121 lines
3.2 KiB
Python
121 lines
3.2 KiB
Python
import os
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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import torch
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import numpy as np
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from sentence_transformers import SentenceTransformer
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from datasets import Dataset
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from peft import LoraConfig, get_peft_model
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForSeq2Seq
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import weaviate
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from weaviate.client import WeaviateClient
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from weaviate.connect import ConnectionParams
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# 1️⃣ Inicjalizacja modelu do embeddingów
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embed_model = SentenceTransformer("all-MiniLM-L6-v2")
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# 2️⃣ Połączenie z Weaviate i pobranie dokumentów
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client = WeaviateClient(
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connection_params=ConnectionParams.from_params(
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http_host="weaviate",
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http_port=8080,
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http_secure=False,
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grpc_host="weaviate",
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grpc_port=50051,
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grpc_secure=False,
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)
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)
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collection_name = "Document" # Zakładam, że to jest nazwa Twojej kolekcji
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result = (
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client.query.get(collection_name, ["content"])
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.with_additional(["id"])
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.do()
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)
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documents = [item['content'] for item in result['data']['Get'][collection_name]]
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# 3️⃣ Generowanie embeddingów
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embeddings = embed_model.encode(documents)
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# 4️⃣ Przygotowanie danych treningowych
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def create_training_data():
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data = {
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"text": documents,
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"embedding": embeddings.tolist()
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}
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return Dataset.from_dict(data)
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dataset = create_training_data()
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# Podział danych na treningowe i ewaluacyjne
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split_dataset = dataset.train_test_split(test_size=0.25)
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train_dataset = split_dataset["train"]
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eval_dataset = split_dataset["test"]
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# 5️⃣ Ładowanie modelu allegro/multislav-5lang
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_name = "allegro/multislav-5lang"
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# 6️⃣ Konfiguracja LoRA
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lora_config = LoraConfig(
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r=8, lora_alpha=32, lora_dropout=0.1, bias="none", task_type="SEQ_2_SEQ_LM"
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)
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model = get_peft_model(model, lora_config)
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# 7️⃣ Tokenizacja danych
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max_length = 384
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def tokenize_function(examples):
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return tokenizer(
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examples["text"],
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padding="max_length",
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truncation=True,
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max_length=max_length
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)
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tokenized_train = train_dataset.map(tokenize_function, batched=True)
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tokenized_eval = eval_dataset.map(tokenize_function, batched=True)
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# 8️⃣ Parametry treningu
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training_args = TrainingArguments(
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output_dir="./results",
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eval_strategy="steps",
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eval_steps=500,
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save_strategy="steps",
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save_steps=500,
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learning_rate=1e-5,
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per_device_train_batch_size=2,
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per_device_eval_batch_size=2,
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num_train_epochs=16,
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weight_decay=0.01,
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load_best_model_at_end=True,
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metric_for_best_model="loss",
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greater_is_better=False,
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)
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# 9️⃣ Data Collator
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data_collator = DataCollatorForSeq2Seq(
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tokenizer=tokenizer,
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model=model
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)
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# 🔟 Trening modelu
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=tokenized_train,
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eval_dataset=tokenized_eval,
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data_collator=data_collator,
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)
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trainer.train()
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# 1️⃣1️⃣ Zapis modelu
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model.save_pretrained("./models/allegro")
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tokenizer.save_pretrained("./models/allegro")
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print("✅ Model został wytrenowany i zapisany!")
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