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@@ -33,3 +33,4 @@ 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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language: zh
+tags:
+- sentiment-analysis
+- emotion-classification
+- slang
+- traditional-chinese
+license: apache-2.0
+widget:
+- text: "頂"
+- text: "笑死"
+- text: "QQ"
+- text: "傻眼"
+---
+
+# 台灣網路俚語情緒分析模型
+
+繁體中文網路俚語情緒分析模型，支援 8 種情緒分類。
+
+## 情緒類別
+
+- LABEL_0: 平淡語氣
+- LABEL_1: 關切語調
+- LABEL_2: 開心語調
+- LABEL_3: 憤怒語調
+- LABEL_4: 悲傷語調
+- LABEL_5: 疑問語調
+- LABEL_6: 驚奇語調
+- LABEL_7: 厭惡語調
+
+## 使用方式
+```python
+from transformers import pipeline
+
+# 注意用戶名是 Yao0965
+classifier = pipeline('text-classification', model='Yao0965/slang-emotion-model')
+result = classifier('笑死')[0]
+print(result)
+```
+
+## 範例
+
+| 輸入 | 預測情緒 |
+|------|---------|
+| 頂 | 開心語調 |
+| 雷 | 厭惡語調 |
+| 笑死 | 開心語調 |
+| QQ | 悲傷語調 |

config.json

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+{
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+    "XLMRobertaForSequenceClassification"
+  ],
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+  "hidden_act": "gelu",
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+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7"
+  },
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+  "intermediate_size": 4096,
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+    "LABEL_6": 6,
+    "LABEL_7": 7
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+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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special_tokens_map.json

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tokenizer_config.json

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+}