Upload BertForSequenceClassification

config.json

@@ -1,7 +1,11 @@
 {
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
   "attention_probs_dropout_prob": 0.1,
   "auto_map": {
-    "AutoConfig": "configuration_bert.BertConfig"
+    "AutoConfig": "configuration_bert.BertConfig",
+    "AutoModelForSequenceClassification": "modeling_bert.BertForSequenceClassification"
   },
   "classifier_dropout": null,
   "hidden_act": "gelu",
@@ -16,6 +20,7 @@
   "num_hidden_layers": 12,
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
   "transformers_version": "4.33.3",
   "type_vocab_size": 2,
   "use_cache": true,

modeling_bert.py

@@ -19,16 +19,12 @@ from transformers.modeling_outputs import (
     SequenceClassifierOutput
 )
 
-from .configuration_bert import BertClsConfig
+from .configuration_bert import BertConfig
 
 
 class BertPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
 
-    config_class = BertClsConfig
+    config_class = BertConfig
     load_tf_weights = load_tf_weights_in_bert
     base_model_prefix = "bert"
     supports_gradient_checkpointing = True
@@ -50,7 +46,7 @@ class BertPreTrainedModel(PreTrainedModel):
             module.weight.data.fill_(1.0)
 
 
-class BertClsPooler(nn.Module):
+class BertPooler(nn.Module):
 
     def __init__(self, config):
         super().__init__()
@@ -68,7 +64,7 @@ class BertClsPooler(nn.Module):
 
 class BertModel(BertPreTrainedModel):
 
-    config_class = BertClsConfig
+    config_class = BertConfig
 
     def __init__(self, config, add_pooling_layer=True):
         super().__init__(config)
@@ -77,7 +73,7 @@ class BertModel(BertPreTrainedModel):
         self.embeddings = BertEmbeddings(config)
         self.encoder = BertEncoder(config)
 
-        self.pooler = BertClsPooler(config) if add_pooling_layer else None
+        self.pooler = BertPooler(config) if add_pooling_layer else None
 
         # Initialize weights and apply final processing
         self.post_init()
@@ -201,7 +197,7 @@ class BertModel(BertPreTrainedModel):
 
 class BertForSequenceClassification(BertPreTrainedModel):
 
-    config_class = BertClsConfig
+    config_class = BertConfig
 
     def __init__(self, config):
         super().__init__(config)
@@ -290,4 +286,3 @@ class BertForSequenceClassification(BertPreTrainedModel):
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
-

pytorch_model.bin

@@ -1,3 +1,3 @@
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-oid sha256:6302c5d80ac329f1276bc9de48a4d43959ed0a4e84b7b97ef722792fe825652f
+oid sha256:601a8071a8e164093f8cbf0ed22b304427f5feff9f93aee5963fc4081a735fe5
 size 438000689