Instructions to use brain-bzh/reve-positions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brain-bzh/reve-positions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="brain-bzh/reve-positions", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("brain-bzh/reve-positions", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update position_bank.py
Browse files- position_bank.py +1 -0
position_bank.py
CHANGED
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@@ -13,6 +13,7 @@ class RevePositionBank(PreTrainedModel):
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self.position_names = config.position_names
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self.mapping = {name: i for i, name in enumerate(self.position_names)}
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self.register_buffer("embedding", torch.randn(len(self.position_names), 3))
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def forward(self, channel_names: list[str]):
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indices = [self.mapping[q] for q in channel_names if q in self.mapping]
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self.position_names = config.position_names
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self.mapping = {name: i for i, name in enumerate(self.position_names)}
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self.register_buffer("embedding", torch.randn(len(self.position_names), 3))
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self.post_init()
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def forward(self, channel_names: list[str]):
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indices = [self.mapping[q] for q in channel_names if q in self.mapping]
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