Update README.md

README.md

@@ -23,6 +23,8 @@ It can be used for `topic classification`, `sentiment analysis` and as a reranke
 
 The model was trained on synthetic data and can be used in commercial applications.
 
+This model wasn't additionally fine-tuned on any dataset except initial (MoritzLaurer/synthetic_zeroshot_mixtral_v0.1).
+
 ### How to use:
 First of all, you need to install GLiClass library:
 ```bash
@@ -34,8 +36,8 @@ Than you need to initialize a model and a pipeline:
 from gliclass import GLiClassModel, ZeroShotClassificationPipeline
 from transformers import AutoTokenizer
 
-model = GLiClassModel.from_pretrained("knowledgator/gliclass-base-v1")
-tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-base-v1")
+model = GLiClassModel.from_pretrained("knowledgator/gliclass-base-v1.0-init")
+tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-base-v1.0-init")
 
 pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')