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config.json

@@ -64,5 +64,6 @@
   "transformers_version": "4.37.2",
   "type_vocab_size": 2,
   "use_cache": true,
+  "version": "0.1.1",
   "vocab_size": 28895
 }

configuration_bionexttager.py

@@ -13,8 +13,10 @@ class BioNextTaggerConfig(PretrainedConfig):
         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

modeling_bionexttagger.py

@@ -1,7 +1,7 @@
 
 import os
 from typing import Optional, Union
-from transformers import AutoModel, PreTrainedModel, AutoConfig
+from transformers import AutoModel, PreTrainedModel, AutoConfig, BertModel
 from transformers.modeling_outputs import  TokenClassifierOutput
 from torch import nn
 from torch.nn import CrossEntropyLoss
@@ -22,7 +22,11 @@ class BioNextTaggerModel(PreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
-        self.bert = AutoModel.from_pretrained(config._name_or_path, config=config.get_backbonemodel_config(), add_pooling_layer=False)
+        #print("LOAD BERT?")
+        self.bert = BertModel(config.get_backbonemodel_config(), add_pooling_layer=False)
+                    #AutoModel.from_pretrained(config._name_or_path, 
+                    #                          config=config.get_backbonemodel_config(), 
+                    #                          add_pooling_layer=False)
         # self.vocab_size = config.vocab_size
         classifier_dropout = (config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob)
         self.dropout = nn.Dropout(config.hidden_dropout_prob)