File size: 1,893 Bytes
536bee4 6c43e5c 536bee4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 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 | from torch import nn
from transformers import AutoModel, PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import QuestionAnsweringModelOutput
class CustomQAModelConfig(PretrainedConfig):
model_type = "modernbert"
def __init__(self, base_model_name_or_path="answerdotai/ModernBERT-base", **kwargs):
self.base_model_name_or_path = base_model_name_or_path
super().__init__(**kwargs)
class CustomQAModel(PreTrainedModel):
config_class = CustomQAModelConfig
def __init__(self, config):
super().__init__(config)
self.base = AutoModel.from_pretrained(config.base_model_name_or_path)
hidden_size = self.base.config.hidden_size
self.qa_outputs = nn.Linear(hidden_size, 2)
self.loss_fn = nn.CrossEntropyLoss()
def forward(
self,
input_ids=None,
attention_mask=None,
start_positions=None,
end_positions=None,
):
outputs = self.base(
input_ids=input_ids,
attention_mask=attention_mask,
)
hidden_states = outputs.last_hidden_state
logits = self.qa_outputs(hidden_states)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
loss = None
if start_positions is not None and end_positions is not None:
start_positions = start_positions.clamp(0, start_logits.size(1) - 1)
end_positions = end_positions.clamp(0, end_logits.size(1) - 1)
start_loss = self.loss_fn(start_logits, start_positions)
end_loss = self.loss_fn(end_logits, end_positions)
loss = (start_loss + end_loss) / 2
return QuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
)
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