Unstarted training

[BigDataMLexplorer]

Hi, I need help with training a sentence transformer. The whole configuration went well, but after calling trainer.train(), the training progress is not shown and there are no errors also. One GPU is a bit full, probably just loading the model, but training is not happening - there are no records in the output dir and the progress bar is not showing. Does anyone know what to do about it?

Here is my packages versions:
Name: datasets
Version: 3.6.0

Name: setfit
Version: 1.1.2

Name: sentence-transformers
Version: 4.1.0

Name: torch
Version: 2.7.1

Name: accelerate
Version: 1.8.0

Name: pandas
Version: 2.2.2

Here is my training script:

1) IMPORTS

import pandas as pd
from datasets import Dataset
from setfit import SetFitModel, Trainer, TrainingArguments
from sentence_transformers.losses import ContrastiveLoss, CosineSimilarityLoss
from transformers import TrainerCallback
from sklearn.metrics import accuracy_score, f1_score

2) Binary classification dataset

def load_excel(path):
df = pd.read_excel(path)
df = df.rename(columns={'labels': 'label'})
df = df.drop(columns=['Unnamed: 0'])
#df["label"] = df["label"].astype(int)
df['label'] = (df['label'] == 4).astype(int)
df["text"] = df["text"].astype(str)
print(df.head(10))
return Dataset.from_pandas(df[["text", "label"]])

train_ds = load_excel("xxx")
val_ds = load_excel("xx")

print(f"len of train_ds: {len(train_ds)}")
print(f"len of val_ds: {len(val_ds)}")

3) MODEL

model = SetFitModel.from_pretrained("xxx/simcse_small_e_czech") # local path
print("Model loaded successfully")

4) METRICS

def compute_metrics(y_pred, y_true):
return {
"accuracy": accuracy_score(y_true, y_pred),
"f1": f1_score(y_true, y_pred, average="macro")
}

5) CALLBACKS

class LogCallback(TrainerCallback):
def init(self, logfile="xxx/training_log.txt"):
self.logfile = logfile
def on_evaluate(self, args, state, control, metrics=None, **kw):
if metrics:
msg = (f"epoch={state.epoch:.0f} "
f"acc={metrics['accuracy']:.4f} "
f"f1={metrics['f1']:.4f}")
print(msg)
with open(self.logfile, "a") as f:
f.write(msg + "\n")

7) ARGUMENTS

training_args = TrainingArguments(
output_dir="xxx/setfit_checkpoints",
batch_size=16,
num_epochs=3,
body_learning_rate=1e-4,
loss=ContrastiveLoss,
logging_strategy="epoch",
evaluation_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="accuracy",
greater_is_better=True,
)

8) TRAINER

trainer = Trainer(
model=model,
train_dataset=train_ds,
eval_dataset=val_ds,
args=training_args,
metric=compute_metrics,
callbacks=[LogCallback()],
column_mapping={"text": "text", "label": "label"},
)

9) TRAINING

print("Training started")
trainer.train()

10) SAVE

print("Final metrics:", trainer.evaluate())
trainer.model.save_pretrained("final_model_contrastive")`