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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+split.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

all_results.json

@@ -0,0 +1,51 @@
+{
+    "epoch": 10.0,
+    "eval_f1_B-DISEASE": 0.8212511199804513,
+    "eval_f1_B-MEDICATION": 0.9441194996850535,
+    "eval_f1_B-PROCEDURE": 0.8438837081733407,
+    "eval_f1_B-SYMPTOM": 0.8142008132624335,
+    "eval_f1_I-DISEASE": 0.8531332420369243,
+    "eval_f1_I-MEDICATION": 0.9504971609841921,
+    "eval_f1_I-PROCEDURE": 0.855959918699867,
+    "eval_f1_I-SYMPTOM": 0.8203061784176257,
+    "eval_f1_O": 0.9279796337095003,
+    "eval_f1_macro": 0.870147919438821,
+    "eval_f1_micro": 0.8940428457113243,
+    "eval_loss": 0.5246506333351135,
+    "eval_precision_B-DISEASE": 0.8439357160793505,
+    "eval_precision_B-MEDICATION": 0.9548598471059337,
+    "eval_precision_B-PROCEDURE": 0.8662552367223749,
+    "eval_precision_B-SYMPTOM": 0.8270493539504342,
+    "eval_precision_I-DISEASE": 0.8624838159699522,
+    "eval_precision_I-MEDICATION": 0.9581855315489627,
+    "eval_precision_I-PROCEDURE": 0.8779157051309547,
+    "eval_precision_I-SYMPTOM": 0.8292179488587081,
+    "eval_precision_O": 0.9223528464068083,
+    "eval_precision_macro": 0.8824728890859421,
+    "eval_precision_micro": 0.8962622493770654,
+    "eval_rauc_macro": 0.918932582829971,
+    "eval_rauc_micro": 0.9393388823070546,
+    "eval_recall_B-DISEASE": 0.7997541048623781,
+    "eval_recall_B-MEDICATION": 0.9336180815091654,
+    "eval_recall_B-PROCEDURE": 0.8226386036960985,
+    "eval_recall_B-SYMPTOM": 0.8017453798767967,
+    "eval_recall_I-DISEASE": 0.8439832411344849,
+    "eval_recall_I-MEDICATION": 0.942931189506532,
+    "eval_recall_I-PROCEDURE": 0.8350755220446033,
+    "eval_recall_I-SYMPTOM": 0.8115839243498818,
+    "eval_recall_O": 0.9336754945048357,
+    "eval_recall_macro": 0.858333949053864,
+    "eval_recall_micro": 0.8918344066604195,
+    "eval_roc_auc_B-DISEASE": 0.898501130076159,
+    "eval_roc_auc_B-MEDICATION": 0.9667188383374489,
+    "eval_roc_auc_B-PROCEDURE": 0.9102252873491329,
+    "eval_roc_auc_B-SYMPTOM": 0.8990597025985936,
+    "eval_roc_auc_I-DISEASE": 0.9132048321619536,
+    "eval_roc_auc_I-MEDICATION": 0.9711798590609849,
+    "eval_roc_auc_I-PROCEDURE": 0.9118804724182935,
+    "eval_roc_auc_I-SYMPTOM": 0.8927663421914375,
+    "eval_roc_auc_O": 0.9068567812757345,
+    "eval_runtime": 60.8549,
+    "eval_samples_per_second": 178.671,
+    "eval_steps_per_second": 22.348
+}

config.json

@@ -0,0 +1,72 @@
+{
+  "architectures": [
+    "MultiLabelTokenClassificationModelCustom"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_eurobert.EuroBertConfig",
+    "AutoModelForTokenClassification": "modeling.MultiLabelTokenClassificationModelCustom"
+  },
+  "backbone_model_name": "EuroBERT/EuroBERT-610m",
+  "bos_token": "<|begin_of_text|>",
+  "bos_token_id": 128000,
+  "classifier_dropout": 0.1,
+  "classifier_hidden_layers": [
+    768,
+    768,
+    768
+  ],
+  "clf_pooling": "late",
+  "dtype": "float32",
+  "eos_token": "<|end_of_text|>",
+  "eos_token_id": 128001,
+  "freeze_backbone": false,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_dropout": 0.0,
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1152,
+  "id2label": {
+    "0": "O",
+    "1": "B-PROCEDURE",
+    "2": "I-PROCEDURE",
+    "3": "B-MEDICATION",
+    "4": "I-MEDICATION",
+    "5": "B-SYMPTOM",
+    "6": "I-SYMPTOM",
+    "7": "B-DISEASE",
+    "8": "I-DISEASE"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "B-DISEASE": 7,
+    "B-MEDICATION": 3,
+    "B-PROCEDURE": 1,
+    "B-SYMPTOM": 5,
+    "I-DISEASE": 8,
+    "I-MEDICATION": 4,
+    "I-PROCEDURE": 2,
+    "I-SYMPTOM": 6,
+    "O": 0
+  },
+  "mask_token": "<|mask|>",
+  "mask_token_id": 128002,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "eurobert",
+  "num_attention_heads": 18,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 6,
+  "pad_token": "<|end_of_text|>",
