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unitorch.cli.models.xlm_roberta¤

XLMRobertaProcessor¤

Tip

core/process/xlm_roberta is the section for configuration of XLMRobertaProcessor.

Bases: XLMRobertaProcessor

XLM-RoBERTa Processor for handling text processing tasks.

Source code in src/unitorch/cli/models/xlm_roberta/processing.py 
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def __init__(
    self,
    vocab_path: str,
    max_seq_length: Optional[int] = 128,
    source_type_id: Optional[int] = 0,
    target_type_id: Optional[int] = 0,
):
    super().__init__(
        vocab_path=vocab_path,
        max_seq_length=max_seq_length,
        source_type_id=source_type_id,
        target_type_id=target_type_id,
    )

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/models/xlm_roberta/processing.py 
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@classmethod
@config_defaults_init("core/process/xlm_roberta")
def from_config(cls, config, **kwargs):
    config.set_default_section("core/process/xlm_roberta")
    pretrained_name = config.getoption("pretrained_name", "xlm-roberta-base")
    vocab_path = config.getoption("vocab_path", None)
    vocab_path = pop_value(
        vocab_path,
        nested_dict_value(pretrained_xlm_roberta_infos, pretrained_name, "vocab"),
    )
    vocab_path = cached_path(vocab_path)

    return {
        "vocab_path": vocab_path,
    }

_classification ¤

_classification(
    text: str,
    text_pair: Optional[str] = None,
    max_seq_length: Optional[int] = None,
)
Source code in src/unitorch/cli/models/xlm_roberta/processing.py 
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@register_process("core/process/xlm_roberta/classification")
def _classification(
    self,
    text: str,
    text_pair: Optional[str] = None,
    max_seq_length: Optional[int] = None,
):
    outputs = super().classification(
        text=text,
        text_pair=text_pair,
        max_seq_length=max_seq_length,
    )
    return TensorInputs(
        input_ids=outputs.input_ids,
        attention_mask=outputs.attention_mask,
        token_type_ids=outputs.token_type_ids,
        position_ids=outputs.position_ids,
    )

XLMRobertaForClassification¤

Tip

core/model/classification/xlm_roberta is the section for configuration of XLMRobertaForClassification.

Bases: XLMRobertaForClassification

XLM-RoBERTa model for classification tasks.

Source code in src/unitorch/cli/models/xlm_roberta/modeling.py 
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def __init__(
    self,
    config_path: str,
    num_classes: Optional[int] = 1,
    gradient_checkpointing: Optional[bool] = False,
):
    super().__init__(
        config_path=config_path,
        num_classes=num_classes,
        gradient_checkpointing=gradient_checkpointing,
    )

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/models/xlm_roberta/modeling.py 
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@classmethod
@config_defaults_init("core/model/classification/xlm_roberta")
def from_config(cls, config, **kwargs):
    config.set_default_section("core/model/classification/xlm_roberta")
    pretrained_name = config.getoption("pretrained_name", "xlm-roberta-base")
    config_path = config.getoption("config_path", None)
    num_classes = config.getoption("num_classes", 1)

    config_path = pop_value(
        config_path,
        nested_dict_value(pretrained_xlm_roberta_infos, pretrained_name, "config"),
    )

    config_path = cached_path(config_path)
    gradient_checkpointing = config.getoption("gradient_checkpointing", False)

    inst = cls(config_path, num_classes, gradient_checkpointing)
    pretrained_weight_path = config.getoption("pretrained_weight_path", None)
    weight_path = pop_value(
        pretrained_weight_path,
        nested_dict_value(pretrained_xlm_roberta_infos, pretrained_name, "weight"),
        check_none=False,
    )
    if weight_path is not None:
        inst.from_pretrained(weight_path)

    return inst

forward ¤

forward(
    input_ids: Tensor,
    attention_mask: Optional[Tensor] = None,
    token_type_ids: Optional[Tensor] = None,
    position_ids: Optional[Tensor] = None,
)
Source code in src/unitorch/cli/models/xlm_roberta/modeling.py 
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@autocast(device_type=("cuda" if torch.cuda.is_available() else "cpu"))
def forward(
    self,
    input_ids: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    token_type_ids: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None,
):
    outputs = super().forward(
        input_ids=input_ids,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
    )
    return ClassificationOutputs(outputs=outputs)

XLMRobertaXLForClassification¤

Tip

core/model/classification/xlm_roberta_xl is the section for configuration of XLMRobertaXLForClassification.

Bases: XLMRobertaXLForClassification

XLM-RoBERTa XL model for classification tasks.

Source code in src/unitorch/cli/models/xlm_roberta/modeling.py 
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def __init__(
    self,
    config_path: str,
    num_classes: Optional[int] = 1,
    gradient_checkpointing: Optional[bool] = False,
):
    super().__init__(
        config_path=config_path,
        num_classes=num_classes,
        gradient_checkpointing=gradient_checkpointing,
    )

from_config classmethod ¤

from_config(config, **kwargs)
Source code in src/unitorch/cli/models/xlm_roberta/modeling.py 
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@classmethod
@config_defaults_init("core/model/classification/xlm_roberta_xl")
def from_config(cls, config, **kwargs):
    config.set_default_section("core/model/classification/xlm_roberta_xl")
    pretrained_name = config.getoption("pretrained_name", "xlm-roberta-xl")
    config_path = config.getoption("config_path", None)

    config_path = pop_value(
        config_path,
        nested_dict_value(pretrained_xlm_roberta_infos, pretrained_name, "config"),
    )

    config_path = cached_path(config_path)
    gradient_checkpointing = config.getoption("gradient_checkpointing", False)
    num_classes = config.getoption("num_classes", 1)

    inst = cls(config_path, num_classes, gradient_checkpointing)
    pretrained_weight_path = config.getoption("pretrained_weight_path", None)
    weight_path = pop_value(
        pretrained_weight_path,
        nested_dict_value(pretrained_xlm_roberta_infos, pretrained_name, "weight"),
        check_none=False,
    )
    if weight_path is not None:
        inst.from_pretrained(weight_path)

    return inst

forward ¤

forward(
    input_ids: Tensor,
    attention_mask: Optional[Tensor] = None,
    token_type_ids: Optional[Tensor] = None,
    position_ids: Optional[Tensor] = None,
)
Source code in src/unitorch/cli/models/xlm_roberta/modeling.py 
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@autocast(device_type=("cuda" if torch.cuda.is_available() else "cpu"))
def forward(
    self,
    input_ids: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    token_type_ids: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = None,
):
    outputs = super().forward(
        input_ids=input_ids,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
    )
    return ClassificationOutputs(outputs=outputs)