File size: 3,123 Bytes
d9c317f | 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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import torch
import os
import torch.nn as nn
from transformers import RobertaPreTrainedModel, RobertaModel, AutoConfig
from transformers.modeling_outputs import SequenceClassifierOutput
class TransformerForABSA(RobertaPreTrainedModel):
base_model_prefix = "roberta"
def __init__(self, config):
super().__init__(config)
self.roberta = RobertaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# Thêm lớp "none" vào num_sentiments
self.sentiment_classifiers = nn.ModuleList([
nn.Linear(config.hidden_size, config.num_sentiments + 1) # +1 cho "none"
for _ in range(config.num_aspects)
])
self.init_weights()
def forward(
self,
input_ids=None,
attention_mask=None,
labels=None,
return_dict=None
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(input_ids, attention_mask=attention_mask, return_dict=return_dict)
pooled = self.dropout(outputs.pooler_output) # [B, H]
all_logits = torch.stack([cls(pooled) for cls in self.sentiment_classifiers], dim=1) # [B, A, S+1]
loss = None
if labels is not None:
# labels: [B, A], với lớp "none" thay vì -100
B, A, _ = all_logits.size()
logits_flat = all_logits.view(-1, all_logits.size(-1))
targets_flat = labels.view(-1)
loss_fct = nn.CrossEntropyLoss() # Không dùng ignore_index
loss = loss_fct(logits_flat, targets_flat)
if not return_dict:
return ((loss, all_logits) + outputs[2:]) if loss is not None else (all_logits,) + outputs[2:]
return SequenceClassifierOutput(
loss=loss,
logits=all_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def save_pretrained(self, save_directory: str, **kwargs):
"""
HuggingFace Trainer đôi khi truyền vào state_dict=..., nên ta
chấp nhận thêm **kwargs để không vướng lỗi.
"""
# 1) Lưu phần backbone (encoder)
self.roberta.save_pretrained(save_directory, **kwargs)
# 2) Cập nhật config rồi lưu
config = self.roberta.config
config.num_aspects = len(self.sentiment_classifiers)
config.num_sentiments = self.sentiment_classifiers[0].out_features
config.auto_map = {"AutoModel": "models.TransformerForABSA"}
config.save_pretrained(save_directory, **kwargs)
# 3) Lưu toàn bộ state_dict (bao gồm cả 2 head) —
# nếu Trainer đã truyền state_dict trong kwargs, có thể dùng luôn,
# nếu không, lấy từ self.state_dict()
sd = kwargs.get("state_dict", None) or self.state_dict()
torch.save(sd, os.path.join(save_directory, "pytorch_model.bin")) |