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README.md

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+---
+language: en
+tags: [text-classification, emotion-detection, mental-health, hci, distilbert, cnn]
+datasets: [go_emotions]
+metrics: [f1, accuracy]
+---
+# MoodShift — Hybrid DistilBERT + CNN Emotion Classifier
+Novel hybrid architecture for HCI research (ICCA 2026).
+
+## Architecture
+- **DistilBERT** → [CLS] global context (768-dim)
+- **CNN (kernels 2,3,4)** → local n-gram emotion patterns (384-dim)
+- **Fusion** → 1152-dim → Linear(512) → Linear(7)
+
+## Labels
+positive | sadness | anger | anxiety | confusion | curiosity | neutral
+
+Best Val F1: **0.9158**

config.json

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+{
+  "model_type": "hybrid_distilbert_cnn",
+  "num_labels": 6,
+  "id2label": {
+    "0": "sadness",
+    "1": "joy",
+    "2": "love",
+    "3": "anger",
+    "4": "fear",
+    "5": "surprise"
+  },
+  "label2id": {
+    "sadness": 0,
+    "joy": 1,
+    "love": 2,
+    "anger": 3,
+    "fear": 4,
+    "surprise": 5
+  },
+  "cnn_embed_dim": 128,
+  "cnn_filters": 128,
+  "cnn_kernels": [
+    2,
+    3,
+    4
+  ],
+  "max_len": 128,
+  "bert_base": "distilbert-base-uncased",
+  "best_val_f1": 0.9158
+}

model_arch.py

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+
+import torch, torch.nn as nn, torch.nn.functional as F
+from transformers import DistilBertModel
+
+class CNNBranch(nn.Module):
+    def __init__(self, vocab=30522, edim=128, nf=128, kernels=(2,3,4)):
+        super().__init__()
+        self.emb=nn.Embedding(vocab,edim,padding_idx=0)
+        self.convs=nn.ModuleList([nn.Conv1d(edim,nf,k,padding=k//2) for k in kernels])
+        self.drop=nn.Dropout(0.3); self.out_dim=nf*len(kernels)
+    def forward(self,ids):
+        x=self.emb(ids).permute(0,2,1)
+        return self.drop(torch.cat([F.adaptive_max_pool1d(F.gelu(c(x)),1).squeeze(2) for c in self.convs],1))
+
+class HybridClassifier(nn.Module):
+    def __init__(self, n_labels=6, vocab=30522, edim=128, nf=128, kernels=(2,3,4), drop=0.3):
+        super().__init__()
+        self.bert=DistilBertModel.from_pretrained("distilbert-base-uncased")
+        self.cnn=CNNBranch(vocab,edim,nf,kernels)
+        fused=768+self.cnn.out_dim
+        self.head=nn.Sequential(nn.Dropout(drop),nn.Linear(fused,512),nn.GELU(),nn.Dropout(drop*0.7),nn.Linear(512,n_labels))
+        self.n_labels=n_labels
+    def forward(self, input_ids, attention_mask):
+        cls=self.bert(input_ids=input_ids,attention_mask=attention_mask).last_hidden_state[:,0,:]
+        cnn=self.cnn(input_ids)
+        return self.head(torch.cat([cls,cnn],1))

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:80cb48d3b4da0914f22041e29bb8d1c808e7f728cd2c4fd6c88fd6961f748a81
+size 284087647

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}