Test modelrun using timm library

notebooks/01_Demo_Pipeline_Usage_In_Code.ipynb

@@ -3,7 +3,10 @@
   {
    "cell_type": "markdown",
    "source": [
-    "- Alternative maybe https://huggingface.co/docs/transformers/custom_models"
+    "- https://huggingface.co/docs/transformers/custom_models - Alternative creating custom models\n",
+    "- https://huggingface.co/templates/feature-extraction - Template for inference API\n",
+    "- https://huggingface-widgets.netlify.app/ - Widgets for visualizing models in inference API\n",
+    "- https://huggingface.co/docs/hub/models-widgets#how-can-i-control-my-models-widget-inference-api-parameters - Controlling inference API parameters"
    ],
    "metadata": {
     "collapsed": false
@@ -11,84 +14,314 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "outputs": [],
    "source": [
-    "from transformers.pipelines import PIPELINE_REGISTRY\n",
-    "from transformers import pipeline\n",
+    "import timm\n",
+    "import torch\n",
     "from pipeline_wrapper import MyPipeline"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-05T11:23:02.669143300Z",
-     "start_time": "2024-02-05T11:23:02.652092400Z"
+     "end_time": "2024-02-15T08:30:05.075994Z",
+     "start_time": "2024-02-15T08:29:59.741405700Z"
     }
    }
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Using timm to extract features"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'torch' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[10], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m test_tensor \u001B[38;5;241m=\u001B[39m \u001B[43mtorch\u001B[49m\u001B[38;5;241m.\u001B[39mrandn(\u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m3\u001B[39m, \u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m1\u001B[39m)\n",
+      "\u001B[1;31mNameError\u001B[0m: name 'torch' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "test_tensor = torch.randn(2, 3, 1, 1)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-02-15T08:29:09.423837500Z",
+     "start_time": "2024-02-15T08:29:09.397259300Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "outputs": [],
+   "source": [
+    "feature_extractor = timm.create_model('resnet18', pretrained=True, num_classes=0, global_pool='')\n",
+    "features = feature_extractor.forward_features(test_tensor)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-02-15T07:55:57.395514300Z",
+     "start_time": "2024-02-15T07:55:56.561361800Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "tensor([[[[0.0000]],\n\n         [[0.6944]],\n\n         [[0.0000]],\n\n         ...,\n\n         [[0.0000]],\n\n         [[0.0000]],\n\n         [[0.0000]]],\n\n\n        [[[0.0000]],\n\n         [[0.0000]],\n\n         [[0.0143]],\n\n         ...,\n\n         [[0.0000]],\n\n         [[0.0000]],\n\n         [[0.0000]]]], grad_fn=<ReluBackward0>)"
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-02-15T07:55:58.303122200Z",
+     "start_time": "2024-02-15T07:55:58.266709100Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Unknown model (ECG2HRV)",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mRuntimeError\u001B[0m                              Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[15], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m feature_extractor \u001B[38;5;241m=\u001B[39m \u001B[43mtimm\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate_model\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mHUBII-Platform/ECG2HRV\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpretrained\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnum_classes\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mglobal_pool\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\timm\\models\\_factory.py:113\u001B[0m, in \u001B[0;36mcreate_model\u001B[1;34m(model_name, pretrained, pretrained_cfg, pretrained_cfg_overlay, checkpoint_path, scriptable, exportable, no_jit, **kwargs)\u001B[0m\n\u001B[0;32m    110\u001B[0m         pretrained_cfg \u001B[38;5;241m=\u001B[39m pretrained_tag\n\u001B[0;32m    112\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m is_model(model_name):\n\u001B[1;32m--> 113\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUnknown model (\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m)\u001B[39m\u001B[38;5;124m'\u001B[39m \u001B[38;5;241m%\u001B[39m model_name)\n\u001B[0;32m    115\u001B[0m create_fn \u001B[38;5;241m=\u001B[39m model_entrypoint(model_name)\n\u001B[0;32m    116\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m set_layer_config(scriptable\u001B[38;5;241m=\u001B[39mscriptable, exportable\u001B[38;5;241m=\u001B[39mexportable, no_jit\u001B[38;5;241m=\u001B[39mno_jit):\n",
+      "\u001B[1;31mRuntimeError\u001B[0m: Unknown model (ECG2HRV)"
+     ]
+    }
+   ],
+   "source": [
+    "feature_extractor = timm.create_model('HUBII-Platform/ECG2HRV', pretrained=True, num_classes=0, global_pool='')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-02-15T08:30:32.648960900Z",
+     "start_time": "2024-02-15T08:30:32.601345400Z"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Using transformers to extract features"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "1. Feature extraction task"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "config.json:   0%|          | 0.00/1.72k [00:00<?, ?B/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "b3723308dd9940aaa268f8f474eeb5df"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "ecg2hrv is already registered. Overwriting pipeline for task ecg2hrv...\n"
