Found via `codespell -S .mypy_cache,yarn.lock,*.json,*.ipynb -L
rouge,nam,vie`
This commit is contained in:
Kian-Meng Ang
2023-02-05 20:18:03 +01:00
committed by GitHub
parent 364a4f5aa3
commit 1e321a6fca
32 changed files with 50 additions and 50 deletions
+1 -1
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@@ -18,7 +18,7 @@ async def get_text_toxicity(
Args:
msg (str): the message that we want to analyze.
api_client (ApiClient, optional): authentification of the user of the request.
api_client (ApiClient, optional): authentication of the user of the request.
Defaults to Depends(deps.get_trusted_api_client).
Returns:
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@@ -14,7 +14,7 @@ class TreeManagerConfiguration(BaseModel):
number is reached."""
max_initial_prompt_review: int = 100
"""Maximum number of initial prompts under review before no more inital prompt tasks will be handed out."""
"""Maximum number of initial prompts under review before no more initial prompt tasks will be handed out."""
max_tree_depth: int = 3
"""Maximum depth of message tree."""
@@ -75,7 +75,7 @@ class TreeManagerConfiguration(BaseModel):
min_active_rankings_per_lang: int = 0
"""When the number of active ranking tasks is below this value when a tree enters a terminal
state an available trees in BACKLOG_RANKING will be actived (i.e. enters the RANKING state)."""
state an available trees in BACKLOG_RANKING will be activated (i.e. enters the RANKING state)."""
labels_initial_prompt: list[TextLabel] = [
TextLabel.spam,
@@ -12,7 +12,7 @@ class State(str, Enum):
"""States of the Open-Assistant message tree state machine."""
INITIAL_PROMPT_REVIEW = "initial_prompt_review"
"""In this state the message tree consists only of a single inital prompt root node.
"""In this state the message tree consists only of a single initial prompt root node.
Initial prompt labeling tasks will determine if the tree goes into `growing` or
`aborted_low_grade` state."""
@@ -33,11 +33,11 @@ class State(str, Enum):
compute the aggergated ranking scores that will appear in the dataset."""
READY_FOR_EXPORT = "ready_for_export"
"""The Scoring algorithm computed rankings scores for all childern. The message tree can be
"""The Scoring algorithm computed rankings scores for all children. The message tree can be
exported as part of an Open-Assistant message tree dataset."""
SCORING_FAILED = "scoring_failed"
"""An exception occured in the scoring algorithm."""
"""An exception occurred in the scoring algorithm."""
ABORTED_LOW_GRADE = "aborted_low_grade"
"""The system received too many bad reviews and stopped handing out tasks for this message tree."""
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@@ -484,7 +484,7 @@ class PromptRepository:
OasstErrorCode.TASK_PAYLOAD_TYPE_MISMATCH,
)
logger.debug(f"text_labels relpy: {valid_labels=}, {mandatory_labels=}")
logger.debug(f"text_labels reply: {valid_labels=}, {mandatory_labels=}")
if valid_labels:
if not all([label in valid_labels for label in text_labels.labels.keys()]):
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@@ -177,7 +177,7 @@ class TaskRepository:
if not allow_personal_tasks and not task.collective:
raise OasstError("This is not a collective task", OasstErrorCode.TASK_NOT_COLLECTIVE)
if task.done:
raise OasstError("Allready closed", OasstErrorCode.TASK_ALREADY_DONE)
raise OasstError("Already closed", OasstErrorCode.TASK_ALREADY_DONE)
task.done = True
self.db.add(task)
+6 -6
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@@ -320,7 +320,7 @@ class TreeManager:
if num_red_flag is not None and num_red_flag >= self.cfg.auto_mod_red_flags:
if m.parent_id is None:
logger.warning(
f"[AUTO MOD] Halting tree {m.message_tree_id}, inital prompt got too many red flags ({m.emojis})."
f"[AUTO MOD] Halting tree {m.message_tree_id}, initial prompt got too many red flags ({m.emojis})."
)
self.enter_low_grade_state(m.message_tree_id)
else:
@@ -895,7 +895,7 @@ class TreeManager:
logger.warning("The intersection of ranking results ID sets has less than two elements. Skipping.")
continue
# keep only elements in commond set
# keep only elements in command set
ordered_ids_list = [list(filter(lambda x: x in common_set, ids)) for ids in ordered_ids_list]
assert all(len(x) == len(common_set) for x in ordered_ids_list)
@@ -1069,7 +1069,7 @@ HAVING(COUNT(mr.message_id) FILTER (WHERE mr.user_id = :user_id) = 0)
"""
def query_incomplete_rankings(self, lang: str) -> list[IncompleteRankingsRow]:
"""Query parents which have childern that need further rankings"""
"""Query parents which have children that need further rankings"""
user_id = self.pr.user_id if not settings.DEBUG_ALLOW_DUPLICATE_TASKS else None
r = self.db.execute(
@@ -1256,7 +1256,7 @@ LEFT JOIN message_reaction mr ON mr.task_id = t.id AND mr.payload_type = 'Rankin
@managed_tx_method(CommitMode.COMMIT)
def ensure_tree_states(self) -> None:
"""Add message tree state rows for all root nodes (inital prompt messages)."""
"""Add message tree state rows for all root nodes (initial prompt messages)."""
missing_tree_ids = self.query_misssing_tree_states()
for id in missing_tree_ids:
@@ -1598,7 +1598,7 @@ DELETE FROM message WHERE message_tree_id = :message_tree_id;
total_messages = sum(len(x) for x in replies_by_tree.values())
logger.debug(f"found: {len(replies_by_tree)} trees; {len(prompts)} prompts; {total_messages} messages;")
# remove all trees based on inital prompts of the user
# remove all trees based on initial prompts of the user
if purge_initial_prompts:
for p in prompts:
self.purge_message_tree(p.message_tree_id)
@@ -1636,7 +1636,7 @@ DELETE FROM message WHERE message_tree_id = :message_tree_id;
logger.debug(f"purging message: {m.id}")
self._purge_message_internal(m.id)
# update childern counts
# update children counts
self.pr.update_children_counts(m.message_tree_id)
# reactivate tree
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@@ -66,7 +66,7 @@ def get_winner(pairs):
def get_ranking(pairs):
"""
Abuses concordance property to get a (not necessarily unqiue) ranking.
Abuses concordance property to get a (not necessarily unique) ranking.
The lack of uniqueness is due to the potential existence of multiple
equally ranked winners. We have to pick one, which is where
the non-uniqueness comes from