Upload TFBertForSequenceClassification

README.md

@@ -2,48 +2,45 @@
 tags:
 - generated_from_keras_callback
 model-index:
-- name: adult-content-classifier-text
+- name: adult-content-identify-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
 
-Determine whether online sales products are adult content. Input: product title or description, Output results: 0 Unknown, 1 Adult Content, 2 General Merchandise.
+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
 
-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 4.37.1
+- Transformers 4.38.2
 - TensorFlow 2.15.0
-- Datasets 2.17.0
-- Tokenizers 0.15.1
+- Tokenizers 0.15.2

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "/home/jie/model/text",
+  "_name_or_path": "/home/rundeck/_ai_bulk_files/Colab_Notebooks/_saved_model/_saved_model_friDay/friDay_adult_sexy/FacebookAI-xlm-roberta-base-tf-keras",
   "architectures": [
     "BertForSequenceClassification"
   ],
@@ -11,16 +11,16 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "unknown",
-    "1": "adult_\u6210\u4eba\u5546\u54c1",
-    "2": "regular_\u4e00\u822c\u5546\u54c1"
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
   "label2id": {
-    "adult_\u6210\u4eba\u5546\u54c1": 1,
-    "regular_\u4e00\u822c\u5546\u54c1": 2,
-    "unknown": 0
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
   },
   "layer_norm_eps": 1e-05,
   "max_position_embeddings": 514,
@@ -30,7 +30,7 @@
   "output_past": true,
   "pad_token_id": 1,
   "position_embedding_type": "absolute",
-  "transformers_version": "4.37.1",
+  "transformers_version": "4.38.2",
   "type_vocab_size": 1,
   "use_cache": true,
   "vocab_size": 250002

tf_model.h5

@@ -1,3 +1,3 @@
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+oid sha256:c44889ea931a7a55e38a495430be4f547cdbf2183ba62d1bdb90ccac4b7c1199
 size 1112471836