Fill-Mask
Transformers
PyTorch
Norwegian
Norwegian Bokmål
Norwegian Nynorsk
BERT
NorBERT
Norwegian
encoder
custom_code
Instructions to use ltg/norbert3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ltg/norbert3-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ltg/norbert3-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_norbert.py
Browse files- modeling_norbert.py +1 -1
modeling_norbert.py
CHANGED
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@@ -163,7 +163,7 @@ class Attention(nn.Module):
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position_indices = torch.arange(query_len, dtype=torch.long).unsqueeze(1) \
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- torch.arange(query_len, dtype=torch.long).unsqueeze(0)
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position_indices = self.make_log_bucket_position(position_indices, self.config.position_bucket_size, 512)
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| 166 |
-
position_indices = self.position_bucket_size - 1 + position_indices
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self.position_indices = position_indices.to(hidden_states.device)
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hidden_states = self.pre_layer_norm(hidden_states)
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position_indices = torch.arange(query_len, dtype=torch.long).unsqueeze(1) \
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- torch.arange(query_len, dtype=torch.long).unsqueeze(0)
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position_indices = self.make_log_bucket_position(position_indices, self.config.position_bucket_size, 512)
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+
position_indices = self.config.position_bucket_size - 1 + position_indices
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self.position_indices = position_indices.to(hidden_states.device)
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hidden_states = self.pre_layer_norm(hidden_states)
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