Add model card

README.md

@@ -7,9 +7,9 @@ datasets:
 - imagenet-21k
 ---
 
-# BEiT (large-sized model, fine-tuned on ImageNet-22k) 
+# BEiT (large-sized model, pre-trained only) 
 
-BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on the same dataset at resolution 224x224. It was introduced in the paper [BEIT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong and Furu Wei and first released in [this repository](https://github.com/microsoft/unilm/tree/master/beit). 
+BEiT model pre-trained in a self-supervised fashion on ImageNet-22k - also called ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper [BEIT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong and Furu Wei and first released in [this repository](https://github.com/microsoft/unilm/tree/master/beit). 
 
 Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team.
 
@@ -32,26 +32,26 @@ fine-tuned versions on a task that interests you.
 Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
 
 ```python
-from transformers import BEiTFeatureExtractor, BEiTForImageClassification
+from transformers import BeitFeatureExtractor, BeitForMaskedImageModeling
 from PIL import Image
 import requests
+
 url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
 image = Image.open(requests.get(url, stream=True).raw)
-feature_extractor = BEiTFeatureExtractor.from_pretrained('microsoft/beit-large-patch16-224-pt22k')
-model = BEiTForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k')
+
+feature_extractor = BeitFeatureExtractor.from_pretrained('microsoft/beit-large-patch16-224-pt22k')
+model = BeitForMaskedImageModeling.from_pretrained('microsoft/beit-large-patch16-224-pt22k')
+
 inputs = feature_extractor(images=image, return_tensors="pt")
 outputs = model(**inputs)
 logits = outputs.logits
-# model predicts one of the 21,841 ImageNet-22k classes
-predicted_class_idx = logits.argmax(-1).item()
-print("Predicted class:", model.config.id2label[predicted_class_idx])
 ```
 
 Currently, both the feature extractor and model support PyTorch.
 
 ## Training data
 
-The BEiT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on the same dataset.
+The BEiT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes.
 
 ## Training procedure