interstellarninja
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Update README.md
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README.md
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@@ -26,7 +26,7 @@ The following `bitsandbytes` quantization config was used during training:
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## Inferenece Example:
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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<<<domain>>>: Image classification, <<<api_call>>>: model = hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/5'), <<<api_provider>>>: TensorFlow Hub, <<<explanation>>>: This API call uses TensorFlow's pre-trained Inception V3 image classification model from TensorFlow Hub, which can be used to classify images into multiple categories including animal species and objects, among other things. By loading this model, you can analyze a given picture of an animal from a database, and get the predictions for its class label. <<<code>>>: import tensorflow as tf
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import tensorflow_hub as hub
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model_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'
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model = hub.load(model_id)
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## Inferenece Example:
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### System:
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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<<<domain>>>: Image classification, <<<api_call>>>: model = hub.load('https://tfhub.dev/google/imagenet/inception_v3/classification/5'), <<<api_provider>>>: TensorFlow Hub, <<<explanation>>>: This API call uses TensorFlow's pre-trained Inception V3 image classification model from TensorFlow Hub, which can be used to classify images into multiple categories including animal species and objects, among other things. By loading this model, you can analyze a given picture of an animal from a database, and get the predictions for its class label. <<<code>>>: import tensorflow as tf
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import tensorflow_hub as hub
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model_id = 'https://tfhub.dev/google/imagenet/inception_v3/classification/5'
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model = hub.load(model_id)
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