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The output dimensions for one of the examples is wrong, since the batch size is 2, not 1. Further, there is no need to import RobertaClassificationHead for that example, as it is later accessed via torchtext.models.RobertaClassificationHead

How to reproduce the issue

Run the following:

>>> import torch, torchtext
>>> from torchtext.models import RobertaClassificationHead
>>> from torchtext.functional import to_tensor
>>> xlmr_large = torchtext.models.XLMR_LARGE_ENCODER
>>> classifier_head = torchtext.models.RobertaClassificationHead(num_classes=2, input_dim = 1024)
>>> model = xlmr_large.get_model(head=classifier_head)
>>> transform = xlmr_large.transform()
>>> input_batch = ["Hello world", "How are you!"]
>>> model_input = to_tensor(transform(input_batch), padding_value=1)
>>> output = model(model_input)
>>> output.shape

Expected result

torch.Size([2, 2])

Result given in docstring

torch.Size([1, 2])

The output dimensions for one of the examples is wrong, since the batch size is 2, not 1. Further, there is no need to import RobertaClassificationHead for that example, as it is accessed via torchtext.models.RobertaClassificationHead
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pytorch-bot bot commented May 16, 2024

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/text/2265

Note: Links to docs will display an error until the docs builds have been completed.

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