diff --git a/README2.md b/README2.md
new file mode 100644
index 0000000..b157d7d
--- /dev/null
+++ b/README2.md
@@ -0,0 +1,20 @@
+# Spoter Embeddings
+
+## Creating dataset
+First, make a folder where all you're videos are located. When this is done, all keypoints can be extracted from the videos using the following command. This will extract the keypoints and store them in \<path-to-landmarks-folder\>.
+```
+python3 preprocessing.py extract --videos-folder <path-to-videos-folder> --output-folder <path-to-landmarks-folder>
+```
+
+When this is done, the dataset can be created using the following command:
+```
+python3 preprocessing.py create --landmarks-dataset <path-to-landmarks-folder> --videos-folder <path-to-videos-folder> --dataset-folder <dataset-output-folder> (--create-new-split --test-size <test-percentage>)
+```
+The above command generates a train (and val) csv file which includes all the extracted keypoints. These can then be used to train or generates embeddings.
+
+## Creating Embeddings
+The embeddings can be created using the following command:
+```
+python3 export_embeddings.py --checkpoint <checkpoint-path> --dataset <path-to-dataset> --output <embeddings-output-file>
+```
+The command above generates the embeddings for a given dataset and saves them as a csv file.
\ No newline at end of file
diff --git a/export_embeddings.py b/export_embeddings.py
new file mode 100644
index 0000000..7246d70
--- /dev/null
+++ b/export_embeddings.py
@@ -0,0 +1,91 @@
+import multiprocessing
+import os
+import torch
+import argparse
+from datasets.dataset_loader import LocalDatasetLoader
+from datasets.embedding_dataset import SLREmbeddingDataset
+from torch.utils.data import DataLoader
+from datasets import SLREmbeddingDataset, collate_fn_padd
+from models.spoter_embedding_model import SPOTER_EMBEDDINGS
+import numpy as np
+import random
+import pandas as pd
+
+seed = 43
+random.seed(seed)
+np.random.seed(seed)
+os.environ["PYTHONHASHSEED"] = str(seed)
+torch.manual_seed(seed)
+torch.cuda.manual_seed(seed)
+torch.cuda.manual_seed_all(seed)
+torch.backends.cudnn.deterministic = True
+torch.use_deterministic_algorithms(True) 
+generator = torch.Generator()
+generator.manual_seed(seed)
+
+def seed_worker(worker_id):
+    worker_seed = torch.initial_seed() % 2**32
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)
+
+generator = torch.Generator()
+generator.manual_seed(seed)
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Export embeddings')
+    parser.add_argument('--checkpoint', type=str, default=None, help='Path to checkpoint')
+    parser.add_argument('--output', type=str, default=None, help='Path to output')
+    parser.add_argument('--dataset', type=str, default=None, help='Path to data')
+    args = parser.parse_args()
+    return args
+
+args = parse_args()
+
+device = torch.device("cpu")
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+
+# load the model
+checkpoint = torch.load(args.checkpoint, map_location=device)
+
+model = SPOTER_EMBEDDINGS(
+    features=checkpoint["config_args"].vector_length,
+    hidden_dim=checkpoint["config_args"].hidden_dim,
+    norm_emb=checkpoint["config_args"].normalize_embeddings,
+).to(device)
+
+model.load_state_dict(checkpoint["state_dict"])
+
+dataset_loader = LocalDatasetLoader()
+dataset = SLREmbeddingDataset(args.dataset, triplet=False, augmentations=False)
+
+data_loader = DataLoader(
+        dataset,
+        batch_size=1,
+        shuffle=False,
+        collate_fn=collate_fn_padd,
+        pin_memory=torch.cuda.is_available(),
+        num_workers=multiprocessing.cpu_count(),
+        worker_init_fn=seed_worker,
+        generator=generator,
+    )
+
+embeddings = []
+k = 0
+with torch.no_grad():
+    for i, (inputs, labels, masks) in enumerate(data_loader):
+        k += 1
+        inputs = inputs.to(device)
+        masks = masks.to(device)
+        outputs = model(inputs, masks)
+
+        for n in range(outputs.shape[0]):
+            embeddings.append(outputs[n].cpu().numpy())
+
+df = pd.read_csv(args.dataset)
+df["embeddings"] = embeddings
+df = df[['embeddings', 'label_name', 'labels']]
+df['embeddings2'] = df['embeddings'].apply(lambda x: x.tolist())
+
+
+df.to_csv(args.output, index=False)
\ No newline at end of file
diff --git a/notebooks/embeddings_evaluation.ipynb b/notebooks/embeddings_evaluation.ipynb
