Upload 3 files

config.json

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+{
+  "architectures": [
+    "CAPModel"
+  ],
+  "base_model_name": "GroNLP/hateBERT",
+  "dropout": 0.1,
+  "dtype": "float32",
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "model_type": "cap",
+  "transformers_version": "5.14.1"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bca3a92057ad1958f6f42d300e9760b4f429253cd30dc7b402ba532179341a43
+size 437962508

modeling_cap.py

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+import torch
+import torch.nn as nn
+from transformers import AutoModel, AutoConfig, PreTrainedModel, PretrainedConfig
+
+
+class CAPConfig(PretrainedConfig):
+    model_type = "cap"
+
+    def __init__(self, base_model_name="roberta-base", num_labels=3, dropout=0.1, **kwargs):
+        super().__init__(**kwargs)
+        self.base_model_name = base_model_name
+        self.num_labels = num_labels
+        self.dropout = dropout
+
+
+class CAPModel(PreTrainedModel):
+    config_class = CAPConfig
+    base_model_prefix = "backbone"
+
+    def __init__(self, config):
+        super().__init__(config)
+        backbone_config = AutoConfig.from_pretrained(config.base_model_name)
+        self.backbone = AutoModel.from_config(backbone_config)
+        hidden_size = self.backbone.config.hidden_size
+        self.num_labels = config.num_labels
+        self.dropout = nn.Dropout(config.dropout)
+        self.head = nn.Linear(hidden_size, config.num_labels)
+        self.post_init()
+
+    def forward(self, input_ids, attention_mask, token_valid_mask):
+        outputs = self.backbone(input_ids=input_ids, attention_mask=attention_mask)
+        subword_states = self.dropout(outputs.last_hidden_state)
+        token_logits = self.head(subword_states)
+        valid_mask = token_valid_mask.unsqueeze(-1)
+        masked_token_logits = token_logits * valid_mask
+        valid_counts = token_valid_mask.sum(dim=1, keepdim=True).clamp(min=1e-9)
+        sequence_logits = masked_token_logits.sum(dim=1) / valid_counts
+        return sequence_logits, token_logits