Upload model

CloneDetectionModel.py

@@ -0,0 +1,96 @@
+"""
+Original work:
+https://github.com/sangHa0411/CloneDetection/blob/main/models/codebert.py#L169
+
+Copyright (c) 2022 Sangha Park(sangha110495), Young Jin Ahn(snoop2head)
+
+All credits to the original authors.
+"""
+import torch.nn as nn
+from transformers import (
+    RobertaPreTrainedModel,
+    RobertaModel,
+)
+from transformers.modeling_outputs import SequenceClassifierOutput
+
+
+class CloneDetectionModel(RobertaPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.roberta = RobertaModel(config, add_pooling_layer=False)
+        self.net = nn.Sequential(
+            nn.Dropout(config.hidden_dropout_prob),
+            nn.Linear(config.hidden_size, config.hidden_size),
+            nn.ReLU(),
+        )
+        self.classifier = nn.Linear(config.hidden_size * 4, config.num_labels)
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=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,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        batch_size, _, hidden_size = hidden_states.shape
+
+        # CLS code1 SEP SEP code2 SEP
+        cls_flag = input_ids == self.config.tokenizer_cls_token_id  # cls token
+        sep_flag = input_ids == self.config.tokenizer_sep_token_id  # sep token
+
+        special_token_states = hidden_states[cls_flag + sep_flag].view(
+            batch_size, -1, hidden_size
+        )  # (batch_size, 4, hidden_size)
+        special_hidden_states = self.net(
+            special_token_states
+        )  # (batch_size, 4, hidden_size)
+
+        pooled_output = special_hidden_states.view(
+            batch_size, -1
+        )  # (batch_size, hidden_size * 4)
+        logits = self.classifier(pooled_output)  # (batch_size, num_labels)
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

config.json

@@ -1,9 +1,12 @@
 {
-  "_name_or_path": "microsoft/graphcodebert-base",
+  "_name_or_path": "./checkpoint",
   "architectures": [
-    "RobertaRBERT"
+    "CloneDetectionModel"
   ],
   "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoModel": "CloneDetectionModel.CloneDetectionModel"
+  },
   "bos_token_id": 0,
   "classifier_dropout": null,
   "dropout_rate": 0.1,

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 498680501
+oid sha256:cd61ea43ac55f9dcb691449f3489fbc90638a96a958289b24c7abf6306642f02
+size 498675949