index 717a026..d012c83 100644
--- a/notebooks/embeddings_evaluation.ipynb
+++ b/notebooks/embeddings_evaluation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "id": "c20f7fd5",
    "metadata": {},
    "outputs": [],
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "id": "ada032d0",
    "metadata": {},
    "outputs": [],
@@ -22,13 +22,12 @@
     "import os\n",
     "import os.path as op\n",
     "import pandas as pd\n",
-    "import json\n",
-    "import base64"
+    "import json"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "id": "05682e73",
    "metadata": {},
    "outputs": [],
@@ -38,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "id": "fede7684",
    "metadata": {},
    "outputs": [],
@@ -48,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "id": "ce531994",
    "metadata": {},
    "outputs": [],
@@ -64,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "id": "f4a2d672",
    "metadata": {},
    "outputs": [],
@@ -87,17 +86,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "id": "1d9db764",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<torch._C.Generator at 0x7fb050be7710>"
+       "<torch._C.Generator at 0x7f010919d710>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -119,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "id": "71224139",
    "metadata": {},
    "outputs": [],
@@ -133,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "id": "013d3774",
    "metadata": {},
    "outputs": [
@@ -143,7 +142,7 @@
        "<All keys matched successfully>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -169,27 +168,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 24,
    "id": "ba6b58f0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "SL_DATASET = 'wlasl'  # or 'lsa'\n",
-    "if SL_DATASET == 'wlasl':\n",
+    "SL_DATASET = 'basic-signs'  # or 'wlasl'\n",
+    "\n",
+    "if SL_DATASET == 'fingerspelling':\n",
     "    dataset_name = \"fingerspelling\"\n",
-    "    num_classes = 100\n",
     "    split_dataset_path = \"fingerspelling_{}.csv\"\n",
-    "else:\n",
-    "    dataset_name = \"lsa64_mapped_mediapipe_only_landmarks_25fps\"\n",
-    "    num_classes = 64\n",
-    "    split_dataset_path = \"LSA64_{}.csv\"\n",
-    "    \n",
+    "elif SL_DATASET == 'wlasl':\n",
+    "    dataset_name = \"wlasl\"\n",
+    "    split_dataset_path = \"WLASL100_{}.csv\"\n",
+    "elif SL_DATASET == 'basic-signs':\n",
+    "    dataset_name = \"basic-signs\"\n",
+    "    split_dataset_path = \"basic-signs_{}.csv\"\n",
     "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 25,
    "id": "5643a72c",
    "metadata": {},
    "outputs": [],
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 16,
    "id": "04a62088",
    "metadata": {},
    "outputs": [],
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 17,
    "id": "79c837c1",
    "metadata": {},
    "outputs": [],
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 18,
    "id": "8b5bda73",
    "metadata": {},
    "outputs": [],
@@ -280,17 +280,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 19,
    "id": "0efa0871",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(560, 560)"
+       "(164, 164)"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -301,7 +301,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 21,
+   "id": "ab83c6e2",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "for split in splits:\n",
+    "    df = dfs[split]\n",
+    "    df['embeddings'] =  embeddings_split[split]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
    "id": "0b9fb9c2",
    "metadata": {},
    "outputs": [
@@ -309,54 +323,42 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0      [0.4734516, -0.58630264, 0.18397862, -0.165259...\n",
-      "1      [1.6672437, -2.3754091, -0.77506787, -0.666019...\n",
-      "2      [1.7801772, -0.0077665895, 0.22098881, 0.09736...\n",
-      "3      [-0.6503094, 0.14683367, 0.1253598, 0.5183654,...\n",
-      "4      [1.2275296, -0.4874984, 0.56826925, -0.9628880...\n",
+      "0      [1.7327625, -3.015248, -1.4775522, -0.7505071,...\n",
+      "1      [2.0936582, -0.596195, -0.7918601, -0.15896143...\n",
+      "2      [-1.4007742, -0.9608915, 1.3294879, -0.5185398...\n",
+      "3      [1.3280737, -3.299126, -1.0110444, -1.2528414,...\n",
+      "4      [-0.071124956, -0.79259753, 0.7182858, 0.38130...\n",