+      "C:\\Users\\merti\\anaconda3\\envs\\py310\\lib\\site-packages\\huggingface_hub\\file_download.py:149: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\merti\\.cache\\huggingface\\hub\\models--facebook--bart-base. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
+      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
+      "  warnings.warn(message)\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": "model.safetensors:   0%|          | 0.00/558M [00:00<?, ?B/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "5bffab7288404bc284624575d056c74d"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "2f3359fbba014f02994e17163fcc0f1c"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "b315c7ae15b64a148df558539c3fc980"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "1b275b44c1cf4d8eaa9b57aedad04240"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "PIPELINE_REGISTRY.register_pipeline(\n",
-    "    \"ecg2hrv\",\n",
-    "    pipeline_class=MyPipeline,\n",
-    "    # model_class=MyModel\n",
-    ")\n"
+    "# Example with pipeline\n",
+    "from transformers import pipeline\n",
+    "checkpoint = \"facebook/bart-base\"\n",
+    "feature_extractor = pipeline(\"feature-extraction\", framework=\"pt\",model=checkpoint)\n",
+    "text = \"Transformers is an awesome library!\""
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-05T11:41:30.464821400Z",
-     "start_time": "2024-02-05T11:41:30.444149300Z"
+     "end_time": "2024-02-15T08:02:58.086587200Z",
+     "start_time": "2024-02-15T08:02:15.582501300Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'default'",
+     "ename": "OSError",
+     "evalue": "It looks like the config file at 'C:\\Users\\merti\\.cache\\huggingface\\hub\\models--HUBII-Platform--ECG2HRV\\snapshots\\75f67e01de12e33cfb05cfbfed35ff621246b3f9\\config.json' is not a valid JSON file.",
      "output_type": "error",
      "traceback": [
       "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
-      "Cell \u001B[1;32mIn[18], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m feature_extractor \u001B[38;5;241m=\u001B[39m \u001B[43mpipeline\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mecg2hrv\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n",
-      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\pipelines\\__init__.py:833\u001B[0m, in \u001B[0;36mpipeline\u001B[1;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001B[0m\n\u001B[0;32m    830\u001B[0m \u001B[38;5;66;03m# Use default model/config/tokenizer for the task if no model is provided\u001B[39;00m\n\u001B[0;32m    831\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m model \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m    832\u001B[0m     \u001B[38;5;66;03m# At that point framework might still be undetermined\u001B[39;00m\n\u001B[1;32m--> 833\u001B[0m     model, default_revision \u001B[38;5;241m=\u001B[39m \u001B[43mget_default_model_and_revision\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtargeted_task\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mframework\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtask_options\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    834\u001B[0m     revision \u001B[38;5;241m=\u001B[39m revision \u001B[38;5;28;01mif\u001B[39;00m revision \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m default_revision\n\u001B[0;32m    835\u001B[0m     logger\u001B[38;5;241m.\u001B[39mwarning(\n\u001B[0;32m    836\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNo model was supplied, defaulted to \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mmodel\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m and revision\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    837\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mrevision\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m (\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mHUGGINGFACE_CO_RESOLVE_ENDPOINT\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mmodel\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m).\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    838\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mUsing a pipeline without specifying a model name and revision in production is not recommended.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    839\u001B[0m     )\n",
-      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\pipelines\\base.py:399\u001B[0m, in \u001B[0;36mget_default_model_and_revision\u001B[1;34m(targeted_task, framework, task_options)\u001B[0m\n\u001B[0;32m    396\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m is_tf_available() \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m is_torch_available():\n\u001B[0;32m    397\u001B[0m     framework \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtf\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m--> 399\u001B[0m defaults \u001B[38;5;241m=\u001B[39m \u001B[43mtargeted_task\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mdefault\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[0;32m    400\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m task_options:\n\u001B[0;32m    401\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m task_options \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m defaults:\n",
