Update README.md

README.md

@@ -22,7 +22,7 @@ datasets:
 
 **GLiNER-Multitask** is a model designed to extract various pieces of information from plain text based on a user-provided custom prompt. This versatile model leverages a bidirectional transformer encoder, similar to BERT, which ensures both high generalization and compute efficiency despite its compact size.
 
-The `gliner-multitask-large` variant achieves state-of-the-art performance on NER zero-shot benchmarks, demonstrating its robustness and flexibility. It excels not only in named entity recognition but also in handling various other information extraction tasks, making it a powerful tool for diverse natural language processing applications.
+The `gliner-multitask-v1.0` variant achieves state-of-the-art performance on NER zero-shot benchmarks, demonstrating its robustness and flexibility. It excels not only in named entity recognition but also in handling various other information extraction tasks, making it a powerful tool for diverse natural language processing applications.
 
 ### Supported tasks:
 * **Named Entity Recognition (NER)**: Identifies and categorizes entities such as names, organizations, dates, and other specific items in the text.
@@ -49,7 +49,7 @@ Once you've downloaded the GLiNER library, you can import the GLiNER class. You
 ```python
 from gliner import GLiNER
 
-model = GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v1.0")
+model = GLiNER.from_pretrained("knowledgator/gliner-multitask-v1.0")
 
 text = """
 Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014.
@@ -124,7 +124,7 @@ from utca.implementation.tasks import (
 
 predictor = GLiNERPredictor( # Predictor manages the model that will be used by tasks
     GLiNERPredictorConfig(
-        model_name = "knowledgator/gliner-multitask-large-v1.0", # Model to use
+        model_name = "knowledgator/gliner-multitask-v1.0", # Model to use
         device = "cuda:0", # Device to use
     )
 )