
- attention_mask (
torch.Tensorof shape(batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:- 1 for tokens that are not masked,
- 0 for tokens that are masked.
- position_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1]. - past_key_values (
Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=Trueorconfig.use_cache=True.Only Cache instance is allowed as input, see our kv cache guide. If no
past_key_valuesare passed, DynamicCache will be initialized by default.The model will output the same cache format that is fed as input.
If
past_key_valuesare used, the user is expected to input only unprocessedinput_ids(those that don’t have their past key value states given to this model) of shape(batch_size, unprocessed_length)instead of allinput_idsof shape(batch_size, sequence_length). - inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passinginput_idsyou can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_idsindices into associated vectors than the model’s internal embedding lookup matrix. - labels (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in[0, ..., config.vocab_size]or -100 (seeinput_idsdocstring). Tokens with indices set to-100are ignored (masked), the loss is only computed for the tokens with labels in[0, ..., config.vocab_size]. - use_cache (
bool, optional) — If set toTrue,past_key_valueskey value states are returned and can be used to speed up decoding (seepast_key_values).
Returns
TokenClassifierOutput
The GenericForTokenClassification forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.