Model Training Code
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Model Training Code has 12 facts recorded in Dontopedia across 2 references, with 2 live disagreements.
Mostly:contains(5), saves(2), uses(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (7)
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.
containedInContained in(3)
- Profiling Block
ex:profiling-block - Record Function Block
ex:record-function-block - Training Loop
ex:training-loop
containsContains(2)
- Code Snippet
ex:code-snippet - Code Structure
ex:code-structure
followedByFollowed by(1)
- Inference Example
ex:inference-example
usedInUsed in(1)
- Hugging Face Transformers
ex:hugging-face-transformers
Other facts (12)
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 |
|---|---|---|
| Contains | Profiling Block | [2] |
| Contains | Record Function Block | [2] |
| Contains | Training Loop | [2] |
| Contains | Profiling Output | [2] |
| Contains | Inference Example | [2] |
| Saves | Model to File | [1] |
| Saves | Tokenizer to File | [1] |
| Uses | Trainer | [1] |
| Performs | Model Evaluation | [1] |
| Prints | Evaluation Results | [1] |
| Target Directory | ./fine_tuned_model | [1] |
| Rdf:type | Code Snippet | [2] |
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 (2)
ctx:claims/beam/5204f06e-f2cf-464f-a927-d8caac3da87b- full textbeam-chunktext/plain1 KB
doc:beam/5204f06e-f2cf-464f-a927-d8caac3da87bShow excerpt
model=model, args=training_args, train_dataset=train_dataset, eval_dataset=_dataset, ) # Train the model trainer.train() # Evaluate the model eval_results = trainer.evaluate() print(f"Evaluation results: {eval_results}") …
ctx:claims/beam/80e4b051-0931-49af-8359-38149d7a6361- full textbeam-chunktext/plain1 KB
doc:beam/80e4b051-0931-49af-8359-38149d7a6361Show excerpt
with profiler.profile(record_shapes=True, use_cuda=True) as prof: with profiler.record_function("model_training"): for i, (batch_inputs, batch_targets) in enumerate(dataloader): with autocast(): # Us…
See also
Keep researching
Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.