Upload config

config.json

@@ -1,11 +1,6 @@
 {
-  "_name_or_path": "shhossain/all-MiniLM-L6-v2-sentiment-classifier",
-  "architectures": [
-    "SententenceTransformerSentimentModel"
-  ],
   "auto_map": {
-    "AutoConfig": "shhossain/all-MiniLM-L6-v2-sentiment-classifier--config.SentimentConfig",
-    "AutoModelForSequenceClassification": "shhossain/all-MiniLM-L6-v2-sentiment-classifier--model.SententenceTransformerSentimentModel"
+    "AutoConfig": "config.SentimentConfig"
   },
   "class_map": {
     "0": "sad",
@@ -15,20 +10,9 @@
     "4": "fear",
     "5": "surprise"
   },
-  "custom_pipelines": {
-    "text-classification": {
-      "impl": "smodelpipeline.SentimentModelPipe",
-      "pt": [
-        "AutoModelForSequenceClassification"
-      ],
-      "tf": [],
-      "type": "text"
-    }
-  },
-  "embedding_model": "all-MiniLM-L6-v2",
+  "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
   "h1": 44,
   "h2": 46,
   "model_type": "SententenceTransformerSentimentClassifier",
-  "torch_dtype": "float32",
   "transformers_version": "4.29.0"
 }

config.py

@@ -6,7 +6,7 @@ mp = {0:'sad',1:'joy',2:'love',3:'anger',4:'fear',5:'surprise'}
 class SentimentConfig(PretrainedConfig):
     model_type = "SententenceTransformerSentimentClassifier"
 
-    def __init__(self, embedding_model: str="all-MiniLM-L6-v2", class_map: dict=mp, h1: int=44, h2: int=46, **kwargs):
+    def __init__(self, embedding_model: str="sentence-transformers/all-MiniLM-L6-v2", class_map: dict=mp, h1: int=44, h2: int=46, **kwargs):
         self.embedding_model = embedding_model
         self.class_map = class_map
         self.h1 = h1