refactor : 统一 SectionedMaskBuilder，支持可配置 dtype
- 三合一 MaskBuilder，移除 chat/instruction/text，统一为 sections 配置 - OutputConfig 增加 dtype 字段 (per-key，默认 int32) - 移除 from __future__ import annotations - 测试适配新配置格式
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+102
-104
@@ -1,13 +1,11 @@
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"""Mask building strategies for preprocessing pipeline.
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Each builder knows how to tokenize one input format and construct
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the loss_mask according to declarative mask rules from the config.
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The single :class:`SectionedMaskBuilder` handles all input formats
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via declarative ``input.sections`` config.
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"""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import List, Optional
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from typing import Optional
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from astrai.factory import BaseFactory
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@@ -40,122 +38,122 @@ def _extract_domain(item: dict, domain_key: Optional[str]) -> str:
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return val if isinstance(val, str) else "__default__"
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@MaskBuilderFactory.register("chat")
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class ChatMaskBuilder(BaseMaskBuilder):
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"""Mask by role via message-level tokenisation with role-span tracking.
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def _resolve_action(action: str, role: str, config) -> str:
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"""Resolve action to "train" or "mask".
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For each message, renders the chat template for that single message,
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encodes individually, and records its token span + role action.
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The concatenated sequence receives a loss_mask built from span rules.
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- ``"train"`` / ``"mask"`` → literal
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- ``"$role"`` → look up ``role`` in ``config.mask``, fall back to ``config.mask_default``
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"""
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if action == "$role":
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return config.mask.get(role, config.mask_default)
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return action
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@MaskBuilderFactory.register("sectioned")
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class SectionedMaskBuilder(BaseMaskBuilder):
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"""Config-driven builder: iterates over ``input.sections`` in order.
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Each section specifies a JSONL field + mask action.
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Section spec::
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{
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"field": "messages", # JSONL key
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"action": "$role", # "train" | "mask" | "$role"
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"template": true, # apply chat_template per message (optional)
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"add_special_tokens": false # override encode flag (optional)
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}
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Example configs::
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# Chat
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{"input": {"sections": [
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{"field": "messages", "action": "$role", "template": true}
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]}}
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# Instruction
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{"input": {"sections": [
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{"field": "prompt", "action": "mask", "add_special_tokens": true},
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{"field": "response", "action": "train"}
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]}}
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# Text
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{"input": {"sections": [
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{"field": "text", "action": "train"}
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]}}
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"""
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def build(self, item: dict, config, tokenizer) -> Optional[dict]:
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messages = item.get(config.input.messages_key)
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if not isinstance(messages, list) or not messages:
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sections = config.input.sections
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if not sections:
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return None
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all_ids: List[int] = []
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spans: List[tuple] = []
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all_ids: list[int] = []
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loss_mask: list[int] = []
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if tokenizer.bos_token_id is not None:
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has_template = any(s.get("template") for s in sections)
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is_text_config = not has_template and all(
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s["action"] == "train" for s in sections
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)
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if has_template and tokenizer.bos_token_id is not None:
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all_ids.append(tokenizer.bos_token_id)
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loss_mask.append(0)
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for msg in messages:
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role = msg.get("role", "")
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action = config.mask.get(role, config.mask_default)
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rendered = tokenizer.apply_chat_template(
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[msg], tokenize=False, add_generation_prompt=False
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first_section = True
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for sec in sections:
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field = sec["field"]
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action = sec["action"]
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use_template = sec.get("template", False)
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add_special = sec.get(
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"add_special_tokens", not use_template and first_section
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)
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ids = tokenizer.encode(rendered, add_special_tokens=False)
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start = len(all_ids)
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all_ids.extend(ids)
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spans.append((start, len(all_ids), action))
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if use_template:
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messages = item.get(field)
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if not isinstance(messages, list) or not messages:
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continue
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for msg in messages:
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role = msg.get("role", "")
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act = _resolve_action(action, role, config)
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rendered = tokenizer.apply_chat_template(
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[msg], tokenize=False, add_generation_prompt=False
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)
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ids = tokenizer.encode(rendered, add_special_tokens=False)
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all_ids.extend(ids)
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val = 1 if act == "train" else 0
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loss_mask.extend([val] * len(ids))
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else:
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text = str(item.get(field, ""))
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if not text.strip():
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continue
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if is_text_config:
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pp = config.preprocessing
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if pp.min_chars > 0 and len(text) < pp.min_chars:
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continue
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if len(text) > pp.max_chars:
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continue
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ids = tokenizer.encode(text, add_special_tokens=add_special)
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all_ids.extend(ids)
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val = 1 if action == "train" else 0
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loss_mask.extend([val] * len(ids))
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if len(all_ids) <= 1:
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return None
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first_section = False
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max_len = config.preprocessing.max_seq_len
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all_ids = all_ids[:max_len]
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loss_mask = loss_mask[: len(all_ids)]
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loss_mask = [0] * len(all_ids)
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for start, end, action in spans:
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if start >= len(all_ids):
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break
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e = min(end, len(all_ids))
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if action == "train":
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loss_mask[start:e] = [1] * (e - start)
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if not all_ids:
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return None
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return {
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if has_template and len(all_ids) <= 1:
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return None
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result: dict = {
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"ids": all_ids,
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"loss_mask": loss_mask,
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"domain": _extract_domain(item, config.output.domain_key),
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}
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@MaskBuilderFactory.register("instruction")
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class InstructionMaskBuilder(BaseMaskBuilder):
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"""Mask by prompt / response field boundary.
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Encodes prompt and response independently, then fills mask
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according to ``prompt`` / ``response`` entries in the mask config.
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"""
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def build(self, item: dict, config, tokenizer) -> Optional[dict]:
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prompt = str(item.get(config.input.prompt_key, ""))
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response = str(item.get(config.input.response_key, ""))
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if not prompt.strip() and not response.strip():
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return None
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prompt_ids = tokenizer.encode(prompt, add_special_tokens=True)
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response_ids = tokenizer.encode(response, add_special_tokens=False)
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max_len = config.preprocessing.max_seq_len
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full_ids = (prompt_ids + response_ids)[:max_len]
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prompt_action = config.mask.get("prompt", config.mask_default)
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response_action = config.mask.get("response", config.mask_default)
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p_len = min(len(prompt_ids), len(full_ids))
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r_len = len(full_ids) - p_len
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loss_mask = []
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if prompt_action == "train":
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loss_mask += [1] * p_len
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else:
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loss_mask += [0] * p_len
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if response_action == "train":
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loss_mask += [1] * r_len
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else:
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loss_mask += [0] * r_len
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return {
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"ids": full_ids,
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"loss_mask": loss_mask,
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"domain": _extract_domain(item, config.output.domain_key),
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}
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@MaskBuilderFactory.register("text")
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class TextMaskBuilder(BaseMaskBuilder):
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"""Plain tokenisation — no mask, used for pre-training data."""
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def build(self, item: dict, config, tokenizer) -> Optional[dict]:
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text = item.get(config.input.text_key, "")
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if not isinstance(text, str) or not text.strip():
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return None
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pp = config.preprocessing
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if not (pp.min_chars <= len(text) <= pp.max_chars):
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return None
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ids = tokenizer.encode(text, add_special_tokens=True)
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ids = ids[: pp.max_seq_len]
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return {
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"ids": ids,
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"domain": _extract_domain(item, config.output.domain_key),
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}
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if not all(m == 1 for m in loss_mask):
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result["loss_mask"] = loss_mask
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return result
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