+  "pad_token_id": 128001,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 250000,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.0",
+  "use_cache": false,
+  "vocab_size": 128256
+}

configuration_eurobert.py

@@ -0,0 +1,216 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/eurobert/modular_eurobert.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_eurobert.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 Nicolas Boizard, Duarte M. Alves, Hippolyte Gisserot-Boukhlef and the EuroBert team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from transformers.utils import logging
+from transformers.models.llama import LlamaConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class EuroBertConfig(LlamaConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`EuroBertModel`]. It is used to instantiate an EuroBert
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the EuroBERT-210m.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 128256):
+            Vocabulary size of the EuroBert model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`EuroBertModel`]
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the encoder and pooler.
+        max_position_embeddings (`int`, *optional*, defaults to 8192):
+            The maximum sequence length that this model might ever be used with. EuroBert supports up to 8192 tokens,
+            EuroBert-pretrained up to 2048.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        bos_token_id (`int`, *optional*, defaults to 128000):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 128001):
+            End of stream token id.
+        pad_token_id (`int`, *optional*, defaults to 128001):
+            Padding token id.
+        mask_token_id (`int`, *optional*, defaults to 128002):
+            Mask token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
+            understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
+            results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 250000.0):
+            The base period of the RoPE embeddings. EuroBert used base period of 250000.0,
+            EuroBert-pretrained 10000.0.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'eurobert3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'eurobert3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'eurobert3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'eurobert3'. Scaling factor applied to high frequency components of the RoPE
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
+        head_dim (`int`, *optional*):
+            The attention head dimension. If None, it will default to hidden_size // num_attention_heads
+        classifier_pooling (`str`, *optional*, defaults to `"late"`):
+            The pooling strategy to use for the classifier. Can be one of ['bos', 'mean', 'late'].
+
+    ```python
+    >>> from transformers import EuroBertModel, EuroBertConfig
+
+    >>> # Initializing a EuroBert eurobert-base style configuration
+    >>> configuration = EuroBertConfig()
+
+    >>> # Initializing a model from the eurobert-base style configuration
+    >>> model = EuroBertModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "eurobert"
+
+    def __init__(
+        self,
+        vocab_size=128256,
+        hidden_size=768,
+        intermediate_size=3072,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=8192,
+        initializer_range=0.02,
+        rms_norm_eps=1e-05,
+        bos_token_id=128000,
+        eos_token_id=128001,
+        pad_token_id=128001,
+        mask_token_id=128002,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=250000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        head_dim=None,
+        classifier_pooling="late",
+        **kwargs,
+    ):
+        # use_cache is specific to decoder models and should be set to False for encoder models
+        use_cache = kwargs.pop("use_cache", None)
+        if use_cache:
+            logger.warning_once(
+                "The `use_cache` argument to EuroBertConfig is set to `False`, as caching is never used for encoder models."
