Update README.md

README.md

@@ -8,7 +8,8 @@ pipeline_tag: image-classification
 
 The **Fine-Tuned Vision Transformer (ViT)** is a variant of the transformer encoder architecture, similar to BERT, that has been adapted for image classification tasks. This specific model, named "google/vit-base-patch16-224-in21k," is pre-trained on a substantial collection of images in a supervised manner, leveraging the ImageNet-21k dataset. The images in the pre-training dataset are resized to a resolution of 224x224 pixels, making it suitable for a wide range of image recognition tasks.
 
-During the pre-training phase, the model underwent training for fewer than 20 epochs with a batch size of 16. This training process involved learning valuable visual features from the ImageNet-21k dataset to create a robust foundation for subsequent fine-tuning on specific tasks.
+During the pre-training phase, the model underwent training for fewer than 20 epochs with a batch size of 16. 
+This training process involved learning valuable visual features from the dataset to create a robust foundation for this specific tasks.
 
 ## Intended Uses & Limitations
 
@@ -53,7 +54,8 @@ model.config.id2label[predicted_label]
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 ### Limitations
-- **Specialized Task Fine-Tuning**: While the model is adept at NSFW image classification, its performance may vary when applied to other tasks. Users interested in employing this model for different tasks should explore fine-tuned versions available in the model hub for optimal results.
+- **Specialized Task Fine-Tuning**: While the model is adept at NSFW image classification, its performance may vary when applied to other tasks.
+- Users interested in employing this model for different tasks should explore fine-tuned versions available in the model hub for optimal results.
 
 ## Training Data