File size: 1,574 Bytes
deb51b4 | 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 | import torch
import torch.nn as nn
from transformers import (
AutoModel,
PreTrainedModel
)
from transformers.modeling_outputs import SequenceClassifierOutput
from .configuration_veritas import BERTClassifierConfig
from transformers import AutoModel, AutoConfig
class BERTClassifier(PreTrainedModel):
config_class = BERTClassifierConfig
def __init__(self, config):
super().__init__(config)
bert_config = AutoConfig.from_pretrained(
"neuralmind/bert-base-portuguese-cased"
)
self.bert = AutoModel.from_config(bert_config)
for param in self.bert.encoder.layer[:6].parameters():
param.requires_grad = False
hidden = self.bert.config.hidden_size
self.classifier = nn.Sequential(
nn.Linear(hidden, 32),
nn.GELU(),
nn.Dropout(config.drop_rate),
nn.Linear(32, 16),
nn.GELU(),
nn.Linear(16, config.num_labels)
)
self.post_init()
def forward(
self,
input_ids=None,
attention_mask=None,
labels=None
):
outputs = self.bert(
input_ids=input_ids,
attention_mask=attention_mask
)
pooler = outputs.last_hidden_state[:, 0, :]
logits = self.classifier(pooler)
loss = None
if labels is not None:
loss_fn = nn.CrossEntropyLoss()
loss = loss_fn(logits, labels)
return SequenceClassifierOutput(
loss=loss,
logits=logits
) |