+            )
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        super().__init__(
+            vocab_size=vocab_size,
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            num_hidden_layers=num_hidden_layers,
+            num_attention_heads=num_attention_heads,
+            num_key_value_heads=num_key_value_heads,
+            hidden_act=hidden_act,
+            max_position_embeddings=max_position_embeddings,
+            initializer_range=initializer_range,
+            rms_norm_eps=rms_norm_eps,
+            use_cache=False,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            pretraining_tp=pretraining_tp,
+            tie_word_embeddings=tie_word_embeddings,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            attention_bias=attention_bias,
+            attention_dropout=attention_dropout,
+            mlp_bias=mlp_bias,
+            head_dim=head_dim,
+            **kwargs,
+        )
+        self.mask_token_id = mask_token_id
+        self.clf_pooling = classifier_pooling
+
+
+__all__ = ["EuroBertConfig"]

eval_results.json

@@ -0,0 +1,51 @@
+{
+    "epoch": 10.0,
+    "eval_f1_B-DISEASE": 0.8212511199804513,
+    "eval_f1_B-MEDICATION": 0.9441194996850535,
+    "eval_f1_B-PROCEDURE": 0.8438837081733407,
+    "eval_f1_B-SYMPTOM": 0.8142008132624335,
+    "eval_f1_I-DISEASE": 0.8531332420369243,
+    "eval_f1_I-MEDICATION": 0.9504971609841921,
+    "eval_f1_I-PROCEDURE": 0.855959918699867,
+    "eval_f1_I-SYMPTOM": 0.8203061784176257,
+    "eval_f1_O": 0.9279796337095003,
+    "eval_f1_macro": 0.870147919438821,
+    "eval_f1_micro": 0.8940428457113243,
+    "eval_loss": 0.5246506333351135,
+    "eval_precision_B-DISEASE": 0.8439357160793505,
+    "eval_precision_B-MEDICATION": 0.9548598471059337,
+    "eval_precision_B-PROCEDURE": 0.8662552367223749,
+    "eval_precision_B-SYMPTOM": 0.8270493539504342,
+    "eval_precision_I-DISEASE": 0.8624838159699522,
+    "eval_precision_I-MEDICATION": 0.9581855315489627,
+    "eval_precision_I-PROCEDURE": 0.8779157051309547,
+    "eval_precision_I-SYMPTOM": 0.8292179488587081,
+    "eval_precision_O": 0.9223528464068083,
+    "eval_precision_macro": 0.8824728890859421,
+    "eval_precision_micro": 0.8962622493770654,
+    "eval_rauc_macro": 0.918932582829971,
+    "eval_rauc_micro": 0.9393388823070546,
+    "eval_recall_B-DISEASE": 0.7997541048623781,
+    "eval_recall_B-MEDICATION": 0.9336180815091654,
+    "eval_recall_B-PROCEDURE": 0.8226386036960985,
+    "eval_recall_B-SYMPTOM": 0.8017453798767967,
+    "eval_recall_I-DISEASE": 0.8439832411344849,
+    "eval_recall_I-MEDICATION": 0.942931189506532,
+    "eval_recall_I-PROCEDURE": 0.8350755220446033,
+    "eval_recall_I-SYMPTOM": 0.8115839243498818,
+    "eval_recall_O": 0.9336754945048357,
+    "eval_recall_macro": 0.858333949053864,
+    "eval_recall_micro": 0.8918344066604195,
+    "eval_roc_auc_B-DISEASE": 0.898501130076159,
+    "eval_roc_auc_B-MEDICATION": 0.9667188383374489,
+    "eval_roc_auc_B-PROCEDURE": 0.9102252873491329,
+    "eval_roc_auc_B-SYMPTOM": 0.8990597025985936,
+    "eval_roc_auc_I-DISEASE": 0.9132048321619536,
+    "eval_roc_auc_I-MEDICATION": 0.9711798590609849,
+    "eval_roc_auc_I-PROCEDURE": 0.9118804724182935,
+    "eval_roc_auc_I-SYMPTOM": 0.8927663421914375,
+    "eval_roc_auc_O": 0.9068567812757345,
+    "eval_runtime": 60.8549,
+    "eval_samples_per_second": 178.671,
+    "eval_steps_per_second": 22.348
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19d4ed8e14f6ea59020188e10c8747567b4eb6d2b5d0330fbc440948f788c717
+size 2439821636

modeling.py

@@ -0,0 +1,447 @@
+import inspect
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from transformers import (
+    AutoConfig,
+    AutoModel,
+    DebertaV2Config,
+    PreTrainedModel,
+    RobertaConfig,
+)
+
+try:
+    from transformers import EuroBertModel
+except ImportError:
+    EuroBertModel = None
+from transformers.modeling_outputs import TokenClassifierOutput
+
+
+class MultiLabelTokenClassificationModelCustom(PreTrainedModel):
+    """
+    Custom multi-label token classification model with configurable classifier head.
+    This model can be loaded with trust_remote_code=True for HuggingFace Hub compatibility.
