Upload energybert/hf_configuration.py with huggingface_hub

energybert/hf_configuration.py

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+from transformers import PretrainedConfig
+
+
+class BertEnergyConfig(PretrainedConfig):
+    model_type = "bert_energy"
+
+    def __init__(
+        self,
+        path: str | None = None,
+        alpha: float = 1.0,
+        beta: float | None = None,
+        vocab_size: int = 30000,
+        hidden_size: int = 768,
+        embedding_dim: int | None = None,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 12,
+        intermediate_size: int | None = None,
+        activation: str = "relu",
+        positional: bool = True,
+        share_layers: bool = False,
+        layer_norm_eps: float = 1e-12,
+        initializer_range: float = 0.02,
+        initializer_hopfield_range: float = 0.002,
+        hidden_dropout_prob: float = 0.1,
+        attention_probs_dropout_prob: float = 0.1,
+        max_position_embeddings: int = 512,
+        tie_word_embeddings: bool = True,
+        bias: bool = True,
+        compile: bool = False,
+        pad_token_id: int | None = None,
+        problem_type: str | None = None,
+        **kwargs,
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+        self.path = path
+
+        # Energy-specific parameters
+        self.alpha = alpha
+        self.beta = beta
+
+        # Vocabulary / dimensions
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.embedding_dim = embedding_dim if embedding_dim is not None else hidden_size
+
+        # Transformer architecture
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = (
+            intermediate_size if intermediate_size is not None else hidden_size * 4
+        )
+        self.activation = activation
+        self.positional = positional
+        self.share_layers = share_layers
+        self.tie_word_embeddings = tie_word_embeddings
+        self.bias = bias
+
+        # Regularization / initialization
+        self.layer_norm_eps = layer_norm_eps
+        self.initializer_range = initializer_range
+        self.initializer_hopfield_range = initializer_hopfield_range
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+
+        # Sequence length
+        self.max_position_embeddings = max_position_embeddings
+
+        # Misc
+        self.compile = compile
+        self.problem_type = problem_type
+
+        # ---- Validation ----
+        if self.embedding_dim % self.num_attention_heads != 0:
+            raise ValueError("embedding_dim must be divisible by num_attention_heads")
+
+        if self.hidden_size <= 0:
+            raise ValueError("hidden_size must be > 0")
+
+        if self.embedding_dim <= 0:
+            raise ValueError("embedding_dim must be > 0")
+
+        if self.num_hidden_layers <= 0:
+            raise ValueError("num_hidden_layers must be > 0")
+
+        if self.num_attention_heads <= 0:
+            raise ValueError("num_attention_heads must be > 0")
+
+        if self.max_position_embeddings <= 0:
+            raise ValueError("max_position_embeddings must be > 0")
+
+        if self.activation not in ["relu", "gelu", "softmax"]:
+            raise ValueError("activation must be one of: relu, gelu, softmax")