Rename configuration_gpt_bert.py to configuration_ltgbert.py

configuration_gpt_bert.py

@@ -1,54 +0,0 @@
-from __future__ import annotations
-
-import json
-import pathlib
-import copy
-
-from typing import Any
-from transformers.configuration_utils import PretrainedConfig
-
-
-class ModelConfig(PretrainedConfig):
-
-    def __init__(self: ModelConfig, config_file: pathlib.Path | str | None = None, **kwargs):
-        """
-        """
-        super().__init__(**kwargs)
-        if config_file is None:
-            self.attention_probs_dropout_prob: float = 0.1
-            self.hidden_dropout_prob = 0.1
-            self.hidden_size = 384
-            self.intermediate_size = 1280
-            self.max_sequence_length = 128
-            self.position_bucket_size = 32
-            self.num_attention_heads = 6
-            self.num_layers = 12
-            self.vocab_size = 8192
-            self.layer_norm_eps = 1e-7
-        else:
-            if config_file == "str":
-                config_file = pathlib.Path(config_file)
-
-            config: dict[str, Any] = json.load(config_file.open("r"))
-
-            for key, value in config.items():
-                setattr(self, key, value)
-
-    def __repr__(self) -> str:
-        return str(self.to_json_string())
-
-    def to_dict(self) -> dict[str, Any]:
-        """Serializes this instance to a Python dictionary."""
-        output: dict[str, Any] = copy.deepcopy(self.__dict__)
-        return output
-
-    def to_json_string(self) -> str:
-        """Serializes this instance to a JSON string."""
-        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
-
-    def to_json_file(self, json_file_path: pathlib.Path | str) -> None:
-        """Save this instance to a json file."""
-        if isinstance(json_file_path, str):
-            json_file_path: pathlib.Path = pathlib.Path(json_file_path)
-        with json_file_path.open("w", encoding='utf-8') as writer:
-            writer.write(self.to_json_string())

configuration_ltgbert.py

@@ -0,0 +1,36 @@
+from transformers.configuration_utils import PretrainedConfig
+
+
+class LtgbertConfig(PretrainedConfig):
+    """Configuration class to store the configuration of a `LtgbertModel`.
+    """
+    def __init__(
+        self,
+        vocab_size=32768,
+        attention_probs_dropout_prob=0.1,
+        hidden_dropout_prob=0.1,
+        hidden_size=768,
+        intermediate_size=2048,
+        max_position_embeddings=512,
+        position_bucket_size=32,
+        num_attention_heads=12,
+        num_hidden_layers=12,
+        layer_norm_eps=1.0e-7,
+        output_all_encoded_layers=True,
+        temperature=1.0,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.output_all_encoded_layers = output_all_encoded_layers
+        self.position_bucket_size = position_bucket_size
+        self.layer_norm_eps = layer_norm_eps
+        self.temperature = temperature