+    """
+
+    # Supports BERT, RoBERTa, and DeBERTa(V2) models
+
+    # config_class = RobertaConfig
+
+    def __init__(
+        self,
+        config,
+        base_model=None,
+        freeze_backbone=False,
+        classifier_hidden_layers=None,
+        classifier_dropout=0.1,
+    ):
+        super().__init__(config)
+        self.__class__.config_class = config.__class__
+        self.config = config
+        self.num_labels = config.num_labels
+
+        # READ FROM CONFIG (critical)
+        freeze_backbone = getattr(config, "freeze_backbone", False)
+        classifier_hidden_layers = getattr(config, "classifier_hidden_layers", None)
+        classifier_dropout = getattr(config, "classifier_dropout", 0.1)
+
+        # If base_model is not provided, load it from config
+        # modeling.py
+        if base_model is None:
+            # IMPORTANT: Use backbone_model_name (the original pretrained model) for loading,
+            # NOT name_or_path which gets updated to the checkpoint path during saving/loading.
+            backbone_name = getattr(config, "backbone_model_name", None)
+            if backbone_name is None:
+                # Fallback to name_or_path for backwards compatibility
+                backbone_name = getattr(config, "_name_or_path", None)
+            if backbone_name is None:
+                raise ValueError(
+                    "config.backbone_model_name (or config.name_or_path) is required to load pretrained backbone"
+                )
+
+            # Create a clean config for the backbone (without custom attributes that might confuse AutoModel)
+            backbone_config = AutoConfig.from_pretrained(
+                backbone_name, trust_remote_code=True
+            )
+            # Copy over relevant attributes from our config
+            backbone_config.hidden_dropout_prob = getattr(
+                config, "hidden_dropout_prob", 0.1
+            )
+            if "eurobert" in backbone_name.lower() and EuroBertModel is not None:
+                self.backbone = EuroBertModel(backbone_config)
+            else:
+                self.backbone = AutoModel.from_config(
+                    backbone_config,
+                    trust_remote_code=True,
+                )
+        else:
+            self.backbone = base_model
+
+        # Store the backbone model name for future loading
+        if (
+            not hasattr(config, "backbone_model_name")
+            or config.backbone_model_name is None
+        ):
+            # If we got here with a base_model, try to get its name
+            if base_model is not None and hasattr(base_model.config, "_name_or_path"):
+                config.backbone_model_name = base_model.config._name_or_path
+            elif hasattr(config, "_name_or_path"):
+                config.backbone_model_name = config._name_or_path
+
+        # Access custom attributes correctly
+        self.lm_output_size = self.backbone.config.hidden_size
+        # Store configuration for saving/loading
+        self.config.freeze_backbone = freeze_backbone
+        self.config.classifier_hidden_layers = classifier_hidden_layers
+        self.config.classifier_dropout = classifier_dropout
+
+        if freeze_backbone:
+            # print("+" * 30, "\n\n", "Freezing backbone...", "+" * 30, "\n\n")
+            for param in self.backbone.parameters():
+                param.requires_grad = False
+            self.backbone.eval()
+        else:
+            self.backbone.train(True)
+
+        self.dropout = nn.Dropout(
+            config.hidden_dropout_prob
+            if hasattr(config, "hidden_dropout_prob")
+            else 0.1
+        )
+        self._build_classifier_head(classifier_hidden_layers, classifier_dropout)
+        self.post_init()
+
+    @classmethod
+    def from_config(cls, config):
+        return cls(
+            config=config,
+            freeze_backbone=getattr(config, "freeze_backbone", False),
+            classifier_hidden_layers=getattr(config, "classifier_hidden_layers", None),
+            classifier_dropout=getattr(config, "classifier_dropout", 0.1),
+        )
+
+    def _set_backbone_model_name(self, name: str):
+        """Explicitly set the backbone model name in config (call before saving)."""
+        self.config.backbone_model_name = name
+
+    def _build_classifier_head(self, hidden_layers, dropout_rate):
+        """
+        Build a flexible classifier head with configurable hidden layers and dropout.
+
+        Args:
+            hidden_layers: Tuple of integers representing the number of neurons in each hidden layer.
+                          None or empty tuple means a simple linear layer.