       "                             ...                        \n",
-      "555    [-0.4408903, -0.9623146, 0.21583065, -0.381131...\n",
-      "556    [1.7910445, -3.5434258, -1.332628, -0.95276725...\n",
-      "557    [2.3283613, 0.11504881, -0.4955331, -0.4563401...\n",
-      "558    [-1.0491562, -1.1793315, 0.3248821, 0.16679825...\n",
-      "559    [1.447621, -1.2482919, 0.17936605, -1.4752473,...\n",
-      "Name: embeddings, Length: 560, dtype: object\n",
-      "0      B\n",
-      "1      D\n",
-      "2      X\n",
-      "3      O\n",
-      "4      W\n",
-      "      ..\n",
-      "555    F\n",
-      "556    X\n",
-      "557    Z\n",
-      "558    Y\n",
-      "559    W\n",
-      "Name: label_name, Length: 560, dtype: object\n",
+      "159    [-1.5968355, 1.9617733, 0.28859574, 1.256657, ...\n",
+      "160    [0.44801116, -1.8377966, 1.1004394, -1.195648,...\n",
+      "161    [2.0584257, 1.6986116, 0.5129896, 0.27279535, ...\n",
+      "162    [1.6695516, -2.967027, -1.5715427, -0.77170163...\n",
+      "163    [1.4977738, -2.6278958, -1.6123883, -0.8420623...\n",
+      "Name: embeddings, Length: 164, dtype: object\n",
+      "0        TOT-ZIENS\n",
+      "1             GOED\n",
+      "2       GOEDENACHT\n",
+      "3              NEE\n",
+      "4           SLECHT\n",
+      "          ...     \n",
+      "159          SORRY\n",
+      "160    GOEDEMORGEN\n",
+      "161          LINKS\n",
+      "162      TOT-ZIENS\n",
+      "163           GOED\n",
+      "Name: label_name, Length: 164, dtype: object\n",
       "0       0\n",
       "1       1\n",
       "2       2\n",
       "3       3\n",
-      "4       5\n",
+      "4       4\n",
       "       ..\n",
-      "555    24\n",
-      "556     2\n",
-      "557    14\n",
-      "558     8\n",
-      "559     5\n",
-      "Name: labels, Length: 560, dtype: int64\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_969762/1944871806.py:9: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  dfs['train']['embeddings2'] = dfs['train']['embeddings'].apply(lambda x: x.tolist())\n"
+      "159     7\n",
+      "160     5\n",
+      "161    13\n",
+      "162     0\n",
+      "163     1\n",
+      "Name: labels, Length: 164, dtype: int64\n"
      ]
     }
    ],
@@ -372,21 +374,7 @@
     "dfs['train']['embeddings2'] = dfs['train']['embeddings'].apply(lambda x: x.tolist())\n",
     "\n",
     "# save the dfs['train']\n",
-    "dfs['train'].to_csv('../data/fingerspelling/embeddings.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "ab83c6e2",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "for split in splits:\n",
-    "    df = dfs[split]\n",
-    "    df['embeddings'] =  embeddings_split[split]"
+    "dfs['train'].to_csv(f'../data/{dataset_name}/embeddings.csv', index=False)"
    ]
   },
   {
@@ -400,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 23,
    "id": "7399b8ae",
    "metadata": {},
    "outputs": [
@@ -409,16 +397,16 @@
      "output_type": "stream",
      "text": [
       "Using centroids only\n",
-      "Top-1 accuracy:  77.06 %\n",
-      "Top-5 embeddings class match:  100.00 %  (Picks any class in the 5 closest embeddings)\n",
+      "Top-1 accuracy:  80.00 %\n",
+      "Top-5 embeddings class match:  93.33 %  (Picks any class in the 5 closest embeddings)\n",
       "\n",
       "################################\n",
       "\n",
       "Using all embeddings\n",
-      "Top-1 accuracy:  81.65 %\n",
-      "5-nn accuracy:  83.49 % (Picks the class that appears most often in the 5 closest embeddings)\n",
-      "Top-5 embeddings class match:  96.33 %  (Picks any class in the 5 closest embeddings)\n",
-      "Top-5 unique class match:  99.08 %  (Picks the 5 closest distinct classes)\n",
+      "Top-1 accuracy:  80.00 %\n",
+      "5-nn accuracy:  80.00 % (Picks the class that appears most often in the 5 closest embeddings)\n",
+      "Top-5 embeddings class match:  86.67 %  (Picks any class in the 5 closest embeddings)\n",
+      "Top-5 unique class match:  93.33 %  (Picks the 5 closest distinct classes)\n",
       "\n",
       "################################\n",
       "\n"
diff --git a/notebooks/visualize_embeddings.ipynb b/notebooks/visualize_embeddings.ipynb
index 5940d63..84ebdff 100644
--- a/notebooks/visualize_embeddings.ipynb
