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config.json

@@ -7,7 +7,7 @@
   "attention_probs_dropout_prob": 0.1,
   "augmentation": "unk",
   "auto_map": {
-    "AutoConfig": "configuration_bionexttager.BioNextTaggerConfig",
+    "AutoConfig": "modeling_bionexttagger.BioNextTaggerConfig",
     "AutoModel": "modeling_bionexttagger.BioNextTaggerModel"
   },
   "classifier_dropout": null,

modeling_bionexttagger.py

@@ -1,7 +1,7 @@
 
 import os
 from typing import Optional, Union
-from transformers import AutoModel, PreTrainedModel, AutoConfig, BertModel
+from transformers import AutoModel, PreTrainedModel, AutoConfig, BertModel, PretrainedConfig
 from transformers.modeling_outputs import  TokenClassifierOutput
 from torch import nn
 from torch.nn import CrossEntropyLoss
@@ -10,8 +10,35 @@ from typing import List, Optional
 
 import torch
 from itertools import islice
-from .configuration_bionexttager import BioNextTaggerConfig
 
+class BioNextTaggerConfig(PretrainedConfig):
+    model_type = "crf-tagger"
+
+    def __init__(
+        self,
+        augmentation = "unk",
+        context_size = 64,
+        percentage_tags = 0.2,
+        p_augmentation = 0.5,
+        crf_reduction = "mean",
+        version="0.1.1",
+        **kwargs,
+    ):
+        self.version = version
+        self.augmentation = augmentation
+        self.context_size = context_size
+        self.percentage_tags = percentage_tags
+        self.p_augmentation = p_augmentation
+        self.crf_reduction = crf_reduction
+        super().__init__(**kwargs)
+        
+    def get_backbonemodel_config(self):
+        backbonemodel_cfg = AutoConfig.from_pretrained(self._name_or_path)#.to_dict()
+        for k in backbonemodel_cfg.to_dict():
+            if hasattr(self, k):
+                setattr(backbonemodel_cfg,k, getattr(self,k))
+                
+        return backbonemodel_cfg
 
 NUM_PER_LAYER = 16