102 lines
2.8 KiB
Python
102 lines
2.8 KiB
Python
import os
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import torch
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import weaviate
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import numpy as np
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from datasets import Dataset
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments, Trainer, DataCollatorForSeq2Seq
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from weaviate.connect import ConnectionParams
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import weaviate
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# 1️⃣ Połączenie z Weaviate
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client = weaviate.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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client.connect()
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# 2️⃣ Pobranie dokumentów z Weaviate
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def fetch_documents():
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collection = client.collections.get("Document")
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response = collection.query.fetch_objects()
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documents = []
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for item in response["data"]["Get"]["Document"]:
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file_name = item.get("fileName", "unknown_file")
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content = item.get("content", "")
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if content:
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documents.append(f"fileName: {file_name}, content: {content}")
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print(f"fileName: {file_name}")
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return documents
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documents = fetch_documents()
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# 3️⃣ Inicjalizacja modelu
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model_name = "allegro/multislav-5lang"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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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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# 4️⃣ Przygotowanie danych treningowych
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def create_training_data():
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return Dataset.from_dict({"text": documents})
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dataset = create_training_data()
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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 Tokenizacja
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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=512
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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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# 6️⃣ Parametry treningu
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training_args = TrainingArguments(
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output_dir="./results",
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evaluation_strategy="steps",
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eval_steps=500,
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save_steps=500,
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learning_rate=2e-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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save_total_limit=2,
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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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# 7️⃣ Data Collator
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data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
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# 8️⃣ Trening
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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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# 9️⃣ 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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