+++ b/notebooks/visualize_embeddings.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 30,
    "id": "8ef5cd92",
    "metadata": {},
    "outputs": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 31,
    "id": "78c4643a",
    "metadata": {},
    "outputs": [],
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 32,
    "id": "ffba4333",
    "metadata": {},
    "outputs": [],
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 33,
    "id": "5bc81f71",
    "metadata": {},
    "outputs": [],
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 34,
    "id": "3de8bcf2",
    "metadata": {
     "lines_to_next_cell": 0
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 35,
    "id": "91a045ba",
    "metadata": {},
    "outputs": [],
@@ -93,17 +93,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 36,
    "id": "bc50c296",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<torch._C.Generator at 0x7fc6fa3d5290>"
+       "<torch._C.Generator at 0x7f2d4c10efd0>"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 37,
    "id": "82766a17",
    "metadata": {},
    "outputs": [],
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 38,
    "id": "ead15a36",
    "metadata": {},
    "outputs": [
@@ -148,7 +148,7 @@
        "<All keys matched successfully>"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -175,26 +175,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 40,
    "id": "20f8036d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "SL_DATASET = 'wlasl'  # or 'lsa'\n",
-    "if SL_DATASET == 'wlasl':\n",
+    "SL_DATASET = 'basic-signs'  # or 'wlasl'\n",
+    "\n",
+    "if SL_DATASET == 'fingerspelling':\n",
     "    dataset_name = \"fingerspelling\"\n",
-    "    num_classes = 15\n",
-    "    split_dataset_path = \"fingerspelling_train.csv\"\n",
-    "else:\n",
-    "    dataset_name = \"lsa64_mapped_mediapipe_only_landmarks_25fps\"\n",
-    "    num_classes = 64\n",
-    "    split_dataset_path = \"LSA64_{}.csv\"\n",
+    "    split_dataset_path = \"fingerspelling_{}.csv\"\n",
+    "elif SL_DATASET == 'wlasl':\n",
+    "    dataset_name = \"wlasl\"\n",
+    "    split_dataset_path = \"WLASL100_{}.csv\"\n",
+    "elif SL_DATASET == 'basic-signs':\n",
+    "    dataset_name = \"basic-signs\"\n",
+    "    split_dataset_path = \"basic-signs_{}.csv\"\n",
     "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 41,
    "id": "758716b6",
    "metadata": {},
    "outputs": [],
@@ -214,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 42,
    "id": "f1527959",
    "metadata": {},
    "outputs": [
@@ -231,7 +233,7 @@
    ],
    "source": [
     "dataloaders = {}\n",
-    "splits = ['train']\n",
+    "splits = ['train', 'val']\n",
     "dfs = {}\n",
     "for split in splits:\n",
     "    split_set_path = op.join(dataset_folder, split_dataset_path.format(split))\n",
@@ -264,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 43,
    "id": "3c3af5bf",
    "metadata": {
     "lines_to_next_cell": 0
@@ -273,10 +275,10 @@
     {
      "data": {
       "text/plain": [
-       "560"
+       "164"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -301,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 44,
    "id": "dccbe1b9",
    "metadata": {},
    "outputs": [],
@@ -324,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 45,
    "id": "904298f0",
    "metadata": {},
    "outputs": [],
@@ -338,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 46,
    "id": "42832f7c",
    "metadata": {
     "scrolled": false
@@ -349,7 +351,7 @@
       "text/html": [
        "<div class=\"bk-root\">\n",
        "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
-       "        <span id=\"3649\">Loading BokehJS ...</span>\n",
+       "        <span id=\"1368\">Loading BokehJS ...</span>\n",
        "    </div>\n"
       ]
      },
@@ -358,7 +360,7 @@
     },
     {
      "data": {
-      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n    // Clean up Bokeh references\n    if (id != null && id in Bokeh.index) {\n      Bokeh.index[id].model.document.clear();\n      delete Bokeh.index[id];\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim();\n            if (id in Bokeh.index) {\n              Bokeh.index[id].model.document.clear();\n              delete Bokeh.index[id];\n            }\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"3649\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"3649\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
