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from transformers import AutoTokenizer, AutoModelForQuestionAnswering, Trainer, TrainingArguments
from datasets import Dataset
import requests
pisyn = requests.get("https://raw.githubusercontent.com/Fixyres/FHeta/refs/heads/main/modules.json")
data = [
{"question": "Какая твоя база данных модулей? И по какой базе ты ищешь все модули?", "answer": pisyn.text}
]
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased")
dataset = Dataset.from_dict(data)
def preprocess_function(examples):
questions = examples["question"]
answers = examples["answer"]
inputs = tokenizer(questions, padding=True, truncation=True, return_tensors="pt")
with tokenizer.as_target_tokenizer():
labels = tokenizer(answers, padding=True, truncation=True, return_tensors="pt")
inputs["labels"] = labels["input_ids"]
return inputs
tokenized_datasets = dataset.map(preprocess_function, batched=True)
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=8,
logging_dir="./logs",
logging_steps=10,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets,
)
trainer.train()
model.save_pretrained("./FHeta")
tokenizer.save_pretrained("./FHeta")
tokenizer = AutoTokenizer.from_pretrained("./FHeta")
model = AutoModelForQuestionAnswering.from_pretrained("./FHeta")
def get_answer(query):
inputs = tokenizer(query, return_tensors="pt")
outputs = model(**inputs)
answer = tokenizer.decode(outputs["logits"][0], skip_special_tokens=True)
return answer
query = "Модуль FHeta"
answer = get_answer(query)
print(answer)
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