+            dropout_rate: Dropout probability between layers
+        """
+        layers = []
+        input_size = self.lm_output_size
+
+        # If hidden_layers is None or empty, just create a simple linear layer
+        if not hidden_layers:
+            self.classifier = nn.Sequential(
+                nn.Dropout(dropout_rate), nn.Linear(input_size, self.num_labels)
+            )
+            return
+
+        # Build MLP with specified hidden layers
+        for hidden_size in hidden_layers:
+            layers.append(nn.Linear(input_size, hidden_size))
+            layers.append(nn.ReLU())
+            layers.append(nn.Dropout(dropout_rate))
+            input_size = hidden_size
+
+        # Final classification layer
+        layers.append(nn.Linear(input_size, self.num_labels))
+
+        # Create sequential model
+        self.classifier = nn.Sequential(*layers)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        forward_kwargs = {
+            "attention_mask": attention_mask,
+            "token_type_ids": token_type_ids,
+            "position_ids": position_ids,
+            "inputs_embeds": inputs_embeds,
+            "output_attentions": output_attentions,
+            "output_hidden_states": output_hidden_states,
+            "return_dict": return_dict,
+            "head_mask": head_mask,
+        }
+
+        # Remove head_mask for DeBERTa models as they don't support it
+        # Filter by the backbone's actual signature and skip Nones
+        sig = inspect.signature(
+            getattr(self.backbone, "forward", self.backbone.__call__)
+        )
+        allowed = sig.parameters.keys()
+        forward_kwargs = {
+            k: v for k, v in forward_kwargs.items() if k in allowed and v is not None
+        }
+
+        outputs = self.backbone(input_ids, **forward_kwargs)
+
+        sequence_output = outputs[0]
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            # Compute multi-label loss
+            loss_fct = nn.BCEWithLogitsLoss(reduction="mean")
+            # Create mask for valid labels (not -100)
+            mask = (labels != -100).float()
+            # Replace -100 with 0 for loss computation
+            labels_masked = torch.where(
+                labels == -100, torch.zeros_like(labels), labels
+            )
+            # Compute loss only on valid positions
+            loss_tensor = loss_fct(logits, labels_masked.float())
+            loss = (loss_tensor * mask).sum() / mask.sum()
+
+            # loss_fct = nn.BCEWithLogitsLoss(reduction="none")
+            # # labels: [B, T, C] with -100 for masked positions
+            # valid = (labels != -100).float()
+            # labels = torch.where(labels == -100, torch.zeros_like(labels), labels).float()
+            # per_elem = loss_fct(logits, labels)          # [B, T, C]
+            # loss = (per_elem * valid).sum() / valid.sum().clamp_min(1.0)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    # @classmethod
+    # def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+    #     config = kwargs.pop('config', None)
+    #     if config is None:
+    #         config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+
+    #     # Build instance from config (no weights)
+    #     _ = cls(
+    #         config=config,
+    #         freeze_backbone=getattr(config, 'freeze_backbone', False),
+    #         classifier_hidden_layers=getattr(config, 'classifier_hidden_layers', None),
+    #         classifier_dropout=getattr(config, 'classifier_dropout', 0.1),
+    #     )
+
+    #     # Now let HF load the actual weights (safetensors/shards/Hub/local, etc.)
+    #     return super(MultiLabelTokenClassificationModelCustom, cls).from_pretrained(
+    #         pretrained_model_name_or_path,
+    #         *model_args,
+    #         config=config,
+    #         **kwargs
+    #     )
+
+    # @classmethod
+    # def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+    #     """Override from_pretrained to handle custom model loading"""
+    #     config = kwargs.pop('config', None)
+    #     if config is None:
+    #         from transformers import AutoConfig
+    #         config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+
+    #     # Extract custom parameters from config if they exist
+    #     freeze_backbone = getattr(config, 'freeze_backbone', False)
+    #     classifier_hidden_layers = getattr(config, 'classifier_hidden_layers', None)
+    #     classifier_dropout = getattr(config, 'classifier_dropout', 0.1)
+
+    #     model = cls(
+    #         config=config,
+    #         freeze_backbone=freeze_backbone,
+    #         classifier_hidden_layers=classifier_hidden_layers,
+    #         classifier_dropout=classifier_dropout
+    #     )
+
+    #     # Load state dict if available
+    #     try:
+    #         state_dict = torch.load(
+    #             f"{pretrained_model_name_or_path}/model.safetensors",
+    #             map_location="cpu"
+    #         )
+    #         model.load_state_dict(state_dict)
+    #     except Exception as e:
+    #         # If loading fails, the model will be initialized with random weights
+    #         print(f"Warning: Could not load pre-trained weights. Using randomly initialized model: {e}")
+
+    #     return model
+
+
+def load_custom_cardioner_model(model_path: str, device: str = "auto"):
+    """
+    Utility function to easily load a custom CardioNER model.
+
+    Args:
+        model_path: Path to the saved model directory
+        device: Device to load model on ("auto", "cpu", "cuda", etc.)
