check

config.json

@@ -13,5 +13,9 @@
     }
   },
   "repo_id": "Maslionok/pipeline1",
-  "flename": "LID-40-3-2000000-1-4.bin"
+  "flename": "LID-40-3-2000000-1-4.bin",
+  "auto_map": {
+    "AutoConfig": "configuration_stacked.ImpressoConfig",
+    "AutoModelForTokenClassification": "modeling_stacked.ExtendedMultitaskModelForTokenClassification"
+  }
 }

configuration_stacked.py

@@ -2,98 +2,7 @@ from transformers import PretrainedConfig
 import torch
 
 class ImpressoConfig(PretrainedConfig):
-    model_type = "stacked_bert"
-
-    def __init__(
-        self,
-        vocab_size=30522,
-        hidden_size=768,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        intermediate_size=3072,
-        hidden_act="gelu",
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
-        max_position_embeddings=512,
-        type_vocab_size=2,
-        initializer_range=0.02,
-        layer_norm_eps=1e-12,
-        pad_token_id=0,
-        position_embedding_type="absolute",
-        use_cache=True,
-        classifier_dropout=None,
-        pretrained_config=None,
-        values_override=None,
-        label_map=None,
-        **kwargs,
-    ):
-        super().__init__(pad_token_id=pad_token_id, **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.hidden_act = hidden_act
-        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.type_vocab_size = type_vocab_size
-        self.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
-        self.position_embedding_type = position_embedding_type
-        self.use_cache = use_cache
-        self.classifier_dropout = classifier_dropout
-        self.pretrained_config = pretrained_config
-        self.label_map = label_map
-
-        self.values_override = values_override or {}
-        self.outputs = {
-            "logits": {"shape": [None, None, self.hidden_size], "dtype": "float32"}
-        }
-
-    @classmethod
-    def is_torch_support_available(cls):
-        """
-        Indicate whether Torch support is available for this configuration.
-        Required for compatibility with certain parts of the Transformers library.
-        """
-        return True
-
-    @classmethod
-    def patch_ops(self):
-        """
-        A method required by some Hugging Face utilities to modify operator mappings.
-        Currently, it performs no operation and is included for compatibility.
-        Args:
-            ops: A dictionary of operations to potentially patch.
-        Returns:
-            The (unmodified) ops dictionary.
-        """
-        return None
-
-    def generate_dummy_inputs(self, tokenizer, batch_size=1, seq_length=8, framework="pt"):
-        """
-        Generate dummy inputs for testing or export.
-        Args:
-            tokenizer: The tokenizer used to tokenize inputs.
-            batch_size: Number of input samples in the batch.
-            seq_length: Length of each sequence.
-            framework: Framework ("pt" for PyTorch, "tf" for TensorFlow).
-        Returns:
-            Dummy inputs as a dictionary.
-        """
-        if framework == "pt":
-            input_ids = torch.randint(
-                low=0,
-                high=self.vocab_size,
-                size=(batch_size, seq_length),
-                dtype=torch.long
-            )
-            attention_mask = torch.ones((batch_size, seq_length), dtype=torch.long)
-            return {"input_ids": input_ids, "attention_mask": attention_mask}
-        else:
-            raise ValueError("Framework '{}' not supported.".format(framework))
+    model_type = "floret"
 
 # Register the configuration with the transformers library
 ImpressoConfig.register_for_auto_class()