Skip to content

modeling_outputs

multimolecule.models.modeling_outputs

ContactPredictorOutput dataclass

Bases: ModelOutput

Base class for outputs of contact classification & regression models.

Parameters:

Name Type Description Default
loss FloatTensor | None

torch.FloatTensor of shape (1,).

Optional, returned when labels is provided

 None
logits FloatTensor

torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)

Prediction outputs.

 None
hidden_states Tuple[FloatTensor, ...] | None

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).

Optional, returned when output_hidden_states=True is passed or when `config.output_hidden_states=True

Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

 None
attentions Tuple[FloatTensor, ...] | None

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Optional, eturned when output_attentions=True is passed or when config.output_attentions=True

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

 None
Source code in multimolecule/models/modeling_outputs.py
Python
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
@dataclass
class ContactPredictorOutput(ModelOutput):
    """
    Base class for outputs of contact classification & regression models.

    Args:
        loss:
            `torch.FloatTensor` of shape `(1,)`.

            Optional, returned when `labels` is provided
        logits:
            `torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`

            Prediction outputs.
        hidden_states:
            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)`.

            Optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions:
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Optional, eturned when `output_attentions=True` is passed or when `config.output_attentions=True`

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    loss: torch.FloatTensor | None = None
    logits: torch.FloatTensor = None
    hidden_states: Tuple[torch.FloatTensor, ...] | None = None
    attentions: Tuple[torch.FloatTensor, ...] | None = None

NucleotidePredictorOutput dataclass

Bases: ModelOutput

Base class for outputs of nucleotide classification & regression models.

Parameters:

Name Type Description Default
loss FloatTensor | None

torch.FloatTensor of shape (1,).

Optional, returned when labels is provided

 None
logits FloatTensor

torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)

Prediction outputs.

 None
hidden_states Tuple[FloatTensor, ...] | None

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).

Optional, returned when output_hidden_states=True is passed or when `config.output_hidden_states=True

Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

 None
attentions Tuple[FloatTensor, ...] | None

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Optional, eturned when output_attentions=True is passed or when config.output_attentions=True

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

 None
Source code in multimolecule/models/modeling_outputs.py
Python
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
@dataclass
class NucleotidePredictorOutput(ModelOutput):
    """
    Base class for outputs of nucleotide classification & regression models.

    Args:
        loss:
            `torch.FloatTensor` of shape `(1,)`.

            Optional, returned when `labels` is provided
        logits:
            `torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`

            Prediction outputs.
        hidden_states:
            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)`.

            Optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions:
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Optional, eturned when `output_attentions=True` is passed or when `config.output_attentions=True`

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    loss: torch.FloatTensor | None = None
    logits: torch.FloatTensor = None
    hidden_states: Tuple[torch.FloatTensor, ...] | None = None
    attentions: Tuple[torch.FloatTensor, ...] | None = None

SequencePredictorOutput dataclass

Bases: ModelOutput

Base class for outputs of sentence classification & regression models.

Parameters:

Name Type Description Default
loss FloatTensor | None

torch.FloatTensor of shape (1,).

Optional, returned when labels is provided

 None
logits FloatTensor

torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)

Prediction outputs.

 None
hidden_states Tuple[FloatTensor, ...] | None

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).

Optional, returned when output_hidden_states=True is passed or when `config.output_hidden_states=True

Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

 None
attentions Tuple[FloatTensor, ...] | None

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Optional, eturned when output_attentions=True is passed or when config.output_attentions=True

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

 None
Source code in multimolecule/models/modeling_outputs.py
Python
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
@dataclass
class SequencePredictorOutput(ModelOutput):
    """
    Base class for outputs of sentence classification & regression models.

    Args:
        loss:
            `torch.FloatTensor` of shape `(1,)`.

            Optional, returned when `labels` is provided
        logits:
            `torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`

            Prediction outputs.
        hidden_states:
            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)`.

            Optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions:
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Optional, eturned when `output_attentions=True` is passed or when `config.output_attentions=True`

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    loss: torch.FloatTensor | None = None
    logits: torch.FloatTensor = None
    hidden_states: Tuple[torch.FloatTensor, ...] | None = None
    attentions: Tuple[torch.FloatTensor, ...] | None = None

TokenPredictorOutput dataclass

Bases: ModelOutput

Base class for outputs of token classification & regression models.

Parameters:

Name Type Description Default
loss FloatTensor | None

torch.FloatTensor of shape (1,).

Optional, returned when labels is provided

 None
logits FloatTensor

torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)

Prediction outputs.

 None
hidden_states Tuple[FloatTensor, ...] | None

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).

Optional, returned when output_hidden_states=True is passed or when `config.output_hidden_states=True

Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

 None
attentions Tuple[FloatTensor, ...] | None

Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Optional, eturned when output_attentions=True is passed or when config.output_attentions=True

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

 None
Source code in multimolecule/models/modeling_outputs.py
Python
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
@dataclass
class TokenPredictorOutput(ModelOutput):
    """
    Base class for outputs of token classification & regression models.

    Args:
        loss:
            `torch.FloatTensor` of shape `(1,)`.

            Optional, returned when `labels` is provided
        logits:
            `torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`

            Prediction outputs.
        hidden_states:
            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)`.

            Optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions:
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Optional, eturned when `output_attentions=True` is passed or when `config.output_attentions=True`

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    loss: torch.FloatTensor | None = None
    logits: torch.FloatTensor = None
    hidden_states: Tuple[torch.FloatTensor, ...] | None = None
    attentions: Tuple[torch.FloatTensor, ...] | None = None