Berufsschule/VeracityAI
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VeracityAI
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base: Berufsschule:4af1d651da54cb4aaf89abb4b49d01f47d09fd52
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Berufsschule:main
Berufsschule:develop
Berufsschule:ml_model_integration
Berufsschule:database
Berufsschule:test_model
Berufsschule:docs_added
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compare: Berufsschule:5d8ec1a01feacf422b7dcb551741396b73af3a4f
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Berufsschule:develop
Berufsschule:ml_model_integration
Berufsschule:database
Berufsschule:test_model
Berufsschule:docs_added
Berufsschule:main

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src/model/train.py
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@ -67,7 +67,7 @@ class FakeNewsModelTrainer:
return TensorDataset(input_ids, attention_mask, labels), sampler return TensorDataset(input_ids, attention_mask, labels), sampler
def train(self, train_data, val_data, epochs=13, batch_size=16): def train(self, train_data, val_data, epochs=5, batch_size=32):
train_dataset, train_sampler = train_data train_dataset, train_sampler = train_data
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size)
val_dataloader = DataLoader(val_data, batch_size=batch_size, shuffle=False) val_dataloader = DataLoader(val_data, batch_size=batch_size, shuffle=False)
@ -76,8 +76,6 @@ class FakeNewsModelTrainer:
total_steps = len(train_dataloader) * epochs total_steps = len(train_dataloader) * epochs
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps) scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps)
self.model.to(self.device) # Ensure model is on the correct device
for epoch in range(epochs): for epoch in range(epochs):
self.model.train() self.model.train()
total_loss = 0 total_loss = 0
@ -130,7 +128,6 @@ class FakeNewsModelTrainer:
self.model.save_pretrained(path) self.model.save_pretrained(path)
self.tokenizer.save_pretrained(path) self.tokenizer.save_pretrained(path)
class FakeNewsInference: class FakeNewsInference:
def __init__(self, model_path): def __init__(self, model_path):
self.tokenizer = BertTokenizer.from_pretrained(model_path) self.tokenizer = BertTokenizer.from_pretrained(model_path)