mod gpt

gpt.py

@@ -2,66 +2,37 @@ import os
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
 from datasets import Dataset
-from collections import defaultdict
 
 # Konfiguracja
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
-MODEL_NAME = "gpt2"  # Tymczasowo używamy mniejszego modelu do testów
+MODEL_NAME = "gpt2"
 SPECIAL_TOKENS = ["[CITATION_START]", "[CITATION_END]"]
 
-class SourceMapper:
-    def __init__(self):
-        self.source_to_idx = defaultdict(lambda: len(self.source_to_idx))
-        self.idx_to_source = {}
-
-    def add_source(self, source):
-        if source not in self.source_to_idx:
-            idx = self.source_to_idx[source]
-            self.idx_to_source[idx] = source
-
 def prepare_simple_dataset():
-    # Przykładowe dane - zastąp rzeczywistymi danymi
     return [
-        {
+        {"text": "[CITATION_START] Kodeks Pracy, Art. 1 [CITATION_END] Tekst artykułu..."},
-            "text": "[CITATION_START] Kodeks Pracy, Art. 1 [CITATION_END] Tekst artykułu...",
+        {"text": "[CITATION_START] Kodeks Pracy, Art. 2 [CITATION_END] Inny tekst..."}
-            "source_idx": 0
-        },
-        {
-            "text": "[CITATION_START] Kodeks Pracy, Art. 2 [CITATION_END] Inny tekst...",
-            "source_idx": 1
-        }
     ]
 
 def main():
-    # Inicjalizacja
+    # Inicjalizacja tokenizera
     tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
     tokenizer.add_special_tokens({"additional_special_tokens": SPECIAL_TOKENS})
     tokenizer.pad_token = tokenizer.eos_token
 
     # Przygotowanie danych
-    source_mapper = SourceMapper()
     data = prepare_simple_dataset()
-    
+    dataset = Dataset.from_dict({"text": [d["text"] for d in data]})
-    # Tworzenie datasetu
-    dataset = Dataset.from_dict({
-        "text": [d["text"] for d in data],
-        "source_idx": [d["source_idx"] for d in data]
-    })
 
     # Tokenizacja
     def tokenize_function(examples):
-        tokenized = tokenizer(
+        return tokenizer(
             examples["text"],
             truncation=True,
             padding="max_length",
             max_length=128,
             return_tensors="pt"
         )
-        return {
-            "input_ids": tokenized["input_ids"].squeeze(),
-            "attention_mask": tokenized["attention_mask"].squeeze(),
-            "labels": tokenized["input_ids"].squeeze().clone(),
-        }
 
     tokenized_dataset = dataset.map(tokenize_function, batched=True)
 
@@ -72,12 +43,10 @@ def main():
     # Konfiguracja treningu
     training_args = TrainingArguments(
         output_dir="./results",
-        num_train_epochs=1,
+        num_train_pochs=1,
         per_device_train_batch_size=2,
-        gradient_accumulation_steps=1,
+        remove_unused_columns=True,  # Kluczowa zmiana
-        learning_rate=2e-5,
+        logging_steps=1
-        logging_steps=1,
-        remove_unused_columns=False
     )
 
     # Trainer
@@ -87,7 +56,6 @@ def main():
         train_dataset=tokenized_dataset,
     )
 
-    # Rozpoczęcie treningu
     print("Rozpoczęcie treningu...")
     trainer.train()