-      "\u001B[1;31mKeyError\u001B[0m: 'default'"
+      "\u001B[1;31mJSONDecodeError\u001B[0m                           Traceback (most recent call last)",
+      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:719\u001B[0m, in \u001B[0;36mPretrainedConfig._get_config_dict\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m    717\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m    718\u001B[0m     \u001B[38;5;66;03m# Load config dict\u001B[39;00m\n\u001B[1;32m--> 719\u001B[0m     config_dict \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mcls\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_dict_from_json_file\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresolved_config_file\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    720\u001B[0m     config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m commit_hash\n",
+      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:818\u001B[0m, in \u001B[0;36mPretrainedConfig._dict_from_json_file\u001B[1;34m(cls, json_file)\u001B[0m\n\u001B[0;32m    817\u001B[0m     text \u001B[38;5;241m=\u001B[39m reader\u001B[38;5;241m.\u001B[39mread()\n\u001B[1;32m--> 818\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mjson\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mloads\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtext\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\json\\__init__.py:346\u001B[0m, in \u001B[0;36mloads\u001B[1;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001B[0m\n\u001B[0;32m    343\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (\u001B[38;5;28mcls\u001B[39m \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m object_hook \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m\n\u001B[0;32m    344\u001B[0m         parse_int \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m parse_float \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m\n\u001B[0;32m    345\u001B[0m         parse_constant \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m object_pairs_hook \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m kw):\n\u001B[1;32m--> 346\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_default_decoder\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdecode\u001B[49m\u001B[43m(\u001B[49m\u001B[43ms\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    347\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mcls\u001B[39m \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
+      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\json\\decoder.py:337\u001B[0m, in \u001B[0;36mJSONDecoder.decode\u001B[1;34m(self, s, _w)\u001B[0m\n\u001B[0;32m    333\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001B[39;00m\n\u001B[0;32m    334\u001B[0m \u001B[38;5;124;03mcontaining a JSON document).\u001B[39;00m\n\u001B[0;32m    335\u001B[0m \n\u001B[0;32m    336\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m--> 337\u001B[0m obj, end \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mraw_decode\u001B[49m\u001B[43m(\u001B[49m\u001B[43ms\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43midx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_w\u001B[49m\u001B[43m(\u001B[49m\u001B[43ms\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mend\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    338\u001B[0m end \u001B[38;5;241m=\u001B[39m _w(s, end)\u001B[38;5;241m.\u001B[39mend()\n",
+      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\json\\decoder.py:355\u001B[0m, in \u001B[0;36mJSONDecoder.raw_decode\u001B[1;34m(self, s, idx)\u001B[0m\n\u001B[0;32m    354\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mStopIteration\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m--> 355\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m JSONDecodeError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mExpecting value\u001B[39m\u001B[38;5;124m\"\u001B[39m, s, err\u001B[38;5;241m.\u001B[39mvalue) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m    356\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m obj, end\n",
+      "\u001B[1;31mJSONDecodeError\u001B[0m: Expecting value: line 1 column 1 (char 0)",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001B[1;31mOSError\u001B[0m                                   Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[7], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m feature_extractor \u001B[38;5;241m=\u001B[39m \u001B[43mpipeline\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mfeature-extraction\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mHUBII-Platform/ECG2HRV\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\pipelines\\__init__.py:782\u001B[0m, in \u001B[0;36mpipeline\u001B[1;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001B[0m\n\u001B[0;32m    779\u001B[0m                 adapter_config \u001B[38;5;241m=\u001B[39m json\u001B[38;5;241m.