Update README.md

README.md

@@ -126,7 +126,7 @@ tags:
 
 **comprehend-it-multilang-base-base**
 
-This is an encoder-decoder model based on [mT5-base](google/mt5-base) that was trained on multi-language natural language inference datasets as well as on multiple text classification datasets. 
+This is an encoder-decoder model based on [mT5-base](https://huggingface.co/google/mt5-base) that was trained on multi-language natural language inference datasets as well as on multiple text classification datasets. 
 
 The model demonstrates a better contextual understanding of text and verbalized label because both inputs are encoded by different parts of a model - encoder and decoder respectively.
 
@@ -149,8 +149,8 @@ from liqfit.pipeline import ZeroShotClassificationPipeline
 from liqfit.models import T5ForZeroShotClassification
 from transformers import T5Tokenizer
 
-model = T5ForZeroShotClassification.from_pretrained('knowledgator/comprehend-it-multilang-base')
-tokenizer = T5Tokenizer.from_pretrained('knowledgator/comprehend-it-multilang-base')
+model = T5ForZeroShotClassification.from_pretrained('knowledgator/comprehend_it-multilingual-t5-base')
+tokenizer = T5Tokenizer.from_pretrained('knowledgator/comprehend_it-multilingual-t5-base')
 classifier = ZeroShotClassificationPipeline(model=model, tokenizer=tokenizer,
                                                       hypothesis_template = '{}', encoder_decoder = True)
 ```