Update README.md

README.md

@@ -2,45 +2,48 @@
 tags:
 - generated_from_keras_callback
 model-index:
-- name: adult-content-identify-text
+- name: adult-content-classifier-text
   results: []
+pipeline_tag: text-classification
+widget:
+- text: 情趣睡衣 性感睡衣 角色扮演 惹火 緊身連身 漆皮膠衣 黑 L
+  example_title: adult_成人商品標題
+- text: 男平織運動外套-立領外套 慢跑 路跑 藍黑螢光黃 L
+  example_title: regular_一般商品標題
+- text: 'Passionate Embrace: Cross-Laced Sexy Silky Lingerie Sleepwear, White'
+  example_title: adult_product
+- text: >-
+    Men's Plain Weave Sports Jacket - Stand Collar Jacket for Jogging and
+    Running, Blue-Black-Fluorescent Yellow, Size L
+  example_title: regular_product
+license: mit
+language:
+- en
+- zh
 ---
-
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
 probably proofread and complete it, then remove this comment. -->
 
 # adult-content-identify-text
 
-This model was trained from scratch on an unknown dataset.
-It achieves the following results on the evaluation set:
-
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- optimizer: None
-- training_precision: float32
-
-### Training results
+Determine whether online sales products are adult content. Input: product title or description, Output results: 0 Unknown, 1 Adult Content, 2 General Merchandise.
 
+判斷網路銷售商品是否屬於成人內容。輸入: 商品名稱文字，輸出結果: 0 未知, 1 成人內容, 2 一般商品。
 
+# use transformers pipeline
+```python
+from transformers import pipeline
+pipe = pipeline("text-classification", model="jiechau/adult-content-classifier")
+#q = '男平織運動外套-立領外套 慢跑 路跑 藍黑螢光黃 L'
+q = '情趣睡衣 性感睡衣 角色扮演 惹火 緊身連身 漆皮膠衣 黑 L'
+result = pipe(q)
+print(result)
+# [{'label': 'adult_成人商品', 'score': 0.9994813799858093}]
+```
 
 ### Framework versions
 
-- Transformers 4.38.2
+- Transformers 4.37.1
 - TensorFlow 2.15.0
-- Tokenizers 0.15.2
+- Datasets 2.17.0
+- Tokenizers 0.15.1