Spaces:
Running
Running
File size: 1,524 Bytes
68ca6da |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments
from datasets import Dataset
import pandas as pd
# Modell und Tokenizer laden
model = GPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# Daten vorbereiten
train_data = [
{"input_text": "Wie konfiguriere ich den Sprachassistenten?", "output_text": "Um den Sprachassistenten zu konfigurieren, gehen Sie zu den Einstellungen..."},
# Weitere Trainingsdaten hinzufügen
]
# Erstellen eines Dataset-Objekts
train_dataset = Dataset.from_pandas(pd.DataFrame(train_data))
# Daten tokenisieren
def tokenize_function(examples):
inputs = [example['input_text'] for example in examples]
outputs = [example['output_text'] for example in examples]
model_inputs = tokenizer(inputs, padding="max_length", truncation=True, max_length=128)
with tokenizer.as_target_tokenizer():
labels = tokenizer(outputs, padding="max_length", truncation=True, max_length=128)
model_inputs["labels"] = labels["input_ids"]
return model_inputs
tokenized_train_dataset = train_dataset.map(tokenize_function, batched=True)
# Trainingsparameter einstellen
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=3,
per_device_train_batch_size=4,
save_steps=10_000,
save_total_limit=2,
)
# Trainer initialisieren
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_train_dataset,
)
# Training starten
trainer.train() |