\u001B[39mload(f)\n\u001B[0;32m    780\u001B[0m                 model \u001B[38;5;241m=\u001B[39m adapter_config[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbase_model_name_or_path\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[1;32m--> 782\u001B[0m     config \u001B[38;5;241m=\u001B[39m AutoConfig\u001B[38;5;241m.\u001B[39mfrom_pretrained(\n\u001B[0;32m    783\u001B[0m         model, _from_pipeline\u001B[38;5;241m=\u001B[39mtask, code_revision\u001B[38;5;241m=\u001B[39mcode_revision, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mhub_kwargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mmodel_kwargs\n\u001B[0;32m    784\u001B[0m     )\n\u001B[0;32m    785\u001B[0m     hub_kwargs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m config\u001B[38;5;241m.\u001B[39m_commit_hash\n\u001B[0;32m    787\u001B[0m custom_tasks \u001B[38;5;241m=\u001B[39m {}\n",
+      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\models\\auto\\configuration_auto.py:1100\u001B[0m, in \u001B[0;36mAutoConfig.from_pretrained\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m   1097\u001B[0m trust_remote_code \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtrust_remote_code\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[0;32m   1098\u001B[0m code_revision \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcode_revision\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m-> 1100\u001B[0m config_dict, unused_kwargs \u001B[38;5;241m=\u001B[39m PretrainedConfig\u001B[38;5;241m.\u001B[39mget_config_dict(pretrained_model_name_or_path, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   1101\u001B[0m has_remote_code \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mauto_map\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mAutoConfig\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mauto_map\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m   1102\u001B[0m has_local_code \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel_type\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict \u001B[38;5;129;01mand\u001B[39;00m config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel_type\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;129;01min\u001B[39;00m CONFIG_MAPPING\n",
+      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:634\u001B[0m, in \u001B[0;36mPretrainedConfig.get_config_dict\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m    632\u001B[0m original_kwargs \u001B[38;5;241m=\u001B[39m copy\u001B[38;5;241m.\u001B[39mdeepcopy(kwargs)\n\u001B[0;32m    633\u001B[0m \u001B[38;5;66;03m# Get config dict associated with the base config file\u001B[39;00m\n\u001B[1;32m--> 634\u001B[0m config_dict, kwargs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m_get_config_dict(pretrained_model_name_or_path, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m    635\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict:\n\u001B[0;32m    636\u001B[0m     original_kwargs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n",
+      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:722\u001B[0m, in \u001B[0;36mPretrainedConfig._get_config_dict\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m    720\u001B[0m     config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m commit_hash\n\u001B[0;32m    721\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (json\u001B[38;5;241m.\u001B[39mJSONDecodeError, \u001B[38;5;167;01mUnicodeDecodeError\u001B[39;00m):\n\u001B[1;32m--> 722\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mEnvironmentError\u001B[39;00m(\n\u001B[0;32m    723\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mIt looks like the config file at \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mresolved_config_file\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m is not a valid JSON file.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    724\u001B[0m     )\n\u001B[0;32m    726\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_local:\n\u001B[0;32m    727\u001B[0m     logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mloading configuration file \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mresolved_config_file\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
+      "\u001B[1;31mOSError\u001B[0m: It looks like the config file at 'C:\\Users\\merti\\.cache\\huggingface\\hub\\models--HUBII-Platform--ECG2HRV\\snapshots\\75f67e01de12e33cfb05cfbfed35ff621246b3f9\\config.json' is not a valid JSON file."
      ]
     }
    ],
    "source": [
-    "feature_extractor = pipeline(\"ecg2hrv\")"
+    "feature_extractor = pipeline(\"feature-extraction\", model = 'HUBII-Platform/ECG2HRV')"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-05T11:41:40.766340400Z",
-     "start_time": "2024-02-05T11:41:40.656799200Z"
+     "end_time": "2024-02-15T08:25:26.931190300Z",
+     "start_time": "2024-02-15T08:25:25.877444800Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "PIPELINE_REGISTRY.register_pipeline(\n",
+    "    \"ecg2hrv\",\n",
+    "    pipeline_class=MyPipeline,\n",
+    "    # model_class=MyModel\n",
+    ")\n",
+    "feature_extractor = pipeline(\"ecg2hrv\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "feature_extractor = pipeline(\"ecg2hrv\", model=\"HUBII-Platform/ECG2HRV\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "2. AutoModel"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "outputs": [
