updated links from sgnlp to sgnlp-models

README.md

@@ -106,11 +106,11 @@ from sgnlp.models.emotion_entailment import (
 
 # Load model
 config = RecconEmotionEntailmentConfig.from_pretrained(
-    "https://storage.googleapis.com/sgnlp/models/reccon_emotion_entailment/config.json"
+    "https://storage.googleapis.com/sgnlp-models/models/reccon_emotion_entailment/config.json"
 )
 tokenizer = RecconEmotionEntailmentTokenizer.from_pretrained("roberta-base")
 model = RecconEmotionEntailmentModel.from_pretrained(
-    "https://storage.googleapis.com/sgnlp/models/reccon_emotion_entailment/pytorch_model.bin",
+    "https://storage.googleapis.com/sgnlp-models/models/reccon_emotion_entailment/pytorch_model.bin",
     config=config,
 )
 preprocessor = RecconEmotionEntailmentPreprocessor(tokenizer)
@@ -153,8 +153,8 @@ The train and evaluation datasets were derived from the RECCON dataset. The full
 - **Training Time:** ~3 hours for 12 epochs on a single V100 GPU.
 
 # Model Parameters
-- **Model Weights:** [link](https://storage.googleapis.com/sgnlp/models/reccon_emotion_entailment/pytorch_model.bin)
-- **Model Config:** [link](https://storage.googleapis.com/sgnlp/models/reccon_emotion_entailment/config.json)
+- **Model Weights:** [link](https://storage.googleapis.com/sgnlp-models/models/reccon_emotion_entailment/pytorch_model.bin)
+- **Model Config:** [link](https://storage.googleapis.com/sgnlp-models/models/reccon_emotion_entailment/config.json)
 - **Model Inputs:** Target utterance, emotion in target utterance, evidence utterance and conversational history.
 - **Model Outputs:** Probability score of whether evidence utterance caused target utterance to exhibit the emotion specified.
 - **Model Size:** ~477MB