hasValueusedByrdfs:labelisInitializedByhasDefaultValueisAttributeOfdefinesdescriptionOther subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. “X motherOf this subject” — and answer questions the forward facts can't. Grouped by predicate.
enforcesEnforces(2)ex:forwardex:window_constraintsaccessesInstanceVariableAccesses Instance Variable(1)ex:calculate_new_window_sizeassignsAttributeAssigns Attribute(1)ex:__init__attributeAttribute(1)ex:window-size-mismatch-handlerclampedBetweenClamped Between(1)ex:window_sizehasAttributeHas Attribute(1)ex:WindowSizeMismatchHandlerinitializesInitializes(1)ex:__init__parameterParameter(1)ex:__init__setsAttributeSets Attribute(1)ex:__init__startsFromStarts From(1)ex:window-size-adjustmentstoresAttributeStores Attribute(1)ex:context-window-resizerusesUses(1)ex:clamping_operationTimeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
doc:beam/1debb6de-e212-4c64-aafb-6854993ee71b- The `resize_window` function ensures that the window size is within valid bounds (`min_window_size` and `max_window_size`). - It clamps the window size to the valid range before resizing the query. 4. **Complexity Calculation Funct…
doc:beam/8ebe7d3c-d57b-42ef-b244-664edb08d246# Ensure the window size is within valid bounds min_window_size = 256 max_window_size = 2048 # Clamp the window size to the valid range window_size = max(min_window_size, min(window_size, max_window_size)) …
doc:beam/83f64273-9200-45a2-92d1-45b3601b1ba6resizer = ContextWindowResizer(max_window_size=512) input_ids = torch.tensor([[1, 2, 3], [4, 5, 6]]) attention_mask = torch.tensor([[0, 0, 1], [1, 0, 0]]) resized_window = resizer(input_ids, attention_mask) print(resized_window) ``` How can…
doc:beam/95c16244-f18b-44ea-875f-e5f2b9343c8f# High complexity, resize to larger window resized_window = resize_window(query, 2048) elif complexity < 0.2: # Low complexity, resize to smaller window resized_window = resize_window(query, 256) else…
doc:beam/705baea2-2c37-4b6d-b265-85748bc1fdc6# Calculate the new window size based on query complexity new_window_sizes = self.calculate_new_window_size(input_ids, attention_mask) # Resize the context window for each batch element resized_windo…
doc:beam/567b6da2-812f-4974-8fda-2036a11691e1# Test the class resizer = ContextWindowResizer(max_window_size=512) input_ids = torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]) attention_mask = torch.tensor([[1, 1, 1, 0, 0], [1, 1, 1, 1, 0]]) resized_window = resizer(input_ids, attenti…
doc:beam/1f7c6123-f88e-467a-8ceb-ce496303cad91. **Check for Mismatch**: Verify if the input sequence length matches the expected window size. 2. **Handle Mismatch**: If there is a mismatch, either truncate or pad the input sequences to match the expected window size. 3. **Error Handli…
doc:beam/f1f8f635-6c4d-4009-a459-c40f4e5e49a5optimized_input_ids = self.optimize_input_ids(input_ids) optimized_attention_mask = self.optimize_attention_mask(attention_mask) return optimized_input_ids, optimized_attention_mask def optimize_inp…
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