* IMDB_URDUSENTIMENT_MODEL
I have used IMDB URDU dataset to create custom model by using DistilBertForSequenceClassification.
{"language": ["en"], "license": "apache-2.0", "tags": ["text Classification"], "widget": [{"text": "\u0645\u06cc\u06ba \u062a\u0645\u06c1\u06cc\u06ba \u067e\u0633\u0646\u062f \u06a9\u0631\u062a\u0627 \u06c1\u0648\u06ba. </s></s> \u0645\u06cc\u06ba \u062a\u0645 \u0633\u06d2 \u067e\u06cc\u0627\u0631 \u06a9\u0631\u062a\u0627 \u06c1\u0648\u06ba."}]}
transformers pytorch safetensors distilbert text-classification text Classification en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #text Classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

* IMDB_URDUSENTIMENT_MODEL
I have used IMDB URDU dataset to create custom model by using DistilBertForSequenceClassification.
TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #text Classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #text Classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