+      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n    // Clean up Bokeh references\n    if (id != null && id in Bokeh.index) {\n      Bokeh.index[id].model.document.clear();\n      delete Bokeh.index[id];\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim();\n            if (id in Bokeh.index) {\n              Bokeh.index[id].model.document.clear();\n              delete Bokeh.index[id];\n            }\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"1368\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"1368\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
       "application/vnd.bokehjs_load.v0+json": ""
      },
      "metadata": {},
@@ -401,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 47,
    "id": "ead4daf7",
    "metadata": {
     "scrolled": false
@@ -411,7 +413,7 @@
      "data": {
       "text/html": [
        "\n",
-       "  <div class=\"bk-root\" id=\"0936cd18-7278-46e3-94eb-fd0b9db94288\" data-root-id=\"3650\"></div>\n"
+       "  <div class=\"bk-root\" id=\"1a2f2ca2-cdb2-4df2-ba23-85018cca17d9\" data-root-id=\"1369\"></div>\n"
       ]
      },
      "metadata": {},
@@ -419,12 +421,12 @@
     },
     {
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       "application/vnd.bokehjs_exec.v0+json": ""
      },
      "metadata": {
       "application/vnd.bokehjs_exec.v0+json": {
-       "id": "3650"
+       "id": "1369"
       }
      },
      "output_type": "display_data"
diff --git a/preprocessing/create_fingerspelling_dataset.py b/preprocessing/create_fingerspelling_dataset.py
index db4e4cb..c3793cb 100644
--- a/preprocessing/create_fingerspelling_dataset.py
+++ b/preprocessing/create_fingerspelling_dataset.py
@@ -152,19 +152,23 @@ def create(args):
     df = pd.concat([df, df_aux], axis=1)
     if args.create_new_split:
         df_train, df_test = train_test_split(df, test_size=test_size, stratify=df['labels'], random_state=42)
-    else:
-        print(df['split'].unique())
-        df_train = df[(df['split'] == 'train') | (df['split'] == 'val')]
-        df_test = df[df['split'] == 'test']
+   
 
-    print(f'Num classes: {num_classes}')
-    print(df_train['labels'].value_counts())
-    assert set(df_train['labels'].unique()) == set(df_test['labels'].unique(
-    )), 'The labels for train and test dataframe are different. We recommend to download the dataset again, or to use \
-        the --create-new-split flag'
-    for split, df_split in zip(['train', 'val'],
-                               [df_train, df_test]):
-        fn_out = op.join(dataset_folder, f'fingerspelling_{split}.csv')
-        (df_split.reset_index(drop=True)
-                 .applymap(convert_to_str)
-                 .to_csv(fn_out, index=False))
+        print(f'Num classes: {num_classes}')
+        print(df_train['labels'].value_counts())
+        print(df_test['labels'].value_counts())
+        assert set(df_train['labels'].unique()) == set(df_test['labels'].unique(
+        )), 'The labels for train and test dataframe are different. We recommend to download the dataset again, or to use \
+            the --create-new-split flag'
+        for split, df_split in zip(['train', 'val'],
+                                [df_train, df_test]):
+            fn_out = op.join(dataset_folder, f'{split}.csv')
+            (df_split.reset_index(drop=True)
+                    .applymap(convert_to_str)
+                    .to_csv(fn_out, index=False))
+
+    else:
+        fn_out = op.join(dataset_folder, 'train.csv')
+        (df.reset_index(drop=True)
+            .applymap(convert_to_str)
+            .to_csv(fn_out, index=False))
\ No newline at end of file
diff --git a/train.sh b/train.sh
index 4572863..b6eb7ef 100755
--- a/train.sh
+++ b/train.sh
@@ -1,14 +1,14 @@
 #!/bin/sh
 python -m train \
 	--save_checkpoints_every 10 \
-    --experiment_name "basic" \
+    --experiment_name "wlasl" \
     --epochs 300 \
 	--optimizer "ADAM" \
 	--lr 0.0001 \
     --batch_size 16 \
-	--dataset_name "GoogleWLASL" \
-	--training_set_path "spoter_train.csv" \
-	--validation_set_path "spoter_test.csv" \
+	--dataset_name "WLASL" \
+	--training_set_path "WLASL100_train.csv" \
+	--validation_set_path "WLASL100_val.csv" \
 	--vector_length 32 \
 	--epoch_iters -1 \
 	--scheduler_factor 0.2 \