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Update README.md

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@@ -5,7 +5,7 @@ library_name: transformers
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  pipeline_tag: image-classification
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  ---
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- # SwiftFormer
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  ## Model description
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@@ -28,12 +28,12 @@ SwiftFormer paper introduces a novel efficient additive attention mechanism that
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  image = Image.open(requests.get(url, stream=True).raw)
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  from transformers import ViTImageProcessor
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- processor = ViTImageProcessor.from_pretrained('shehan97/swiftformer-xs')
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  inputs = processor(images=image, return_tensors="pt")
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  from transformers.models.swiftformer import SwiftFormerForImageClassification
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- new_model = SwiftFormerForImageClassification.from_pretrained('shehan97/swiftformer-xs')
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  output = new_model(inputs['pixel_values'], output_hidden_states=True)
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  logits = output.logits
 
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  pipeline_tag: image-classification
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  ---
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+ # SwiftFormer (swiftformer-s)
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  ## Model description
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  image = Image.open(requests.get(url, stream=True).raw)
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  from transformers import ViTImageProcessor
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+ processor = ViTImageProcessor.from_pretrained('shehan97/swiftformer-s')
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  inputs = processor(images=image, return_tensors="pt")
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  from transformers.models.swiftformer import SwiftFormerForImageClassification
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+ new_model = SwiftFormerForImageClassification.from_pretrained('shehan97/swiftformer-s')
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  output = new_model(inputs['pixel_values'], output_hidden_states=True)
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  logits = output.logits