     {
      "ename": "OSError",
-     "evalue": "It looks like the config file at 'C:\\Users\\merti\\.cache\\huggingface\\hub\\models--HUBII-Platform--ECG2HRV\\snapshots\\7742915f18da533bfd88c1a49a9e9727635cd1d7\\config.json' is not a valid JSON file.",
+     "evalue": "It looks like the config file at 'C:\\Users\\merti\\.cache\\huggingface\\hub\\models--HUBII-Platform--ECG2HRV\\snapshots\\75f67e01de12e33cfb05cfbfed35ff621246b3f9\\config.json' is not a valid JSON file.",
      "output_type": "error",
      "traceback": [
       "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
@@ -101,29 +334,44 @@
       "\u001B[1;31mJSONDecodeError\u001B[0m: Expecting value: line 1 column 1 (char 0)",
       "\nDuring handling of the above exception, another exception occurred:\n",
       "\u001B[1;31mOSError\u001B[0m                                   Traceback (most recent call last)",
-      "Cell \u001B[1;32mIn[17], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m feature_extractor \u001B[38;5;241m=\u001B[39m \u001B[43mpipeline\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mecg2hrv\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mHUBII-Platform/ECG2HRV\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n",
-      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\pipelines\\__init__.py:782\u001B[0m, in \u001B[0;36mpipeline\u001B[1;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001B[0m\n\u001B[0;32m    779\u001B[0m                 adapter_config \u001B[38;5;241m=\u001B[39m json\u001B[38;5;241m.\u001B[39mload(f)\n\u001B[0;32m    780\u001B[0m                 model \u001B[38;5;241m=\u001B[39m adapter_config[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbase_model_name_or_path\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[1;32m--> 782\u001B[0m     config \u001B[38;5;241m=\u001B[39m AutoConfig\u001B[38;5;241m.\u001B[39mfrom_pretrained(\n\u001B[0;32m    783\u001B[0m         model, _from_pipeline\u001B[38;5;241m=\u001B[39mtask, code_revision\u001B[38;5;241m=\u001B[39mcode_revision, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mhub_kwargs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mmodel_kwargs\n\u001B[0;32m    784\u001B[0m     )\n\u001B[0;32m    785\u001B[0m     hub_kwargs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m config\u001B[38;5;241m.\u001B[39m_commit_hash\n\u001B[0;32m    787\u001B[0m custom_tasks \u001B[38;5;241m=\u001B[39m {}\n",
+      "Cell \u001B[1;32mIn[2], line 3\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m# Example with AutoModel\u001B[39;00m\n\u001B[0;32m      2\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mtransformers\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m AutoTokenizer, AutoModel\n\u001B[1;32m----> 3\u001B[0m model \u001B[38;5;241m=\u001B[39m \u001B[43mAutoModel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfrom_pretrained\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mHUBII-Platform/ECG2HRV\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\models\\auto\\auto_factory.py:526\u001B[0m, in \u001B[0;36m_BaseAutoModelClass.from_pretrained\u001B[1;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001B[0m\n\u001B[0;32m    523\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m kwargs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mquantization_config\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m) \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m    524\u001B[0m     _ \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mquantization_config\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m--> 526\u001B[0m config, kwargs \u001B[38;5;241m=\u001B[39m AutoConfig\u001B[38;5;241m.\u001B[39mfrom_pretrained(\n\u001B[0;32m    527\u001B[0m     pretrained_model_name_or_path,\n\u001B[0;32m    528\u001B[0m     return_unused_kwargs\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m    529\u001B[0m     trust_remote_code\u001B[38;5;241m=\u001B[39mtrust_remote_code,\n\u001B[0;32m    530\u001B[0m     code_revision\u001B[38;5;241m=\u001B[39mcode_revision,\n\u001B[0;32m    531\u001B[0m     _commit_hash\u001B[38;5;241m=\u001B[39mcommit_hash,\n\u001B[0;32m    532\u001B[0m     \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mhub_kwargs,\n\u001B[0;32m    533\u001B[0m     \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs,\n\u001B[0;32m    534\u001B[0m )\n\u001B[0;32m    536\u001B[0m \u001B[38;5;66;03m# if torch_dtype=auto was passed here, ensure to pass it on\u001B[39;00m\n\u001B[0;32m    537\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m kwargs_orig\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtorch_dtype\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m) \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mauto\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n",
