RoFormerForTokenClassification¶
- class lucid.models.RoFormerForTokenClassification(config: RoFormerConfig, num_labels: int = 2)¶
The RoFormerForTokenClassification class predicts labels for each token from RoFormer hidden states.
Class Signature¶
class RoFormerForTokenClassification(config: RoFormerConfig, num_labels: int = 2)
Parameters¶
config (RoFormerConfig): RoFormer configuration for token-level outputs.
num_labels (int, optional): Number of target classes. Default is 2.
Methods¶
- RoFormerForTokenClassification.forward(input_ids: LongTensor | None = None, attention_mask: Tensor | None = None, token_type_ids: LongTensor | None = None, position_ids: LongTensor | None = None, inputs_embeds: FloatTensor | None = None) Tensor
- RoFormerForTokenClassification.get_loss(labels: Tensor, input_ids: LongTensor | None = None, attention_mask: Tensor | None = None, token_type_ids: LongTensor | None = None, position_ids: LongTensor | None = None, inputs_embeds: FloatTensor | None = None, *, ignore_index: int = -100, reduction: str | None = 'mean') Tensor
- RoFormerForTokenClassification.predict_token_labels(input_ids: LongTensor | None = None, attention_mask: Tensor | None = None, token_type_ids: LongTensor | None = None, position_ids: LongTensor | None = None, inputs_embeds: FloatTensor | None = None) Tensor
- RoFormerForTokenClassification.get_accuracy(labels: Tensor, input_ids: LongTensor | None = None, attention_mask: Tensor | None = None, token_type_ids: LongTensor | None = None, position_ids: LongTensor | None = None, inputs_embeds: FloatTensor | None = None, *, ignore_index: int = -100) Tensor
- RoFormerForTokenClassification.get_loss_from_text(tokenizer: BERTTokenizerFast, text_a: str, text_b: str | None = None, labels: Tensor | None = None, *, device: Literal['cpu', 'gpu'] = 'cpu', ignore_index: int = -100, reduction: str | None = 'mean') Tensor
- RoFormerForTokenClassification.predict_token_labels_from_text(tokenizer: BERTTokenizerFast, text_a: str, text_b: str | None = None, *, device: Literal['cpu', 'gpu'] = 'cpu') Tensor
Examples¶
>>> import lucid.models as models
>>> config = models.RoFormerConfig.base(vocab_size=50000)
>>> model = models.RoFormerForTokenClassification(config, num_labels=5)
>>> print(model)
RoFormerForTokenClassification(...)