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RHOAIENG-12605: Update JPype to 1.5.0 #216

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Merged
merged 5 commits into from
Sep 9, 2024
Merged

RHOAIENG-12605: Update JPype to 1.5.0 #216

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5576ff2
Update JPype to 1.5.0
ruivieira Sep 7, 2024
b164662
Add Python 3.11 to GHAs
ruivieira Sep 7, 2024
541e4cf
Fix lint error
ruivieira Sep 7, 2024
5d49d74
Workaround Pylint error
ruivieira Sep 7, 2024
21fa9cb
Fix styling
ruivieira Sep 7, 2024
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2 changes: 1 addition & 1 deletion .github/workflows/workflow.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@ jobs:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: [ 3.8, 3.9 ]
python-version: [ 3.8, 3.9, 3.11 ]
java-version: [ 17 ]
maven-version: [ '3.8.6' ]
steps:
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4 changes: 2 additions & 2 deletions pyproject.toml
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Expand Up @@ -22,7 +22,7 @@ classifiers = [
]

dependencies = [
"Jpype1==1.4.1",
"Jpype1==1.5.0",
"pyarrow==17.0.0",
"matplotlib~=3.6.3",
"pandas~=1.5.3",
Expand All @@ -32,7 +32,7 @@ dependencies = [

[project.optional-dependencies]
dev = [
"JPype1==1.4.1",
"JPype1==1.5.0",
"black~=22.12.0",
"click==8.0.4",
"joblib~=1.2.0",
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10 changes: 6 additions & 4 deletions src/trustyai/language/detoxify/tmarco.py
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Expand Up @@ -104,14 +104,16 @@ def __init__(
"cuda" if torch.cuda.is_available() else "cpu"
)

def load_models(self, experts: list[str] = None, expert_weights: list = None):
def load_models(
self, experts: list[str] = None, expert_weights: list = None
): # pylint: disable=unsubscriptable-object
"""Load expert models."""
if expert_weights is not None:
self.expert_weights = expert_weights
expert_models = []
for expert in experts:
# Load TMaRCO models
if (expert == "trustyai/gplus" or expert == "trustyai/gminus"):
if expert in ["trustyai/gplus", "trustyai/gminus"]:
expert = BartForConditionalGeneration.from_pretrained(
expert,
forced_bos_token_id=self.tokenizer.bos_token_id,
Expand All @@ -122,14 +124,14 @@ def load_models(self, experts: list[str] = None, expert_weights: list = None):
expert = AutoModelForMaskedLM.from_pretrained(
expert,
forced_bos_token_id=self.tokenizer.bos_token_id,
device_map = "auto"
device_map="auto",
)
# Load HuggingFace models
else:
expert = AutoModelForCausalLM.from_pretrained(
expert,
forced_bos_token_id=self.tokenizer.bos_token_id,
device_map = "auto"
device_map="auto",
)
expert_models.append(expert)
self.experts = expert_models
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