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markdown-it-py>=2.2.0->rich>=12.3.0->typer>=0.24.0->typer-slim->transformers) (0.1.2)\n"]}],"source":["pip install torch transformers datasets scikit-learn pandas matplotlib seaborn huggingface_hub"]},{"cell_type":"code","source":["from google.colab import files\n","uploaded = files.upload()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":325},"id":"EmUHP8yoyddT","executionInfo":{"status":"error","timestamp":1772082815261,"user_tz":-330,"elapsed":3935,"user":{"displayName":"jaimy","userId":"07332807517928872066"}},"outputId":"f29f4592-044d-450d-8cf7-8365353f6bca"},"execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-e6f08111-1a99-44c9-82f7-d82e9c6bd60b\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-e6f08111-1a99-44c9-82f7-d82e9c6bd60b\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. 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\u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m(target_dir)\u001b[0m\n\u001b[1;32m     67\u001b[0m   \"\"\"\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m   \u001b[0muploaded_files\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_upload_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m   \u001b[0;31m# Mapping from original filename to filename as saved locally.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m   \u001b[0mlocal_filenames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36m_upload_files\u001b[0;34m(multiple)\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m   \u001b[0;31m# First result is always an indication that the file picker has completed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m   result = _output.eval_js(\n\u001b[0m\u001b[1;32m    162\u001b[0m       'google.colab._files._uploadFiles(\"{input_id}\", \"{output_id}\")'.format(\n\u001b[1;32m    163\u001b[0m           \u001b[0minput_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result, timeout_sec)\u001b[0m\n\u001b[1;32m     38\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     if (\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":["import pandas as pd\n","df = pd.read_csv(\"dataset_master_shuffled.csv\")\n","# Make sure label is integer\n","df[\"Label\"] = df[\"Label\"].astype(int)\n","\n","print(df.head())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y-540s65ym3B","executionInfo":{"status":"ok","timestamp":1772041218407,"user_tz":-330,"elapsed":108,"user":{"displayName":"jaimy","userId":"07332807517928872066"}},"outputId":"2f2fa12c-79e5-4780-a039-4fccce703fa8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["                                             Comment  Label\n","0         സൗജന്യ ഐഫോൺ വാട്സാപ്പിൽ മെസ്സേജ് അയക്കൂ !!      1\n","1  perfect time for me to find your channel as i ...      0\n","2  one of the best thing about dude is that he ne...      0\n","3  i hope we can lock the lockscreen i dont want ...      0\n","4  Prithvirajnte movie Appol aa oru standard prat...      0\n"]}]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","\n","train_texts, val_texts, train_labels, val_labels = train_test_split(\n","    df[\"Comment\"].tolist(),\n","    df[\"Label\"].tolist(),\n","    test_size=0.2,\n","    random_state=42\n",")"],"metadata":{"id":"E7C-0nJf2uhT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","\n","model_name = \"albert-base-v2\"\n","\n","tokenizer = AutoTokenizer.from_pretrained(model_name)\n","\n","model = AutoModelForSequenceClassification.from_pretrained(\n","    model_name,\n","    num_labels=len(set(df[\"Label\"]))\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":640,"referenced_widgets":["beaead7e42374b6cadd1f9b4be87b154","3041b2430b374676ad905e2957c41b1a","cbe1546171fa40e8b59f08c8580c94fa","0a7fe0452d414a16a8e62ceba731fad2","b973afadd2e74ec79d7101cd30131eaf","8f8e26f10a9e4d93b31fc7d5a8a0261a","53e6aabddd6540b288fe3a8bd5846a9c","a6d38b938b444a398a0d806d9d60da1d","2a7ce9b4ad8d44bb93942d4ee4fd26e8","8b6fbd63f8064ec490ccebe0ca9fec21","edb38bde123f4cb69d02be4e57b5aef4","10499e4367c24f29ba1765a5a86b3a3d","5414d3fd667f4490846e746ff42ac60d","6b408641e4ff4681b9db49438e5b4236","ca322715b6e9499e93b3f41a0ad99c37","3b857392952048f2b044b3059ce74596","be8000c1e4b54bb6a23e837ecad1c53b","478dfee1f11f461caff087366fccf1ca","e1bdaa4c7b2441d6a0a0f5907d9b24ee","2b8ff7b7e4a449509be86f239827d26b","d9ae1771a87a49f1a7b4c6e19b7f9911","80f3a8573c854194bf4ca3b20e05577c","