       "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\models\\auto\\configuration_auto.py:1100\u001B[0m, in \u001B[0;36mAutoConfig.from_pretrained\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m   1097\u001B[0m trust_remote_code \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtrust_remote_code\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[0;32m   1098\u001B[0m code_revision \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcode_revision\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m-> 1100\u001B[0m config_dict, unused_kwargs \u001B[38;5;241m=\u001B[39m PretrainedConfig\u001B[38;5;241m.\u001B[39mget_config_dict(pretrained_model_name_or_path, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   1101\u001B[0m has_remote_code \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mauto_map\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mAutoConfig\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mauto_map\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m   1102\u001B[0m has_local_code \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel_type\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict \u001B[38;5;129;01mand\u001B[39;00m config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel_type\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;129;01min\u001B[39;00m CONFIG_MAPPING\n",
       "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:634\u001B[0m, in \u001B[0;36mPretrainedConfig.get_config_dict\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m    632\u001B[0m original_kwargs \u001B[38;5;241m=\u001B[39m copy\u001B[38;5;241m.\u001B[39mdeepcopy(kwargs)\n\u001B[0;32m    633\u001B[0m \u001B[38;5;66;03m# Get config dict associated with the base config file\u001B[39;00m\n\u001B[1;32m--> 634\u001B[0m config_dict, kwargs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m_get_config_dict(pretrained_model_name_or_path, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m    635\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m config_dict:\n\u001B[0;32m    636\u001B[0m     original_kwargs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n",
       "File \u001B[1;32m~\\anaconda3\\envs\\py310\\lib\\site-packages\\transformers\\configuration_utils.py:722\u001B[0m, in \u001B[0;36mPretrainedConfig._get_config_dict\u001B[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001B[0m\n\u001B[0;32m    720\u001B[0m     config_dict[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_commit_hash\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m commit_hash\n\u001B[0;32m    721\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (json\u001B[38;5;241m.\u001B[39mJSONDecodeError, \u001B[38;5;167;01mUnicodeDecodeError\u001B[39;00m):\n\u001B[1;32m--> 722\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mEnvironmentError\u001B[39;00m(\n\u001B[0;32m    723\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mIt looks like the config file at \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mresolved_config_file\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m is not a valid JSON file.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    724\u001B[0m     )\n\u001B[0;32m    726\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_local:\n\u001B[0;32m    727\u001B[0m     logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mloading configuration file \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mresolved_config_file\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
-      "\u001B[1;31mOSError\u001B[0m: It looks like the config file at 'C:\\Users\\merti\\.cache\\huggingface\\hub\\models--HUBII-Platform--ECG2HRV\\snapshots\\7742915f18da533bfd88c1a49a9e9727635cd1d7\\config.json' is not a valid JSON file."
+      "\u001B[1;31mOSError\u001B[0m: It looks like the config file at 'C:\\Users\\merti\\.cache\\huggingface\\hub\\models--HUBII-Platform--ECG2HRV\\snapshots\\75f67e01de12e33cfb05cfbfed35ff621246b3f9\\config.json' is not a valid JSON file."
      ]
     }
    ],
    "source": [
-    "feature_extractor = pipeline(\"ecg2hrv\", model=\"HUBII-Platform/ECG2HRV\")"
+    "# Example with AutoModel\n",
+    "from transformers import AutoTokenizer, AutoModel\n",
+    "model = AutoModel.from_pretrained('HUBII-Platform/ECG2HRV')"
    ],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-05T11:41:36.288140800Z",
-     "start_time": "2024-02-05T11:41:35.591183Z"
+     "end_time": "2024-02-15T08:19:30.471734700Z",
+     "start_time": "2024-02-15T08:19:29.806536100Z"
     }
    }
   },
   {
    "cell_type": "markdown",
-   "source": [],
+   "source": [
+    "3. Batched feature extraction - not supported (see https://huggingface.co/docs/transformers/main_classes/feature_extractor#transformers.BatchFeature)\n",
+    "Not possible since it is not a model itself but a component used in the pipeline"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Using simple download\n",
+    "(See https://huggingface.co/julien-c/wine-quality?structured_data=%7B%7D)"
+   ],
    "metadata": {
     "collapsed": false
    }
@@ -132,7 +380,26 @@
    "cell_type": "code",
    "execution_count": null,
    "outputs": [],
-   "source": [],
+   "source": [
+    "from huggingface_hub import hf_hub_url, cached_download\n",
+    "import joblib"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "REPO_ID = \"HUBII-Platform/ECG2HRV\"\n",
+    "FILENAME = \"feature-extractor.joblib\"\n",
+    "\n",
+    "model = joblib.load(cached_download(\n",
+    "    hf_hub_url(REPO_ID, FILENAME)\n",
+    "))\n"
+   ],
    "metadata": {
     "collapsed": false
    }