mod

hft.py

@@ -1,7 +1,7 @@
 import os
 import torch
 import torch.nn as nn
-from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
 from datasets import Dataset
 import re
 import json
@@ -11,6 +11,7 @@ import pytesseract
 from PIL import Image
 from collections import defaultdict
 from huggingface_hub import login
+from transformers import DataCollatorForLanguageModeling
 
 # Konfiguracja
 os.environ['TORCH_USE_CUDA_DSA'] = '1'
@@ -179,6 +180,17 @@ class CustomTrainer(Trainer):
         outputs = model(**inputs, source_idx=source_idx)
         return (outputs.loss, outputs) if return_outputs else outputs.loss
 
+class CustomDataCollator(DataCollatorForLanguageModeling):
+    def torch_call(self, examples):
+        batch = super().torch_call(examples)
+        
+        # Dodanie source_idx do batcha
+        if "source_idx" in examples[0]:
+            source_idx = [ex["source_idx"] for ex in examples]
+            batch["source_idx"] = torch.tensor(source_idx, dtype=torch.long)
+            
+        return batch
+
 def main():
     source_mapper = SourceMapper()
     model_name = "crumb/nano-mistral"
@@ -193,8 +205,7 @@ def main():
         print("\nBrak danych do treningu!")
         return
 
-    dataset = Dataset.from_list(data)
-
+    # Przygotowanie datasetu
     def tokenize_function(examples):
         tokenized = tokenizer(
             examples["text"],
@@ -204,19 +215,16 @@ def main():
             return_tensors="pt"
         )
         return {
-            "input_ids": tokenized["input_ids"][0],
-            "attention_mask": tokenized["attention_mask"][0],
-            "labels": tokenized["input_ids"][0].clone(),
+            "input_ids": tokenized["input_ids"].squeeze(),
+            "attention_mask": tokenized["attention_mask"].squeeze(),
+            "labels": tokenized["input_ids"].squeeze().clone(),
             "source_idx": examples["source_idx"]
         }
 
-    tokenized_dataset = dataset.map(tokenize_function, batched=False)
-
-    data_collator = DataCollatorForLanguageModeling(
-        tokenizer=tokenizer,
-        mlm=False
-    )
+    dataset = Dataset.from_list(data)
+    tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=16)
 
+    # Model i trening
     model = CustomModel(model_name, AutoModelForCausalLM.from_pretrained(model_name).config)
     model.source_mapper = source_mapper
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -240,7 +248,7 @@ def main():
         model=model,
         args=training_args,
         train_dataset=tokenized_dataset,
-        data_collator=data_collator,
+        data_collator=CustomDataCollator(tokenizer=tokenizer, mlm=False),
         tokenizer=tokenizer
     )