diff --git a/models/README.md b/models/README.md
index 7ae4982..eeeb4d9 100644
--- a/models/README.md
+++ b/models/README.md
@@ -45,7 +45,7 @@ Put [model files]() here:
 ./
 ├── Layout
 │   ├── config.json
-│   └── weights.pth
+│   └── model_final.pth
 ├── MFD
 │   └── weights.pt
 ├── MFR
diff --git a/modules/layoutlmv3/layoutlmv3_base_inference.yaml b/modules/layoutlmv3/layoutlmv3_base_inference.yaml
index 6f47cb9..3e0fd30 100644
--- a/modules/layoutlmv3/layoutlmv3_base_inference.yaml
+++ b/modules/layoutlmv3/layoutlmv3_base_inference.yaml
@@ -1,6 +1,6 @@
 AUG:
   DETR: true
-CACHE_DIR: /mnt/localdata/users/yupanhuang/cache/huggingface
+CACHE_DIR: ~/cache/huggingface
 CUDNN_BENCHMARK: false
 DATALOADER:
   ASPECT_RATIO_GROUPING: true
@@ -294,7 +294,7 @@ MODEL:
     POS_TYPE: abs
   WEIGHTS: 
 OUTPUT_DIR: 
-SCIHUB_DATA_DIR_TRAIN: /mnt/petrelfs/share_data/zhaozhiyuan/publaynet/layout_scihub/train
+SCIHUB_DATA_DIR_TRAIN: ~/publaynet/layout_scihub/train
 SEED: 42
 SOLVER:
   AMP:
diff --git a/pdf_extract.py b/pdf_extract.py
index 3dccf1f..603a577 100644
--- a/pdf_extract.py
+++ b/pdf_extract.py
@@ -113,14 +113,13 @@ def __getitem__(self, idx):
         if img_list is None:
             continue
         print("pdf index:", idx, "pages:", len(img_list))
-        # layout检测 + 公式检测
+        # layout detection and formula detection
         doc_layout_result = []
         latex_filling_list = []
         mf_image_list = []
         for idx, image in enumerate(img_list):
             img_H, img_W = image.shape[0], image.shape[1]
             layout_res = layout_model(image, ignore_catids=[])
-            # 公式检测
             mfd_res = mfd_model.predict(image, imgsz=img_size, conf=conf_thres, iou=iou_thres, verbose=True)[0]
             for xyxy, conf, cla in zip(mfd_res.boxes.xyxy.cpu(), mfd_res.boxes.conf.cpu(), mfd_res.boxes.cls.cpu()):
                 xmin, ymin, xmax, ymax = [int(p.item()) for p in xyxy]
@@ -142,7 +141,7 @@ def __getitem__(self, idx):
             )
             doc_layout_result.append(layout_res)
             
-        # 公式识别，因为识别速度较慢，为了提速，把单个pdf的所有公式裁剪完，一起批量做识别。  
+        # Formula recognition, collect all formula images in whole pdf file, then batch infer them.
         a = time.time()  
         dataset = MathDataset(mf_image_list, transform=mfr_transform)
         dataloader = DataLoader(dataset, batch_size=128, num_workers=32)
@@ -156,7 +155,7 @@ def __getitem__(self, idx):
         b = time.time()
         print("formula nums:", len(mf_image_list), "mfr time:", round(b-a, 2))
             
-        # ocr识别
+        # ocr
         for idx, image in enumerate(img_list):
             pil_img = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
             single_page_res = doc_layout_result[idx]['layout_dets']
@@ -169,7 +168,7 @@ def __getitem__(self, idx):
                         "bbox": [xmin, ymin, xmax, ymax],
                     })
             for res in single_page_res:
-                if int(res['category_id']) in [0, 1, 2, 4, 6, 7]:  #需要进行ocr的类别
+                if int(res['category_id']) in [0, 1, 2, 4, 6, 7]:  #categories that need to do ocr
                     xmin, ymin = int(res['poly'][0]), int(res['poly'][1])
                     xmax, ymax = int(res['poly'][4]), int(res['poly'][5])
                     crop_box = [xmin, ymin, xmax, ymax]
@@ -208,19 +207,19 @@ def __getitem__(self, idx):
                 draw = ImageDraw.Draw(vis_img)
                 for res in single_page_res:
                     label = int(res['category_id'])
-                    if label > 15:     # 筛选要可视化的类别
+                    if label > 15:     # categories that do not need visualize
                         continue
                     label_name = id2names[label]
                     x_min, y_min = int(res['poly'][0]), int(res['poly'][1])
                     x_max, y_max = int(res['poly'][4]), int(res['poly'][5])
                     if args.render and label in [13, 14, 15]:
                         try:
-                            if label in [13, 14]:  # 渲染公式
+                            if label in [13, 14]:  # render formula
                                 window_img = tex2pil(res['latex'])[0]
                             else:
-                                if True:           # 渲染中文
+                                if True:           # render chinese
                                     window_img = zhtext2pil(res['text'])
-                                else:              # 渲染英文
+                                else:              # render english
                                     window_img = tex2pil([res['text']], tex_type="text")[0]
                             ratio = min((x_max - x_min) / window_img.width, (y_max - y_min) / window_img.height) - 0.05
                             window_img = window_img.resize((int(window_img.width * ratio), int(window_img.height * ratio)))