``reasoning_drop_prob`` and ``global_query_drop_prob`` (both 0.5 by default) drop each
field independently per query, so a row carrying both yields all four modes above. A
drop REMOVES the field -- no placeholder is substituted, so the prompt shape changes
with it and the instruction follows. The draw is a hash of
(drop_seed, epoch, field, query): identical across runs, workers and ranks, and
redrawn per epoch once ``set_epoch`` is called. Set both to 0.0 at
eval time to force full mode.
<ParamField path="DEFAULT_INSTRUCTIONS" type="dict[frozenset[str], str]">
</ParamField>
<ParamField path="DEFAULT_PREFIX_TEMPLATE">
</ParamField>
<ParamField path="DEFAULT_SUFFIX_TEMPLATE">
</ParamField>
<ParamField path="DEFAULT_SYSTEM">
</ParamField>
<ParamField path="_epoch">
</ParamField>
<ParamField path="instructions" type="= self._normalize_instructions(raw)">
</ParamField>
<ParamField path="prefix_ids">
</ParamField>
<ParamField path="suffix_ids">
</ParamField>
<Anchor id="nemo_automodel-components-models-qwen3_reranker-collator-Qwen3ContextAwareRerankerCollator-__call__">
<CodeBlock showLineNumbers={false} wordWrap={true}>
```python
nemo_automodel.components.models.qwen3_reranker.collator.Qwen3ContextAwareRerankerCollator.__call__(
    features: list[dict[str, typing.Any]]
) -> transformers.BatchEncoding
```
</CodeBlock>
</Anchor>
<Indent>
Tokenize flattened query-document rows into a padded batch.
**Parameters:**
<ParamField path="features" type="list[dict[str, Any]]">
One mapping per pair with question and doc_text strings, optional
reasoning/global_query strings, and num_labels giving the number of queries.
</ParamField>
**Returns:** `BatchEncoding`
BatchEncoding with input_ids and attention_mask of shape</Indent>
<Anchor id="nemo_automodel-components-models-qwen3_reranker-collator-Qwen3ContextAwareRerankerCollator-_format_one">
<CodeBlock showLineNumbers={false} wordWrap={true}>
```python
nemo_automodel.components.models.qwen3_reranker.collator.Qwen3ContextAwareRerankerCollator._format_one(
    query: str,
    doc: str,
    reasoning: str | None = None,
    global_query: str | None = None
) -> str
```
</CodeBlock>
</Anchor>
<Indent>
Build the user-turn text for a single (query, doc) pair.
Order: decide which context fields survive the drop draws, cap each surviving
item on its own token budget, pick the instruction matching what survived, then
assemble with the context sub-fields embedded inside ``&lt;Query&gt;:``.
Capping precedes assembly so each item is guaranteed its share. The document is
normally left uncapped and takes the remainder of ``rerank_max_length``, absorbing
all overflow on its own because it is assembled last.</Indent>
<Anchor id="nemo_automodel-components-models-qwen3_reranker-collator-Qwen3ContextAwareRerankerCollator-_keep_field">
<CodeBlock showLineNumbers={false} wordWrap={true}>
```python
nemo_automodel.components.models.qwen3_reranker.collator.Qwen3ContextAwareRerankerCollator._keep_field(
    kind: str,
    query: str,
    prob: float
) -> bool
```
</CodeBlock>
</Anchor>
<Indent>
Whether to KEEP a context field for this query, deterministically.
Keyed on the QUERY, not the (query, document) pair: the dataset repeats the
context fields across every passage of a group, and a listwise group is scored as
one softmax over 1 positive + n negatives. If passages within a group disagreed
about which context was present, the comparison would be between different
prompts rather than between documents.
Hashed rather than sampled from a shared RNG so the draw depends only on
(drop_seed, epoch, field, query) -- identical across runs, workers and ranks, and
independent of batch order. hashlib rather than hash(): PYTHONHASHSEED randomises
str hashing per process, which would make runs unreproducible.</Indent>
<Anchor id="nemo_automodel-components-models-qwen3_reranker-collator-Qwen3ContextAwareRerankerCollator-_normalize_instructions">
<CodeBlock showLineNumbers={false} wordWrap={true}>
```python
nemo_automodel.components.models.qwen3_reranker.collator.Qwen3ContextAwareRerankerCollator._normalize_instructions(
    raw: dict[str | tuple[str, ...] | frozenset[str], str]
) -> dict[frozenset[str], str]
```
</CodeBlock>
</Anchor>
<Indent>
<Badge>staticmethod</Badge>
Normalize instruction keys to frozensets of field name strings.
Accepts three key formats so callers can use whichever is most natural:
- ``frozenset`` — used directly (Python API).
- ``tuple`` of strings — converted to frozenset (Python API).
- ``str`` — comma-separated field names (YAML-friendly); empty string maps to
  the no-context frozenset. Examples::
      ""                    → frozenset()
      "reasoning"           → frozenset(&#123;"reasoning"&#125;)
      "global_query,reasoning" → frozenset(&#123;"global_query","reasoning"&#125;)</Indent>
<Anchor id="nemo_automodel-components-models-qwen3_reranker-collator-Qwen3ContextAwareRerankerCollator-_truncate_tokens">
<CodeBlock showLineNumbers={false} wordWrap={true}>
```python
nemo_automodel.components.models.qwen3_reranker.collator.Qwen3ContextAwareRerankerCollator._truncate_tokens(
    text: str,
    limit: int | None = None
) -> str
```
</CodeBlock>
</Anchor>
<Indent>
Cut ``text`` to at most ``limit`` tokens. No-op when limit is None.</Indent>
<Anchor id="nemo_automodel-components-models-qwen3_reranker-collator-Qwen3ContextAwareRerankerCollator-set_epoch">
<CodeBlock showLineNumbers={false} wordWrap={true}>
```python
nemo_automodel.components.models.qwen3_reranker.collator.Qwen3ContextAwareRerankerCollator.set_epoch(
    epoch: int
) -> None
```
</CodeBlock>
</Anchor>
<Indent>
Set the epoch used for deterministic context dropout.
Called by ``StepScheduler.set_epoch`` on the training dataloader's collate
function, so drops are redrawn each epoch. A validation collator is never given an
epoch and stays at 0, keeping its prompt mix fixed -- otherwise a val_loss that
moved because the sampled modes changed would be indistinguishable from one that
moved because the model did.
**Parameters:**
<ParamField path="epoch" type="int">
Zero-based index of the epoch about to run.
</ParamField></Indent></Indent>
<style>{`
  .light .fern-code-block,
  .light .fern-prose code:not(.code-block) {
    background-color: var(--nv-color-bg-alt, #f7f7f7) !important;
  }
  .dark .fern-code-block,
  .dark .fern-prose code:not(.code-block) {
    background-color: #1f1f1f !important;
  }
`}</style>