+
+    Returns:
+        tuple: (model, tokenizer, config)
+    """
+    # Validate model directory
+    import os
+
+    import torch
+    from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+    required_files = ["config.json", "modeling.py", "pytorch_model.bin"]
+    missing_files = [
+        f for f in required_files if not os.path.exists(os.path.join(model_path, f))
+    ]
+
+    if missing_files:
+        raise FileNotFoundError(
+            f"Missing required files in {model_path}: {missing_files}"
+        )
+
+    print(f"Loading custom CardioNER model from: {model_path}")
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+    # Load model with trust_remote_code=True
+    model = AutoModelForTokenClassification.from_pretrained(
+        model_path, trust_remote_code=True, use_safetensors=True
+    )
+
+    # Set device
+    if device == "auto":
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    model = model.to(device)
+
+    print(f"Model loaded successfully on {device}")
+    print(f"Model type: {type(model).__name__}")
+    print(f"Number of labels: {model.num_labels}")
+
+    return model, tokenizer, model.config
+
+
+def validate_custom_model_directory(model_path: str) -> dict:
+    """
+    Validate that a model directory contains all necessary files for custom model loading.
+
+    Args:
+        model_path: Path to the model directory
+
+    Returns:
+        dict: Validation results with status and details
+    """
+    import json
+    import os
+
+    validation_results = {
+        "valid": True,
+        "errors": [],
+        "warnings": [],
+        "files_found": [],
+        "model_info": {},
+    }
+
+    # Required files
+    required_files = {
+        "config.json": "Model configuration",
+        "modeling.py": "Custom model class definition",
+        "pytorch_model.bin": "Model weights",
+    }
+
+    # Optional files
+    optional_files = {
+        "tokenizer.json": "Tokenizer vocabulary",
+        "tokenizer_config.json": "Tokenizer configuration",
+        "training_args.json": "Training arguments",
+    }
+
+    # Check required files
+    for filename, description in required_files.items():
+        filepath = os.path.join(model_path, filename)
+        if os.path.exists(filepath):
+            validation_results["files_found"].append(f"{filename} ({description})")
+        else:
+            validation_results["valid"] = False
+            validation_results["errors"].append(
+                f"Missing required file: {filename} - {description}"
+            )
+
+    # Check optional files
+    for filename, description in optional_files.items():
+        filepath = os.path.join(model_path, filename)
+        if os.path.exists(filepath):
+            validation_results["files_found"].append(f"{filename} ({description})")
+        else:
+            validation_results["warnings"].append(
+                f"Missing optional file: {filename} - {description}"
+            )
+
+    # Parse config if available
+    config_path = os.path.join(model_path, "config.json")
+    if os.path.exists(config_path):
+        try:
+            with open(config_path, "r") as f:
+                config = json.load(f)
+
+            validation_results["model_info"]["num_labels"] = config.get(
+                "num_labels", "Unknown"
+            )
+            validation_results["model_info"]["model_type"] = config.get(
+                "model_type", "Unknown"
+            )
+            validation_results["model_info"]["has_auto_map"] = "auto_map" in config
+            validation_results["model_info"]["classifier_hidden_layers"] = config.get(
+                "classifier_hidden_layers", None
+            )
+            validation_results["model_info"]["freeze_backbone"] = config.get(
+                "freeze_backbone", None
+            )
+
+            if not config.get("auto_map"):
+                validation_results["warnings"].append(
+                    "No auto_map found in config - may not load correctly with trust_remote_code=True"
+                )
+
+        except json.JSONDecodeError as e:
+            validation_results["valid"] = False
+            validation_results["errors"].append(f"Invalid config.json: {str(e)}")
+
+    # Check modeling.py content
+    modeling_path = os.path.join(model_path, "modeling.py")
+    if os.path.exists(modeling_path):
+        try:
+            with open(modeling_path, "r") as f:
+                content = f.read()
+
+            if "MultiLabelTokenClassificationModelCustom" not in content:
+                validation_results["valid"] = False
+                validation_results["errors"].append(
+                    "modeling.py does not contain MultiLabelTokenClassificationModelCustom class"
+                )
+
+        except Exception as e:
+            validation_results["warnings"].append(
+                f"Could not read modeling.py: {str(e)}"
+            )
+
+    return validation_results

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<|mask|>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

split.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5ab6c697f66017f778fa908249fcb8e163c4196ec3e9eddf70afdc3b0982622d
+size 41843165

tokenizer.json

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+oid sha256:98d4a1d32152d6cedf85b5e88f3b205106dca1fe72aaab34e0ac13c238421069
+size 17210235

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