2fa0b4ddd4f84877b35b85810ecaa06b","39b098d7a9924d46b6ff9b2e5d5d1d06","ece22759d17b4bba8c57cb626a00fd80","664a9451f18e4c039199d5af8f68780c","ebad94d898e349f2bf5498897389e2ce","e4da49a90a034b6d8eb4a47d73aec13e","2ad99d2939134ff6a6c83206cdbcbcd8","e9bb55ae56dd4e5ebb3d67bcd053d813","3b5fc88449564c8b81c5bd5d59fcfcef","109c3eb68c444e07a522e7a4cc37bf74","a44dce13ab8f4cadb7cb01b9fa63531c","e299173a4e34462b87bb622671db5720","06a5145ba7b649fea5ac93d828bf3d16","f3429d85763142cf9814b29cf625269a","0bf0bf78cc134f0f97bbb53964a47843","870c9c9881a74997b43a4bb65adfdd93","fc7bdefbc974403abb324906b6aafeda","023fd4f7365b4521bdb0445345590765","83ab689ec3ca414ba1eed9a93f442641","c5f7f307e2db4dcf9fb2c6f860fe7ce8","c86c0922175645c196496d41a33a5e1e","d6bee4a385934e7d9581a0ed1cf31db4","0c201f435026445db3dcd221b2567f1a","95044fc60a7b42b5a86ccf05a8282e9d","13f87ca4c32e40ccacf7befd64afc07e","f82711e1ea7d4f94838cf3145a0880d8","789749349cd54e41a44cf0e5c1cd3df3","efa1369a0346461b9ffa1e5510d8b1c5","7d539eea244344c3a7cd6c8444e1f732","8ccc86f27db649e2a25908ccc532e876","a51c629e1f924a1eb282a4bf949865ba","579fc4e3091941c8b820ce8638104311","9b2217ddad404f83a6679ae75674016b","53f4796b9c004896a9e911962b2daabb","c60a497099674eed94d3d36998fc9d03","9c3571f2f0ba4200a8f67d4eabea9ed1","72db8f71f75048369fdfe0ed6be765ad","dfe9dfcdcf0f4cf4944058b9f34444f9","fd8831d8b1d74fdf9775b020b32360c8","ae98a33090494ae5915ac2d6a0a1d603","898db6cef18b4220a58cd2fd9403a9f5","081ff59f9590492ebbe756039755b179","9c9feeea89e14f9b8e91a9464a0e8a02","00fcf2f457734c9f81e02dfb8d5979e4"]},"id":"JZtn2hu50Y3P","executionInfo":{"status":"ok","timestamp":1772041226075,"user_tz":-330,"elapsed":4525,"user":{"displayName":"jaimy","userId":"07332807517928872066"}},"outputId":"37300df1-916b-4330-e8f8-e9e38290dbd0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n","Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n","WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/684 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"beaead7e42374b6cadd1f9b4be87b154"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"10499e4367c24f29ba1765a5a86b3a3d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["spiece.model:   0%|          | 0.00/760k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2fa0b4ddd4f84877b35b85810ecaa06b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.31M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e299173a4e34462b87bb622671db5720"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/47.4M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0c201f435026445db3dcd221b2567f1a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/25 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"53f4796b9c004896a9e911962b2daabb"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["AlbertForSequenceClassification LOAD REPORT from: albert-base-v2\n","Key                          | Status     | \n","-----------------------------+------------+-\n","predictions.LayerNorm.bias   | UNEXPECTED | \n","predictions.dense.bias       | UNEXPECTED | \n","predictions.dense.weight     | UNEXPECTED | \n","predictions.decoder.bias     | UNEXPECTED | \n","predictions.LayerNorm.weight | UNEXPECTED | \n","predictions.bias             | UNEXPECTED | \n","classifier.weight            | MISSING    | \n","classifier.bias              | MISSING    | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n","- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"]}]},{"cell_type":"code","source":["train_encodings = tokenizer(\n","    train_texts,\n","    truncation=True,\n","    padding=True,\n","    max_length=128\n",")\n","\n","val_encodings = tokenizer(\n","    val_texts,\n","    truncation=True,\n","    padding=True,\n","    max_length=128\n",")"],"metadata":{"id":"dhAX4neY01FO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import torch\n","\n","class Dataset(torch.utils.data.Dataset):\n","    def __init__(self, encodings, labels):\n","        self.encodings = encodings\n","        self.labels = labels\n","\n","    def __getitem__(self, idx):\n","        item = {k: torch.tensor(v[idx]) for k, v in self.encodings.items()}\n","        item[\"labels\"] = torch.tensor(self.labels[idx])\n","        return item\n","\n","    def __len__(self):\n","        return len(self.labels)\n","\n","train_dataset = Dataset(train_encodings, train_labels)\n","val_dataset = Dataset(val_encodings, val_labels)"],"metadata":{"id":"HAAPy2vm04OW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n","\n","def compute_metrics(pred):\n","    labels = pred.label_ids\n","    preds = np.argmax(pred.predictions, axis=1)\n","\n","    precision, recall, f1, _ = precision_recall_fscore_support(\n","        labels, preds, average=\"weighted\"\n","    )\n","    acc = accuracy_score(labels, preds)\n","\n","    return {\n","        \"accuracy\": acc,\n","        \"f1\": f1,\n","        \"precision\": precision,\n","        \"recall\": recall\n","    }"],"metadata":{"id":"YR-d6mTc1AGO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers import Trainer, TrainingArguments\n","from transformers import EarlyStoppingCallback\n","training_args = TrainingArguments(\n","    output_dir=\"./results\",\n","    num_train_epochs=3,\n","    learning_rate=2e-5,\n","    per_device_train_batch_size=16,\n","    per_device_eval_batch_size=16,\n","    eval_strategy=\"steps\",\n","    eval_steps=500,\n","    save_strategy=\"steps\",\n","    save_steps=500,\n","    load_best_model_at_end=True,\n","    metric_for_best_model=\"f1\",\n","    weight_decay=0.01,\n","    logging_steps=50,\n","    save_total_limit=2\n",")\n","\n","trainer = Trainer(\n","    model=model,\n","    args=training_args,\n","    train_dataset=train_dataset,\n","    eval_dataset=val_dataset,\n","    compute_metrics=compute_metrics,\n","    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]\n",")\n","\n","trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":945,"referenced_widgets":["3c39bfc8dcf649d7bbc76866a11af3ba","85118340c03b4e98887bf16d3abcb38a","f4cda4818f194cd191fc5d137bd45277","4061c9b45e4f4962855dc9938e6a2ae1","0e7ab6083cf24298b923f3e8a346cc78","5f1e7509607244e6836ee99e1c429c98","8f04db44195f4122be777e0580511aad","fccef3eed21442778aad24c132563279","22fef89be0d44e59a854a0010be1ff38","cd780399a0bf4d35aa6fe6305b8f74c2","e660cb5a717b4eb097d616a7cd4b9e57","a4a39cea6bb34028b33d30fbbd0239ed","7f4b05f0e35e4859ab2b1d43d4954ba7","7d432ff75596429cbf9ad1a18402b0fd","0236ee66c06a46b0a307abe3b0727458","93b024ed2aa24ac39e4cc17ea3794ff3","2b56820ffe19480a863b2e3afcb9a770","df8f349afed7446bbece1a4bf61362fb","e7af1374a7dc49b18d459a3e35b89fd8","a2a2c8099aec4c3bb4b7a17d6428509b","a5a230910fb042d29e6ded52e808693d","d394c6878dde4c13a33242637b86c534","a64d1a858d064b998753a56ba9290479","f246333ec6544ac89ef3bf99b101190d","8cdbf7ee87c442a5b4f3826e8e8dd836","f23ef1b450c5414db51879435af3033d","381aacc7dca04b17872f19005a614fc0","3894a3b8685449bba990c9648c2a433b","7a8336c017ea4ab0b6541ca479f6a181","be41d6c574ec48a5ad8af917dd0880d2","237d7ffdb20b4d9e9b467ca51a786aff","20162bf7c39e4b2fb14a8fc72f7a0b1a","e70ec9ac091143a18311bc103939f6a9","116b5e03072440a3b3ff776562d07b36","ece034d8ec7847a1a01fb32baeb55242","2391e00136484b0c9b9f0e916566394b","78a4e7dccd18485697ab86fc8e85dd12","2a3a5a3249274a37be44eaeed1071de5","e649706b6f6a42e184386fc57848eace","494df0a9474649ffa54067e0f5785125","598a5fd8b31644a0822130c86e9167ad","4514755586b24121a8e709f209ba40e8","020f17fff89049c2a3447c34970383c2","41420205ad0345438f62b382735079af","733afd477877434c86e2b82ac2ee469f","3750671ee9864c3db47054a90f8aacae","b8f59a8ee0724c54b07f9164b54e001e","7d3f5ab1f03f4b568f93999f9fb2b04a","cc8d284e26bb44cca5d21ae4097c09fd","955a1a83d0ea433d9cc5f036fb4c0b9e","53d3464a0dc546e6bfcd5c0c4ff4918c","2e7b8eb606fc42e489768a3ed9717b38","d3843673edf54f089154caf576ddf585","10f7d9d13e114faa9c5aa297b76eee00","cc681feda79c4f9cb244e8bfec76c5fa","069d8706724242bfa46cf7ac727fa254","f6e5084c192b4be0b9e8126ba5aac54d","dea3cb05d68e43c79b43ec2417f9a9c6","5db7f18519754bbbba871ca431e63336","a7ce74fa8ecf429bbab7e34e0143386e","b1692ec227754d6fa41377087fbc177d","1333eef4caf34a3cbdbc3f84f45e01d3","66a9bfe7f35e4da98a821b538a861597","aa9a551a2b55471193d844f54d553589","4c3967eb8e9a429ca924ce313c472456","2d685340a6a5484590e581ef02aad8d0","f09249ca778b425eaee67655a0044360","2518098d96c84865b5851d5291204a2a","1d383f5ecb65456187f30acd8d854bde","0834ebfb9fb948d698d8f3bc312aaa90","136e7445301f4c40bf04f6fac10d8ab7","70ef524e227e424583322d2b787e1131","215e9fa57b06465bb432ff80657120ca","c6d6746d3b274253b26ef88287bac2cc","6899dd170ed34b88b1ac1da8df0b6e9a","613b517b24e74b6983fc8c0e99dfe63b","75175a5cca17473f96c5759a6d87488d","285f38fcd7d5440ea3dbd2fcc620649d","f6c1563029574e839bbcf41f5bc2e486","97461b5c37c54790805277fb822ccdbd","43f7d9765c8343159b20156706dffb01","2e49a224618d4796a79374a7aa0b718d","e870c348bf80450eb779f2f14ad61