feat: attempt to detect looping in CAI logs and discard from final dataset
This commit is contained in:
parent
aebd405bbd
commit
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pdm.lock
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pdm.lock
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@ -41,6 +41,12 @@ version = "5.10.1"
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{url = "https://files.pythonhosted.org/packages/59/ef/d54d17c36b46a9b8f6e1d4bf039b7f7ad236504cfb13cf1872caec9cbeaa/scipy-1.9.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:d644a64e174c16cb4b2e41dfea6af722053e83d066da7343f333a54dae9bc31c"},
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||||
{url = "https://files.pythonhosted.org/packages/84/86/4f38fa30c112c3590954420f85d95b8cd23811ecc5cfc4bfd4d988d4db44/scipy-1.9.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5a04cd7d0d3eff6ea4719371cbc44df31411862b9646db617c99718ff68d4840"},
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{url = "https://files.pythonhosted.org/packages/92/f9/7ae2c1ae200212bc84b5a8369a10d644aa8b588140fe292d59db3b4a2545/scipy-1.9.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:abaf921531b5aeaafced90157db505e10345e45038c39e5d9b6c7922d68085cb"},
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||||
{url = "https://files.pythonhosted.org/packages/b5/67/c5451465ec94e654e6315cd5136961d267ae94a0f799b85d26eb9efe4c9f/scipy-1.9.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4db5b30849606a95dcf519763dd3ab6fe9bd91df49eba517359e450a7d80ce2e"},
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||||
{url = "https://files.pythonhosted.org/packages/bb/b7/380c9e4cd71263f03d16f8a92c0e44c9bdef38777e1a7dde1f47ba996bac/scipy-1.9.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c68db6b290cbd4049012990d7fe71a2abd9ffbe82c0056ebe0f01df8be5436b0"},
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||||
{url = "https://files.pythonhosted.org/packages/c3/3e/e40c52775a5d19abd43b1c245fbc5dee283a29acc45c830bc73bfad9468b/scipy-1.9.3-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:90453d2b93ea82a9f434e4e1cba043e779ff67b92f7a0e85d05d286a3625df3c"},
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||||
{url = "https://files.pythonhosted.org/packages/c8/0f/d9f8c50be8670b7ba6f002679e84cd18f46a23faf62c1590f4d1bbec0c8c/scipy-1.9.3-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:da8245491d73ed0a994ed9c2e380fd058ce2fa8a18da204681f2fe1f57f98f95"},
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{url = "https://files.pythonhosted.org/packages/ce/28/635391e72e24bd3f4a91e374f4a186a5e4ecc95f23d8a55c9b0d25777cf7/scipy-1.9.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1a72d885fa44247f92743fc20732ae55564ff2a519e8302fb7e18717c5355a8b"},
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||||
{url = "https://files.pythonhosted.org/packages/cf/0e/3f1685c1fcb5dfe35ec027a5fc7a29e8818c61b2cc7fa207b4fc7b959f52/scipy-1.9.3-cp310-cp310-win_amd64.whl", hash = "sha256:68239b6aa6f9c593da8be1509a05cb7f9efe98b80f43a5861cd24c7557e98523"},
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{url = "https://files.pythonhosted.org/packages/d0/96/4f6eac3fea18f836a0e403539556b1684e6f3361fa39aa5d5797dedecd75/scipy-1.9.3-cp39-cp39-win_amd64.whl", hash = "sha256:5b88e6d91ad9d59478fafe92a7c757d00c59e3bdc3331be8ada76a4f8d683f58"},
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{url = "https://files.pythonhosted.org/packages/df/75/c0254dc58d1f1b00f9d3dbda029743b71b815dd512461ed20d9b7f459e37/scipy-1.9.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b41bc822679ad1c9a5f023bc93f6d0543129ca0f37c1ce294dd9d386f0a21096"},
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||||
{url = "https://files.pythonhosted.org/packages/f4/9d/882134b1e774a9227ab855c71a39612194e1106185595417ce92f0f1e78c/scipy-1.9.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0d54222d7a3ba6022fdf5773931b5d7c56efe41ede7f7128c7b1637700409108"},
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||||
{url = "https://files.pythonhosted.org/packages/f9/37/5cd44af74d7178a44452b17ea162bc93996d5555b4a978877d2efd56fe84/scipy-1.9.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:83c06e62a390a9167da60bedd4575a14c1f58ca9dfde59830fc42e5197283dab"},
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||||
{url = "https://files.pythonhosted.org/packages/fb/ba/1733dbbc19f2aa07d100cfa220bcc83a3977bc5c9f0a5ad262dae1f3ab90/scipy-1.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1884b66a54887e21addf9c16fb588720a8309a57b2e258ae1c7986d4444d3bc0"},
|
||||
]
|
||||
"threadpoolctl 3.1.0" = [
|
||||
{url = "https://files.pythonhosted.org/packages/1b/c7/3d85f8b3894ba7228d0c74e16e97a36a72b2cd2b0e0f8f89b5d435d11f71/threadpoolctl-3.1.0.tar.gz", hash = "sha256:a335baacfaa4400ae1f0d8e3a58d6674d2f8828e3716bb2802c44955ad391380"},
|
||||
{url = "https://files.pythonhosted.org/packages/61/cf/6e354304bcb9c6413c4e02a747b600061c21d38ba51e7e544ac7bc66aecc/threadpoolctl-3.1.0-py3-none-any.whl", hash = "sha256:8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b"},
|
||||
]
|
||||
"toml 0.10.2" = [
|
||||
{url = "https://files.pythonhosted.org/packages/44/6f/7120676b6d73228c96e17f1f794d8ab046fc910d781c8d151120c3f1569e/toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
|
||||
{url = "https://files.pythonhosted.org/packages/be/ba/1f744cdc819428fc6b5084ec34d9b30660f6f9daaf70eead706e3203ec3c/toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
|
||||
|
|
|
|||
|
|
@ -12,6 +12,8 @@ license = {text = "AGPL-3.0-only"}
|
|||
dependencies = [
|
||||
"ijson>=3.1.4",
|
||||
"mashumaro>=3.2",
|
||||
"regex>=2022.10.31",
|
||||
"scikit-learn>=1.2.0",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
|
|
|
|||
|
|
@ -14,6 +14,10 @@ logger = logging.getLogger(__name__)
