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add trust_remote_code=True #20

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Merged
merged 1 commit into from
Jun 14, 2024
Merged

add trust_remote_code=True #20

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15 changes: 12 additions & 3 deletions template/steps/dataset_loader/data_loader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,14 +32,23 @@ def data_loader(

# Load dataset based on the dataset value
{%- if dataset == 'financial_news' %}
dataset = load_dataset("zeroshot/twitter-financial-news-sentiment")
dataset = load_dataset(
"zeroshot/twitter-financial-news-sentiment",
trust_remote_code=True,
)
{%- endif %}
{%- if dataset == 'imdb_reviews' %}
dataset = load_dataset("imdb")["train"]
dataset = load_dataset(
"imdb",
trust_remote_code=True,
)["train"]
dataset = dataset.train_test_split(test_size=0.25, shuffle=True)
{%- endif %}
{%- if dataset == 'airline_reviews' %}
dataset = load_dataset("Shayanvsf/US_Airline_Sentiment")
dataset = load_dataset(
"Shayanvsf/US_Airline_Sentiment",
trust_remote_code=True,
)
dataset = dataset.rename_column("airline_sentiment", "label")
dataset = dataset.remove_columns(["airline_sentiment_confidence","negativereason_confidence"])
{%- endif %}
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6 changes: 3 additions & 3 deletions template/utils/misc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,13 +18,13 @@ def compute_metrics(eval_pred: Tuple[np.ndarray, np.ndarray]) -> Dict[str, float
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
# calculate the mertic using the predicted and true value
accuracy = load_metric("accuracy").compute(
accuracy = load_metric("accuracy", trust_remote_code=True).compute(
predictions=predictions, references=labels
)
f1 = load_metric("f1").compute(
f1 = load_metric("f1", trust_remote_code=True).compute(
predictions=predictions, references=labels, average="weighted"
)
precision = load_metric("precision").compute(
precision = load_metric("precision", trust_remote_code=True).compute(
predictions=predictions, references=labels, average="weighted"
)
return {
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