e7e","5a843a742a254e2395c624bf139108c0","8a14536dda7541618d046ebc252dfb90","ea1b28a100714c68936a3cfb34bd2289","b69352c973674c2daa9bf0600f2d1005","1441a37758cd4e66ab65341e6363be87","dc34a1e71f634d33921162d782205171","c6e24193c7ac4f4b8d413b8e16dd560c","cd8b7dbd6aaf44d8be088db2db97fa37","65304a216e4548ed9f674b573bf2f5de","00e183129e074a49877ba18b9eccb040","b6ab5f7469e44ceca3d50421d0014156","bec0e43014d744d7848e44b4674e481c","162fa7dc41e940a092ccd4db19752f7f","97e257bc0e5b4bb6a43e34121a27aa54","7596aad682964ad1b82a740adab6422f","6d7322e3b2c344589f4b631e69550474","6a5cd0239175404f91bab9a377122ed2","03c7475956424e3cac0f39af75da898f","441e75a16cde49a8bae741247f9ac33f","ae970c8f827147dcbaceebb0e59e972d","d8fc4e08eb404dce87591732c4293dc7","ac26b802b1dc4132853c9760751b56be","1b6ef361661d42baa6740ecbc74c786a","9ae2d7cbff2d4f5ab2fb06d19b521f43","9617613dcbc740409f22d580148086d9","616c2e7495af452e9c089137a87c84ae","c0541154120f4f1ba381230edccee911","e0fee17ae8654943a83ec3b25eda782c","010971ed314f4a0aa681479f97cfa68c","fbb132225e554c1991a89f1722734cee","71a4f40f22f3411db50407330380cf8a","37214fefa5944f8b94df70d84f32398c","538c959a09994192840eceecbeb98f29","19e261abd89943dbafb734b9b6cb3c8f","e9989e5c3f974345a10f4bdbae88ac97","cc9206d54df44865920b6e7d684284a0","a9e9ff852fd94465847945a1ad8ac0a1","f0641799e58b43b096264648d71fe043","8d4f0af9c72a4f25a92a459b50699c94","a7a03dbee98c406ba5f80fcafbefb83d","678f4029df5e4fa497e06c4a27dc0b31","2da9780778f446b28b0835357b656f5b","ab9f311e03134db6b33e48857dbc1769","d5c66678847d40bc9cd990c7fdd72fa0","39a2020de3f14db583f949406a93e64f","20c45006e5604f3f95a2060cb5018a65","bd04a0bac92e4685a0cc94b7d728dfae","ee88e6272ff743ffb45d799e51f9a2bd","d69b284ac1dd495083985a0248256908"]},"id":"5qH3WqJX1Cly","executionInfo":{"status":"ok","timestamp":1772044200577,"user_tz":-330,"elapsed":2952248,"user":{"displayName":"jaimy","userId":"07332807517928872066"}},"outputId":"d478514a-7a7c-4481-8592-65a8797cb4a0"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='5622' max='5622' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [5622/5622 49:08, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","      <th>F1</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>500</td>\n","      <td>0.302142</td>\n","      <td>0.243759</td>\n","      <td>0.916478</td>\n","      <td>0.906412</td>\n","      <td>0.913268</td>\n","      <td>0.916478</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>0.223495</td>\n","      <td>0.246063</td>\n","      <td>0.929820</td>\n","      <td>0.922432</td>\n","      <td>0.929176</td>\n","      <td>0.929820</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>0.176037</td>\n","      <td>0.234554</td>\n","      <td>0.937692</td>\n","      <td>0.933345</td>\n","      <td>0.935692</td>\n","      <td>0.937692</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>0.253113</td>\n","      <td>0.211210</td>\n","      <td>0.939159</td>\n","      <td>0.934797</td>\n","      <td>0.937539</td>\n","      <td>0.939159</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>0.190306</td>\n","      <td>0.220906</td>\n","      <td>0.939693</td>\n","      <td>0.935668</td>\n","      <td>0.937851</td>\n","      <td>0.939693</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>0.170935</td>\n","      <td>0.266394</td>\n","      <td>0.937158</td>\n","      <td>0.935212</td>\n","      <td>0.934665</td>\n","      <td>0.937158</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>0.180439</td>\n","      <td>0.206249</td>\n","      <td>0.944096</td>\n","      <td>0.941289</td>\n","      <td>0.942148</td>\n","      <td>0.944096</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>0.151763</td>\n","      <td>0.213938</td>\n","      <td>0.945430</td>\n","      <td>0.943123</td>\n","      <td>0.943462</td>\n","      <td>0.945430</td>\n","    </tr>\n","    <tr>\n","      <td>4500</td>\n","      <td>0.235877</td>\n","      <td>0.212478</td>\n","      <td>0.944096</td>\n","      <td>0.942184</td>\n","      <td>0.942018</td>\n","      <td>0.944096</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>0.223067</td>\n","      <td>0.214926</td>\n","      <td>0.946631</td>\n","      <td>0.944387</td>\n","      <td>0.944774</td>\n","      <td>0.946631</td>\n","    </tr>\n","    <tr>\n","      <td>5500</td>\n","      <td>0.241155</td>\n","      <td>0.206532</td>\n","      <td>0.946498</td>\n","      <td>0.943945</td>\n","      <td>0.944749</td>\n","      <td>0.946498</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3c39bfc8dcf649d7bbc76866a11af3ba"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a4a39cea6bb34028b33d30fbbd0239ed"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a64d1a858d064b998753a56ba9290479"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Writing model shards:   0%|          | 0/1 [00:00<?, 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?