|
|||
# the user's input).
|
||||
MIN_EPISODE_LEN = 3
|
||||
|
||||
# Discard episodes where the average similarity between the bot's messages is
|
||||
# higher than this value.
|
||||
EPISODE_SIMILARITY_THRESHOLD = 0.55
|
||||
|
||||
#
|
||||
# So here's a quick rundown of what needs to happen. We have a limited context
|
||||
# window (of 2048 tokens, ATM) and for the Persona Dialogue Module (PDM), we
|
||||
|
|
@ -62,12 +66,15 @@ class CharacterAiPDM(BaseModule):
|
|||
# Now, start adding messages and break episodes apart if they get
|
||||
# too big.
|
||||
turns = base_turns.copy()
|
||||
bot_messages: list[str] = []
|
||||
|
||||
for raw_message in chat.messages:
|
||||
message_text = _process_message(raw_message.text)
|
||||
if raw_message.is_human:
|
||||
message = f"{PromptConstants.USER_PREFIX}: {message_text}"
|
||||
else:
|
||||
message = f"{chat.bot.name}: {message_text}"
|
||||
bot_messages.append(message_text)
|
||||
turns.append(message)
|
||||
|
||||
# Splitting logic.
|
||||
|
|
@ -78,15 +85,34 @@ class CharacterAiPDM(BaseModule):
|
|||
cur_episode_len,
|
||||
PromptConstants.TARGET_WORD_COUNT_PER_EPISODE)
|
||||
|
||||
# Calculate similarity between sequential bot message pairs
|
||||
# within this episode, and drop it if it goes above the
|
||||
# defined threshold.
|
||||
similarity_score_matrix = _calculate_similarity_scores(
|
||||
bot_messages)
|
||||
average_similarity_score_for_episode = 0.0
|
||||
for score in similarity_score_matrix[0]:
|
||||
if score == 1:
|
||||
continue
|
||||
average_similarity_score_for_episode += score
|
||||
average_similarity_score_for_episode /= 2
|
||||
|
||||
# Adding the last message made the episode go over the
|
||||
# target word count, so we return the episode without it...
|
||||
removed_turn = turns.pop()
|
||||
yield "\n".join(turns)
|
||||
if average_similarity_score_for_episode <= EPISODE_SIMILARITY_THRESHOLD:
|
||||
yield "\n".join(turns)
|
||||
else:
|
||||
logger.debug(
|
||||
"Ignoring episode due to high similarity between messages (%s > %s)",
|
||||
average_similarity_score_for_episode,
|
||||
EPISODE_SIMILARITY_THRESHOLD)
|
||||
|
||||
# ...and start the next episode with the message we had to
|
||||
# trim out from this one.
|
||||
turns = base_turns.copy()
|
||||
turns.append(removed_turn)
|
||||
bot_messages = []
|
||||
|
||||
|
||||
#
|
||||
|
|
@ -110,6 +136,24 @@ def _process_message(original_string: str) -> str:
|
|||
return string.strip()
|
||||
|
||||
|
||||
def _calculate_similarity_scores(bot_turns: list[str]) -> t.Any:
|
||||
'''
|
||||
Calculates similarity scores between bot turns.
|
||||
|
||||
This is a roundabout way to try and _possibly_ detect the post-1.1 CAI
|
||||
looping behavior so we can handle it during the data preprocessing.
|
||||
'''
|
||||
from sklearn.metrics.pairwise import cosine_similarity
|
||||
from sklearn.feature_extraction.text import CountVectorizer
|
||||
|
||||
vectorizer = CountVectorizer()
|
||||
x = vectorizer.fit_transform(bot_turns)
|
||||
arr = x.toarray()
|
||||
|
||||
sims = cosine_similarity(arr)
|
||||
return sims
|
||||
|
||||
|
||||
def _parse_definitions_for(bot_name: str,
|
||||
raw_definitions: str) -> t.Tuple[str, list[str]]:
|
||||
'''
|
||||
|
|
|
|||
Loading…
Reference in New Issue