Results Directory
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Results Directory has 6 facts recorded in Dontopedia across 3 references, with 1 live disagreement.
Mostly:rdf:type(2), has path(1), is path(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (2)
Other 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.
hasOutputDirectoryHas Output Directory(1)
- Training Args
ex:training-args
hasParameterHas Parameter(1)
- Training Args
ex:training-args
Other facts (6)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Directory | [1] |
| Rdf:type | Output Directory | [2] |
| Has Path | ./results | [2] |
| Is Path | ./results | [3] |
| Is Relative Path | true | [3] |
| Stores | Trained Model | [3] |
Timeline
Timeline 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.
References (3)
ctx:claims/beam/d63b152b-34b0-4323-aea7-f9df40b773a8- full textbeam-chunktext/plain1 KB
doc:beam/d63b152b-34b0-4323-aea7-f9df40b773a8Show excerpt
#### 1. Data Preprocessing ```python from transformers import LlamaTokenizer import torch # Load tokenizer tokenizer = LlamaTokenizer.from_pretrained("llama-2-13b") # Tokenize dataset def tokenize_function(examples): return tokenizer…
ctx:claims/beam/018e6829-a4ce-4a26-9be8-6d8ad3231779- full textbeam-chunktext/plain1 KB
doc:beam/018e6829-a4ce-4a26-9be8-6d8ad3231779Show excerpt
# Define training arguments training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_steps=500, weight_decay=0.01, loggi…
ctx:claims/beam/044caebd-7135-4d04-8046-0eaeb9f0641d- full textbeam-chunktext/plain1 KB
doc:beam/044caebd-7135-4d04-8046-0eaeb9f0641dShow excerpt
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()} item['labels'] = torch.tensor(self.labels[idx]) return item def __len__(self): return len(self.labels) train_dataset = TokenDa…
See also
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