From 093db658b37410b796f6ebc5501ac3827df120e1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=9C=D0=B0=D0=B7=D1=83=D1=80=20=D0=93=D1=80=D0=B5=D1=82?=
 =?UTF-8?q?=D0=B0=20=D0=95=D0=B2=D0=B3=D0=B5=D0=BD=D1=8C=D0=B5=D0=B2=D0=BD?=
 =?UTF-8?q?=D0=B0?= <gemazur_1@edu.hse.ru>
Date: Tue, 25 Mar 2025 18:57:18 +0300
Subject: [PATCH] micro zapusk no cross

---
 .ipynb_checkpoints/checkLora-checkpoint.py | 20 ++++++++++++++++++--
 checkLora.py                               | 20 ++++++++++++++++++--
 2 files changed, 36 insertions(+), 4 deletions(-)

diff --git a/.ipynb_checkpoints/checkLora-checkpoint.py b/.ipynb_checkpoints/checkLora-checkpoint.py
index 7751d3e..0498167 100644
--- a/.ipynb_checkpoints/checkLora-checkpoint.py
+++ b/.ipynb_checkpoints/checkLora-checkpoint.py
@@ -125,8 +125,25 @@
 
 
 import torch
+# from transformers import BertTokenizer, BertModel
+# from peft import PeftModel
+
+# # Определяем устройство
+# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# # Загружаем базовую модель BERT
+# base_model = BertModel.from_pretrained("bert-base-uncased").to(device)
+
+# # Подключаем адаптер LoRA
+# model = PeftModel.from_pretrained(base_model, "./micro_no_cross_fine_tuned").to(device)
+# model.eval()
+
+# # Загружаем токенизатор
+# tokenizer = BertTokenizer.from_pretrained("./micro_no_cross_fine_tuned")
+
 from peft import PeftModel
 from transformers import BertTokenizer, BertConfig
+from micro_no_cross import MultiTaskBert
 
 # Загружаем базовую конфигурацию
 config = BertConfig.from_pretrained("bert-base-uncased")
@@ -142,8 +159,7 @@ tokenizer = BertTokenizer.from_pretrained("./micro_no_cross_fine_tuned")
 
 model.to(device)
 model.eval()
-# Загружаем токенизатор
-tokenizer = BertTokenizer.from_pretrained("./micro_no_cross_fine_tuned")
+
 
 def predict(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512).to(device)
diff --git a/checkLora.py b/checkLora.py
index 7751d3e..0498167 100644
--- a/checkLora.py
+++ b/checkLora.py
@@ -125,8 +125,25 @@
 
 
 import torch
+# from transformers import BertTokenizer, BertModel
+# from peft import PeftModel
+
+# # Определяем устройство
+# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# # Загружаем базовую модель BERT
+# base_model = BertModel.from_pretrained("bert-base-uncased").to(device)
+
+# # Подключаем адаптер LoRA
+# model = PeftModel.from_pretrained(base_model, "./micro_no_cross_fine_tuned").to(device)
+# model.eval()
+
+# # Загружаем токенизатор
+# tokenizer = BertTokenizer.from_pretrained("./micro_no_cross_fine_tuned")
+
 from peft import PeftModel
 from transformers import BertTokenizer, BertConfig
+from micro_no_cross import MultiTaskBert
 
 # Загружаем базовую конфигурацию
 config = BertConfig.from_pretrained("bert-base-uncased")
@@ -142,8 +159,7 @@ tokenizer = BertTokenizer.from_pretrained("./micro_no_cross_fine_tuned")
 
 model.to(device)
 model.eval()
-# Загружаем токенизатор
-tokenizer = BertTokenizer.from_pretrained("./micro_no_cross_fine_tuned")
+
 
 def predict(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512).to(device)
-- 
GitLab