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e0fee17ae8654943a83ec3b25eda782c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8d4f0af9c72a4f25a92a459b50699c94"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["There were missing keys in the checkpoint model loaded: ['albert.embeddings.LayerNorm.weight', 'albert.embeddings.LayerNorm.bias', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.weight', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.bias'].\n","There were unexpected keys in the checkpoint model loaded: ['albert.embeddings.LayerNorm.beta', 'albert.embeddings.LayerNorm.gamma', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.beta', 'albert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.gamma'].\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=5622, training_loss=0.21223701560510005, metrics={'train_runtime': 2951.6805, 'train_samples_per_second': 30.469, 'train_steps_per_second': 1.905, 'total_flos': 537312970920960.0, 'train_loss': 0.21223701560510005, 'epoch': 3.0})"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["results = trainer.evaluate()\n","print(results)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"id":"dhmc0NXb1Mf1","executionInfo":{"status":"ok","timestamp":1772044280781,"user_tz":-330,"elapsed":72078,"user":{"displayName":"jaimy","userId":"07332807517928872066"}},"outputId":"6ed569e6-e92d-41b6-ffdd-a649e33e842d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='469' max='469' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [469/469 01:11]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["{'eval_loss': 0.21487681567668915, 'eval_accuracy': 0.9466310873915944, 'eval_f1': 0.9443871681522494, 'eval_precision': 0.9447744475658005, 'eval_recall': 0.9466310873915944, 'eval_runtime': 72.071, 'eval_samples_per_second': 103.995, 'eval_steps_per_second': 6.507, 'epoch': 3.0}\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","predictions = trainer.predict(val_dataset)\n","preds = np.argmax(predictions.predictions, axis=1)\n","\n","cm = confusion_matrix(val_labels, preds)\n","\n","sns.heatmap(cm, annot=True, fmt=\"d\")\n","plt.xlabel(\"Predicted\")\n","plt.ylabel(\"Actual\")\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"5LBPWIWM1Qrd","executionInfo":{"status":"ok","timestamp":1772044501026,"user_tz":-330,"elapsed":73384,"user":{"displayName":"jaimy","userId":"07332807517928872066"}},"outputId":"0146c987-0a87-4906-ebd3-4f181a76713e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 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Op0tNr2mkkUMfUNNrGunu7l3V667Omr/wU49xN7Zsro9SX9X/pUzWzW1badRTyfrpeH657jX0wfvV8rq/qek1jTS43z0adH9vvT3nQy9+G6Dye2nyTLW+MVadu/RRaWmpUt+aanVIQIWwLCEZMWKE7r77bh04cECpqal64YUX9MILLyg1NVXZ2dnq3bu3RowYYTjPuV7F/MLUlAr4BpeGurVD9T9XNvA4d9WV9XX4D/9PL8D/MjW4IlLNr22qZ5MS5OPjowWLz794tSya/a2JcvOOqri4+C/NA1gtJ/e3R2+Hh3tWfcPD6ignJ8/j3E8/HdeePfv02covdH+/f6pr147nbevg4uFyOr1yVGWWJSTffPONEhISZLPZzrpms9mUkJCgzMxMw3mSkpJUUFDgcYx9dKgJEV+aWlwXrf3ZBz3O/ZD9o+r9ocz8R06nU8UlJX/p3rv2fK+gmoHy8/P7S/MAVsvKytbhw7m6vcMt7nM1awbqxhtbaOOmjPN+rlq13/79aLfzN3DRqwQv16vsLFtDEhERoc2bN6tJkybnvL5582aFhxvvvz/Xq5hLio96JUZI/e/tof7/eEyzZs9V547tte3b3fpw0TI9PeYRSdIvv57SrNlz1eGWNqpbJ1TH8wv1/oLFyjv6k2I7/PepoIdz8lRQ+LMO5+aptNSpXd99L0lqcEWkAgL8tXrdRh09lq/m1zaR3c9PG7Z8pTfemaeB9/Wy5HsD5VWjRoDHc0Wirmyg5s3/pmPHjuvAgUOa9sobeiLpEe3Zu8+97ffQoVx98slvlcQbb2ih1q2ba/2GLTp+PF//c9WVemb8aO3dm6X0jedPWnCRqOILUr3BsoRk1KhRevjhh5WRkaGOHTu6k4/c3FytXLlSr7/+ul566SWrwsP/16xpY72c/JSmpqQqJXWOLq8XobGP/kN3xt4uSfKpVk1ZPxzQomWf6XhBgUKCgnRt02s0e8aLanRVQ/c80994V58s+8z9c+8Hh0uS3nrlBd3Y8jr5+vpq7oLFmjRtllxyqcHlkRo94mH1vqtzxX5h4AK1btVcKz/775qnyS+NlyTNfme+Bg9J0IsvzVCNGgFKmTFJISFBWr9+i+K69XOvefvl11/19x5d9fS4UapRw1+HD+dpxX9Wa2LyVNqWuCRY+i6befPmacqUKcrIyFBpaamk317m06pVKyUmJuqee+65oHl5lw1wbrzLBjhbRbzL5uSEvl6Zp8a497wyT2Vk6bbfe++9V/fee69KSkp09OhvbZY6deqoevXqVoYFAIB3VfEFqd5QKZ7UWr16ddWrV8/qMAAAgEUqRUICAECVVsV3yHgDCQkAAGZjl40h3mUDAAAsR4UEAACz0bIxREICAIDJqvpj372Blg0AALAcFRIAAMxGy8YQCQkAAGYjITFEQgIAgNnY9muINSQAAMByVEgAADAbLRtDJCQAAJjMRUJiiJYNAACwHBUSAADMRoXEEAkJAABm40mthmjZAAAAy1EhAQDAbLRsDJGQAABgNhISQ7RsAACA5aiQAABgMpeLCokREhIAAMxGy8YQCQkAAGYjITHEGhIAAGA5KiQAAJiMd9kYIyEBAMBsJCSGaNkAAADLUSEBAMBsvMrGEAkJAAAmYw2JMVo2AADAclRIAAAwGxUSQyQkAACYjTUkhmjZAAAAy1EhAQDAZCxqNUZCAgCA2WjZGKJlAwCAyVxOl1eOv+L555+XzWbTyJEj3edOnTql+Ph41a5dW4GBgerVq5dyc3M9Ppedna24uDgFBAQoLCxMo0eP1unTpz3GrF69Wi1btpTdblejRo2Umppa7vhISAAAqOK2bNmi1157Tdddd53H+YSEBC1evFgffPCB1qxZo0OHDqlnz57u66WlpYqLi1NxcbE2bNig2bNnKzU1VePGjXOPycrKUlxcnDp06KDMzEyNHDlSQ4YM0YoVK8oVo83lclW5xlbJ0X1WhwBUSv6R7awOAah0Thf/aPo9jnW/1SvzhH6yptyfOXHihFq2bKkZM2boueee0/XXX6+XX35ZBQUFqlu3rubMmaPevXtLknbt2qWmTZsqPT1dbdu21bJly3TnnXfq0KFDCg8PlySlpKRo7NixOnLkiPz8/DR27FgtXbpU27dvd9+zT58+ys/P1/Lly8scJxUSAABM5nJ65ygqKlJhYaHHUVRU9Kf3jo+PV1xcnGJiYjzOZ2RkqKSkxON8kyZN1KBBA6Wnp0uS0tPT1axZM3cyIkmxsbEqLCzUjh073GP+OHdsbKx7jrIiIQEA4CKRnJys4OBgjyM5Ofm84+fOnauvvvrqnGNycnLk5+enkJAQj/Ph4eHKyclxj/l9MnLm+plrfzamsLBQv/76a5m/G7tsAAAwm5d22SQlJSkxMdHjnN1uP+fYAwcO6NFHH1VaWpouu+wy7wRgIiokAACYzFstG7vdrqCgII/jfAlJRkaG8vLy1LJlS/n6+srX11dr1qzRtGnT5Ovrq/DwcBUXFys/P9/jc7m5uYqIiJAkRUREnLXr5szPRmOCgoLk7+9f5t8RCQkAAFVQx44dtW3bNmVmZrqP1q1bq2/fvu5/rl69ulauXOn+zO7du5WdnS2HwyFJcjgc2rZtm/Ly8txj0tLSFBQUpOjoaPeY389xZsyZOcqKlg0AAGaz4MFoNWvW1LXXXutxrkaNGqpdu7b7/ODBg5WYmKjQ0FAFBQVpxIgRcjgcatu2rSSpU6dOio6OVv/+/TVp0iTl5OToySefVHx8vLsyM3ToUE2fPl1jxozRoEGDtGrVKs2fP19Lly4tV7wkJAAAmMxVSZ/UOmXKFFWrVk29evVSUVGRYmNjNWPGDPd1Hx8fLVmyRMOGDZPD4VCNGjU0cOBATZgwwT0mKipKS5cuVUJCgqZOnaorrrhCb7zxhmJjY8sVC88hAS4hPIcEOFtFPIckr6N3nkMStrL8zyG5WLCGBAAAWI6WDQAAJqusLZvKhIQEAACzuWxWR1Dp0bIBAACWo0ICAIDJaNkYIyEBAMBkLictGyO0bAAAgOWokAAAYDJaNsZISAAAMJmLXTaGaNkAAADLUSEBAMBktGyMkZAAAGAydtkYIyEBAMBkVe81tt7HGhIAAGA5KiQAAJiMlo0xEhIAAExGQmKMlg0AALAcFRIAAEzGolZjJCQAAJiMlo0xWjYAAMByVEgAADAZ77IxRkICAIDJeHS8sTIlJIsWLSrzhHfdddcFBwMAAC5NZUpIevToUabJbDabSktL/0o8AABUOU5aNobKlJA4ndSaAAC4UKwhMcYaEgAATMa2X2MXlJCcPHlSa9asUXZ2toqLiz2uPfLII14JDAAAXDrKnZB8/fXX6tq1q3755RedPHlSoaGhOnr0qAICAhQWFkZCAgDAH/CkVmPlfjBaQkKCunXrpuPHj8vf318bN27UDz/8oFatWumll14yI0YAAC5qLqfNK0dVVu6EJDMzU4899piqVasmHx8fFRUVqX79+po0aZKeeOIJM2IEAABVXLkTkurVq6tatd8+FhYWpuzsbElScHCwDhw44N3oAACoApwum1eOqqzca0hatGihLVu26Oqrr9att96qcePG6ejRo3r33Xd17bXXmhEjAAAXNbb9Git3hWTixImqV6+eJOnf//63atWqpWHDhunIkSOaNWuW1wMEAABVX7krJK1bt3b/c1hYmJYvX+7VgAAAqGrYZWOMB6MBAGCyqr7+wxvKnZBERUXJZjv/L3bfvn1/KSAAAHDpKXdCMnLkSI+fS0pK9PXXX2v58uUaPXq0t+ICAKDKYFGrsXInJI8++ug5z7/66qv68ssv/3JAAABUNawhMVbuXTbn06VLF3300Ufemg4AgCqD55AY81pC8uGHHyo0NNRb0wEAgEvIBT0Y7feLWl0ul3JycnTkyBHNmDHDq8FdqFoNOlodAlApXR1yudUhAJck1pAYK3dC0r17d4+EpFq1aqpbt65uu+02NWnSxKvBAQBQFVT1dos3lDshGT9+vAlhAACAS1m515D4+PgoLy/vrPM//fSTfHx8vBIUAABVictLR1VW7gqJ6zx7l4qKiuTn5/eXAwIAoKqhZWOszAnJtGnTJEk2m01vvPGGAgMD3ddKS0u1du1a1pAAAIALUuaEZMqUKZJ+q5CkpKR4tGf8/Px05ZVXKiUlxfsRAgBwkWOXjbEyJyRZWVmSpA4dOmjBggWqVauWaUEBAFCVOK0O4CJQ7jUkn3/+uRlxAACAS1i5d9n06tVLL7zwwlnnJ02apLvvvtsrQQEAUJW4ZPPKUR4zZ87Uddddp6CgIAUFBcnhcGjZsmXu66dOnVJ8fLxq166twMBA9erVS7m5uR5zZGdnKy4uTgEBAQoLC9Po0aN1+vRpjzGrV69Wy5YtZbfb1ahRI6Wmpl7Q76jcCcnatWvVtWvXs8536dJFa9euvaAgAACoypwu7xzlccUVV+j5559XRkaGvvzyS91+++3q3r27duzYIUlKSEjQ4sWL9cEHH2jNmjU6dOiQevbs6f58aWmp4uLiVFxcrA0bNmj27NlKTU3VuHHj3GOysrIUFxenDh06KDMzUyNHjtSQIUO0YsWKcv+ObK7z7eM9D39/f2VmZqpx48Ye53ft2qUWLVro119/LXcQ3hYYEGV1CEClVD+wrtUhAJXOzrzNpt9jVfg9Xpnn5ux3VVRU5HHObrfLbreX6fOhoaF68cUX1bt3b9WtW1dz5sxR7969Jf323/GmTZsqPT1dbdu21bJly3TnnXfq0KFDCg8PlySlpKRo7NixOnLkiPz8/DR27FgtXbpU27dvd9+jT58+ys/P1/Lly8v13cpdIWnWrJnmzZt31vm5c+cqOjq6vNMBAIAySk5OVnBwsMeRnJxs+LnS0lLNnTtXJ0+elMPhUEZGhkpKShQTE+Me06RJEzVo0EDp6emSpPT0dDVr1sydjEhSbGysCgsL3VWW9PR0jznOjDkzR3mUe1HrU089pZ49e+r777/X7bffLklauXKl5syZow8//LDcAQAAUNWVd/3H+SQlJSkxMdHj3J9VR7Zt2yaHw6FTp04pMDBQH3/8saKjo5WZmSk/Pz+FhIR4jA8PD1dOTo4kKScnxyMZOXP9zLU/G1NYWKhff/1V/v7+Zf5u5U5IunXrpoULF2rixIn68MMP5e/vr+bNm2vVqlUKDQ0t73QAAFR53tr2W572jCQ1btxYmZmZKigo0IcffqiBAwdqzZo1XorGu8qdkEhSXFyc4uLiJEmFhYV6//33NWrUKGVkZKi0tNSrAQIAgAvj5+enRo0aSZJatWqlLVu2aOrUqbr33ntVXFys/Px8jypJbm6uIiIiJEkRERHavNlzfc2ZXTi/H/PHnTm5ubkKCgoqV3VEuoA1JGesXbtWAwcOVGRkpCZPnqzbb79dGzduvNDpAACosqzY9nsuTqdTRUVFatWqlapXr66VK1e6r+3evVvZ2dlyOBySJIfDoW3btnm8UDctLU1BQUHuNaMOh8NjjjNjzsxRHuWqkOTk5Cg1NVVvvvmmCgsLdc8996ioqEgLFy5kQSsAAOdhxZNak5KS1KVLFzVo0EA///yz5syZo9WrV2vFihUKDg7W4MGDlZiYqNDQUAUFBWnEiBFyOBxq27atJKlTp06Kjo5W//79NWnSJOXk5OjJJ59UfHy8u200dOhQTZ8+XWPGjNGgQYO0atUqzZ8/X0uXLi13vGWukHTr1k2NGzfW1q1b9fLLL+vQoUN65ZVXyn1DAABgvry8PA0YMECNGzdWx44dtWXLFq1YsUJ33HGHpN/eUXfnnXeqV69eat++vSIiIrRgwQL35318fLRkyRL5+PjI4XCoX79+GjBggCZMmOAeExUVpaVLlyotLU3NmzfX5MmT9cYbbyg2Nrbc8Zb5OSS+vr565JFHNGzYMF199dXu89WrV9c333xTqSokPIcEODeeQwKcrSKeQ/JpeB+vzNM1d65X5qmMylwhWbdunX7++We1atVKbdq00fTp03X06FEzYwMAoEqoLGtIKrMyJyRt27bV66+/rsOHD+sf//iH5s6dq8jISDmdTqWlpennn382M04AAFCFlXuXTY0aNTRo0CCtW7dO27Zt02OPPabnn39eYWFhuuuuu8yIEQCAi5rT5p2jKrvgbb/Sbw9cmTRpkg4ePKj333/fWzEBAFClOGXzylGVXdCD0f7Ix8dHPXr0UI8ePbwxHQAAVUo5X9R7SfpLFRIAAABv8EqFBAAAnJ8VD0a72JCQAABgMqetaq//8AZaNgAAwHJUSAAAMBmLWo2RkAAAYDLWkBijZQMAACxHhQQAAJNV9aesegMJCQAAJqvqT1n1Blo2AADAclRIAAAwGbtsjJGQAABgMtaQGCMhAQDAZGz7NcYaEgAAYDkqJAAAmIw1JMZISAAAMBlrSIzRsgEAAJajQgIAgMlY1GqMhAQAAJORkBijZQMAACxHhQQAAJO5WNRqiIQEAACT0bIxRssGAABYjgoJAAAmo0JijIQEAACT8aRWYyQkAACYjCe1GmMNCQAAsBwVEgAATMYaEmMkJAAAmIyExBgtGwAAYDkqJAAAmIxdNsZISAAAMBm7bIzRsgEAAJajQgIAgMlY1GqMhAQAAJOxhsQYLRsAAGA5KiQAAJjMSY3EEAkJAAAmYw2JMRISAABMRn3EGGtIAACA5aiQAABgMlo2xkhIAAAwGU9qNUbLBgAAWI6EBAAAkznl8spRHsnJybrhhhtUs2ZNhYWFqUePHtq9e7fHmFOnTik+Pl61a9dWYGCgevXqpdzcXI8x2dnZiouLU0BAgMLCwjR69GidPn3aY8zq1avVsmVL2e12NWrUSKmpqeX+HZGQAABgMpeXjvJYs2aN4uPjtXHjRqWlpamkpESdOnXSyZMn3WMSEhK0ePFiffDBB1qzZo0OHTqknj17uq+XlpYqLi5OxcXF2rBhg2bPnq3U1FSNGzfOPSYrK0txcXHq0KGDMjMzNXLkSA0ZMkQrVqwoV7w2l8tV5XYjBQZEWR0CUCnVD6xrdQhApbMzb7Pp9/jXlfd7ZZ5/759zwZ89cuSIwsLCtGbNGrVv314FBQWqW7eu5syZo969e0uSdu3apaZNmyo9PV1t27bVsmXLdOedd+rQoUMKDw+XJKWkpGjs2LE6cuSI/Pz8NHbsWC1dulTbt29336tPnz7Kz8/X8uXLyxwfFRIAAEzm9NJRVFSkwsJCj6OoqKhMMRQUFEiSQkNDJUkZGRkqKSlRTEyMe0yTJk3UoEEDpaenS5LS09PVrFkzdzIiSbGxsSosLNSOHTvcY34/x5kxZ+YoKxISAABM5q01JMnJyQoODvY4kpOTje/vdGrkyJG6+eabde2110qScnJy5Ofnp5CQEI+x4eHhysnJcY/5fTJy5vqZa382prCwUL/++muZf0ds+wUA4CKRlJSkxMREj3N2u93wc/Hx8dq+fbvWrVtnVmh/GQkJAAAm89ZiTbvdXqYE5PeGDx+uJUuWaO3atbriiivc5yMiIlRcXKz8/HyPKklubq4iIiLcYzZv9lxjc2YXzu/H/HFnTm5uroKCguTv71/mOGnZAABgMm+tISkPl8ul4cOH6+OPP9aqVasUFeW54aNVq1aqXr26Vq5c6T63e/duZWdny+FwSJIcDoe2bdumvLw895i0tDQFBQUpOjraPeb3c5wZc2aOsqJCAgCAycr7DBFviI+P15w5c/TJJ5+oZs2a7jUfwcHB8vf3V3BwsAYPHqzExESFhoYqKChII0aMkMPhUNu2bSVJnTp1UnR0tPr3769JkyYpJydHTz75pOLj492VmqFDh2r69OkaM2aMBg0apFWrVmn+/PlaunRpueKlQgIAQBU0c+ZMFRQU6LbbblO9evXcx7x589xjpkyZojvvvFO9evVS+/btFRERoQULFriv+/j4aMmSJfLx8ZHD4VC/fv00YMAATZgwwT0mKipKS5cuVVpampo3b67JkyfrjTfeUGxsbLni5TkkwCWE55AAZ6uI55AkXNnHK/NM2T/XK/NURrRsAAAwGW/7NUbLBgAAWI4KCQAAJnNZsKj1YkNCAgCAyWjZGKNlAwAALEeFBAAAk1nxHJKLDQkJAAAmIx0xRssGAABYjoQE5fLYqGFa88VCHc7dpqz9W/T+vNd09dVXeYyJimqg9+emaP8PX+pQzla98+50hYXVcV9v166NTvySdc6jZavrKvorAV7x2ZcLtTNv81nHU8+PliTVCautF14dr7Xblykja40++uwd3XFnB485gkOCNGnmBG35fpU27Vmp56Y8qYAaZX85GSovp1xeOaoyEhKUyy3t2mjWa+/q9tt6qlu3Aape3VefLH5HAQG//UszIMBfnyx+Ry6XS3Fd++qOjnerul91zf/wDdlsNknSxo1f6aqoGzyOt9+eq6ysbH2VsdXKrwdcsLtjH1C7a7u4j0G94yVJyxf99tKx56c/rSv/p6Hi+z+m7rfdp7SlqzXl9Ylqeu017jkmzZygRo2v0uC7R2hYv0S1dlyvZ156wpLvA++y4uV6FxvWkKBc/t79AY+fhz48WvuzM9SiRTOtX79ZbR2t1bDhFbrZcad+/vmEJOkfD43SwUOZuvW2m7T68/UqKSlRXu5R9xy+vr66My5GKSnvVORXAbzq+E/5Hj8/NGKAfsg6oC0bvpIkXX/DdZow5gVt+/pbSVLKlLc08B/36W/Nm2rn9u901dVXqn3Hm9T7joHa8c1OSdJzSS/ptfdf1qTxU3Xkd38zuPjwHBJjVEjwlwQF1ZQkHT+eL0my2/3kcrlUVFTsHnPqVJGcTqduuqn1OeeIi4tRaO1aevfdD0yPF6gI1av7qlvvLlowZ7H7XOaWrerS/Q4FhwTJZrOpa4875Gf30+YNGZKk61s3U0F+oTsZkaT0tVvkdDrVvNW1Ff4dgIp20SckRUVFKiws9Diq4PsCKyWbzaYXXnxKGzZs0bfffidJ2rL5a508+YuefW6s/P0vU0CAvyYmPyFfX19FRISdc54BD9yjzz5bq0M/5lRk+IBpOna5TTWDA/Xx3CXucwlDnpBvdV9t/O4zfXNwvca/lKQRD45RdtZBSb+tMTl29LjHPKWlpSo4Xqg6YbUrNH54Hy0bY5U6ITlw4IAGDRr0p2OSk5MVHBzscZSczq+YAC9xU16eoOjoxnpg4CPuc0ePHlP/fsPVpWtH5R7ZoUM5WxUcHKSvv94mp/PsP6fIyyMUE9Ne76TOr8jQAVP16nuXvliZ7tFmeeTxoaoZFKgHe8Xr7k4DlZoyR1Nen6irm/6PhZGiori89L+qrFInJMeOHdPs2bP/dExSUpIKCgo8juq+IRUT4CVs8v8+o85dblfXzvedVdlYtfILXXftbYpq2FoN67fUQ0MSFRkZoaysA2fN07//3Tr203EtXfpZRYUOmCryigg52t+gD9/7xH2u/pWXq9+Qe/TkyOe08Yst2r1jj2a89IZ2fLNT9w+6W5J0NO8nhdap5TGXj4+PgmsF6WjeTxX6HQArWLqoddGiRX96fd++fYZz2O122e12j3NndnPAHJP/9xl1u6uTusTepx9+OHjecT/99Fv5+dZbHapbt7Y+PUfS0a9/b82Z87FOnz5tWrxARfr7fd107OhxrUlb7z53mf9lknRWlbC01Klq///fV5lfblNwSJCir2uib7fukiS1adda1apV0zcZ2ysoepilqrdbvMHShKRHjx6y2Wx/uuaD5KJymfLyBN19T3f1uedh/XzihMLCf3u+SGHBzzp1qkjSb0nG7l17dfToMd3YpqUmvThO0195S3v2eCaYt912k6KiGmh26twK/x6AGWw2m3r2uVML5y1VaWmp+3zWnv36YV+2nnkpSZPGT1X+8QJ17HKrbrr1Rg3rmyhJ2rdnv9au3KBn//cJjR/9vHyr++qp5NH69OM0dthUAU7WNhqyuSxcAXr55ZdrxowZ6t69+zmvZ2ZmqlWrVh5/2GURGBDljfBwDid+yTrn+X88PErv/d9HkqRnJoxRv/69VatWsH744Ue9+cZ7mv7Km2d95q23X1b9Bpfrjo53mxoz/qt+YF2rQ6jSbrqtjd6c/4q6tO2t/fuyPa41jKqvxKfi1bJNcwUEBCh7/0G9PeP/tOiDZe4xwSFBejJ5tDrE3iKn06X/LFmlif+arF9O/lrRX+WSsjNvs+n36N+wp1fmefeHBV6ZpzKyNCG56667dP3112vChAnnvP7NN9+oRYsW51wM+WdISIBzIyEBzlYRCUk/LyUk/1eFExJLWzajR4/WyZMnz3u9UaNG+vzzzyswIgAAvK+qP/bdGyxNSNq1a/en12vUqKFbb721gqIBAABW4dHxAACYrKo/Q8QbSEgAADAZ236NkZAAAGAy1pAYq9RPagUAAJcGKiQAAJiMNSTGSEgAADAZa0iM0bIBAACWo0ICAIDJLHwo+kWDhAQAAJOxy8YYLRsAAGA5KiQAAJiMRa3GSEgAADAZ236N0bIBAACWo0ICAIDJWNRqjIQEAACTse3XGAkJAAAmY1GrMdaQAAAAy1EhAQDAZOyyMUZCAgCAyVjUaoyWDQAAsBwVEgAATMYuG2MkJAAAmIyWjTFaNgAAwHJUSAAAMBm7bIyRkAAAYDIna0gM0bIBAACWo0ICAIDJqI8YIyEBAMBk7LIxRssGAACTOeXyylFea9euVbdu3RQZGSmbzaaFCxd6XHe5XBo3bpzq1asnf39/xcTEaM+ePR5jjh07pr59+yooKEghISEaPHiwTpw44TFm69atateunS677DLVr19fkyZNKnesJCQAAFRRJ0+eVPPmzfXqq6+e8/qkSZM0bdo0paSkaNOmTapRo4ZiY2N16tQp95i+fftqx44dSktL05IlS7R27Vo9/PDD7uuFhYXq1KmTGjZsqIyMDL344osaP368Zs2aVa5Yba4q+Pi4wIAoq0MAKqX6gXWtDgGodHbmbTb9Hm0jb/PKPGuyVqioqMjjnN1ul91uN/yszWbTxx9/rB49ekj6rToSGRmpxx57TKNGjZIkFRQUKDw8XKmpqerTp4927typ6OhobdmyRa1bt5YkLV++XF27dtXBgwcVGRmpmTNn6l//+pdycnLk5+cnSXr88ce1cOFC7dq1q8zfjQoJAAAm81bLJjk5WcHBwR5HcnLyBcWUlZWlnJwcxcTEuM8FBwerTZs2Sk9PlySlp6crJCTEnYxIUkxMjKpVq6ZNmza5x7Rv396djEhSbGysdu/erePHj5c5Hha1AgBwkUhKSlJiYqLHubJUR84lJydHkhQeHu5xPjw83H0tJydHYWFhHtd9fX0VGhrqMSYqKuqsOc5cq1WrVpniISEBAMBk3npSa1nbMxcjWjYAAJjM5XJ55fCmiIgISVJubq7H+dzcXPe1iIgI5eXleVw/ffq0jh075jHmXHP8/h5lQUICAMAlKCoqShEREVq5cqX7XGFhoTZt2iSHwyFJcjgcys/PV0ZGhnvMqlWr5HQ61aZNG/eYtWvXqqSkxD0mLS1NjRs3LnO7RiIhAQDAdFY9h+TEiRPKzMxUZmampN8WsmZmZio7O1s2m00jR47Uc889p0WLFmnbtm0aMGCAIiMj3TtxmjZtqs6dO+uhhx7S5s2btX79eg0fPlx9+vRRZGSkJOn++++Xn5+fBg8erB07dmjevHmaOnXqWWtdjLCGBAAAk1n1hI0vv/xSHTp0cP98JkkYOHCgUlNTNWbMGJ08eVIPP/yw8vPzdcstt2j58uW67LLL3J957733NHz4cHXs2FHVqlVTr169NG3aNPf14OBg/ec//1F8fLxatWqlOnXqaNy4cR7PKikLnkMCXEJ4Dglwtop4DkmLiJu9Ms/XOeu9Mk9lRIUEAACT8S4bYyQkAACYzFvbfqsyEhIAAEzmrHqrI7yOXTYAAMByVEgAADAZLRtjJCQAAJiMlo0xWjYAAMByVEgAADAZLRtjJCQAAJiMlo0xWjYAAMByVEgAADAZLRtjJCQAAJiMlo0xWjYAAMByVEgAADAZLRtjJCQAAJjM5XJaHUKlR0ICAIDJnFRIDLGGBAAAWI4KCQAAJnOxy8YQCQkAACajZWOMlg0AALAcFRIAAExGy8YYCQkAACbjSa3GaNkAAADLUSEBAMBkPKnVGAkJAAAmYw2JMVo2AADAclRIAAAwGc8hMUZCAgCAyWjZGCMhAQDAZGz7NcYaEgAAYDkqJAAAmIyWjTESEgAATMaiVmO0bAAAgOWokAAAYDJaNsZISAAAMBm7bIzRsgEAAJajQgIAgMl4uZ4xEhIAAExGy8YYLRsAAGA5KiQAAJiMXTbGSEgAADAZa0iMkZAAAGAyKiTGWEMCAAAsR4UEAACTUSExRkICAIDJSEeM0bIBAACWs7moI8EkRUVFSk5OVlJSkux2u9XhAJUGfxvA2UhIYJrCwkIFBweroKBAQUFBVocDVBr8bQBno2UDAAAsR0ICAAAsR0ICAAAsR0IC09jtdj399NMs2gP+gL8N4GwsagUAAJajQgIAACxHQgIAACxHQgIAACxHQgIAACxHQgLTvPrqq7ryyit12WWXqU2bNtq8ebPVIQGWWrt2rbp166bIyEjZbDYtXLjQ6pCASoOEBKaYN2+eEhMT9fTTT+urr75S8+bNFRsbq7y8PKtDAyxz8uRJNW/eXK+++qrVoQCVDtt+YYo2bdrohhtu0PTp0yVJTqdT9evX14gRI/T4449bHB1gPZvNpo8//lg9evSwOhSgUqBCAq8rLi5WRkaGYmJi3OeqVaummJgYpaenWxgZAKCyIiGB1x09elSlpaUKDw/3OB8eHq6cnByLogIAVGYkJAAAwHIkJPC6OnXqyMfHR7m5uR7nc3NzFRERYVFUAIDKjIQEXufn56dWrVpp5cqV7nNOp1MrV66Uw+GwMDIAQGXla3UAqJoSExM1cOBAtW7dWjfeeKNefvllnTx5Ug8++KDVoQGWOXHihPbu3ev+OSsrS5mZmQoNDVWDBg0sjAywHtt+YZrp06frxRdfVE5Ojq6//npNmzZNbdq0sToswDKrV69Whw4dzjo/cOBApaamVnxAQCVCQgIAACzHGhIAAGA5EhIAAGA5EhIAAGA5EhIAAGA5EhIAAGA5EhIAAGA5EhIAAGA5EhIAAGA5EhKgCnrggQfUo0cP98+33XabRo4cWeFxrF69WjabTfn5+RV+bwAXFxISoAI98MADstlsstls8vPzU6NGjTRhwgSdPn3a1PsuWLBAzz77bJnGkkQAsAIv1wMqWOfOnfX222+rqKhIn376qeLj41W9enUlJSV5jCsuLpafn59X7hkaGuqVeQDALFRIgApmt9sVERGhhg0batiwYYqJidGiRYvcbZZ///vfioyMVOPGjSVJBw4c0D333KOQkBCFhoaqe/fu2r9/v3u+0tJSJSYmKiQkRLVr19aYMWP0x1dU/bFlU1RUpLFjx6p+/fqy2+1q1KiR3nzzTe3fv9/98rdatWrJZrPpgQcekCQ5nU4lJycrKipK/v7+at68uT788EOP+3z66ae65ppr5O/vrw4dOnjECQB/hoQEsJi/v7+Ki4slSStXrtTu3buVlpamJUuWqKSkRLGxsapZs6a++OILrV+/XoGBgercubP7M5MnT1ZqaqreeustrVu3TseOHdPHH3/8p/ccMGCA3n//fU2bNk07d+7Ua6+9psDAQNWvX18fffSRJGn37t06fPiwpk6dKklKTk7WO++8o5SUFO3YsUMJCQnq16+f1qxZI+m3xKlnz57q1q2bMjMzNWTIED3++ONm/doAVDUuABVm4MCBru7du7tcLpfL6XS60tLSXHa73TVq1CjXwIEDXeHh4a6ioiL3+HfffdfVuHFjl9PpdJ8rKipy+fv7u1asWOFyuVyuevXquSZNmuS+XlJS4rriiivc93G5XK5bb73V9eijj7pcLpdr9+7dLkmutLS0c8b4+eefuyS5jh8/7j536tQpV0BAgGvDhg0eYwcPHuy67777XC6Xy5WUlOSKjo72uD527Niz5gKAc2ENCVDBlixZosDAQJWUlMjpdOr+++/X+PHjFR8fr2bNmnmsG/nmm2+0d+9e1axZ02OOU6dO6fvvv1dBQYEOHz6sNm3auK/5+vqqdevWZ7VtzsjMzJSPj49uvfXWMse8d+9e/fLLL7rjjjs8zhcXF6tFixaSpJ07d3rEIUkOh6PM9wBwaSMhASpYhw4dNHPmTPn5+SkyMlK+vv/9M6xRo4bH2BMnTqhVq1Z67733zpqnbt26F3R/f3//cn/mxIkTkqSlS5fq8ssv97hmt9svKA4A+D0SEqCC1ahRQ40aNSrT2JYtW2revHkKCwtTUFDQOcfUq1dPmzZtUvv27SVJp0+fVkZGhlq2bHnO8c2aNZPT6dSaNWsUExNz1vUzFZrS0lL3uejoaNntdmVnZ5+3stK0aVMtWrTI49zGjRuNvyQAiEWtQKXWt29f1alTR927d9cXX3yhrKwsrV69Wo888ogOHjwoSXr00Uf1/PPPa+HChdq1a5f++c9//ukzRK688koNHDhQgwYN0sKFC91zzp8/X5LUsGFD2Ww2LVmyREeOHNGJEydUs2ZNjRo1SgkJCZo9e7a+//57ffXVV3rllVc0e/ZsSdLQoUO1Z88ejR49Wrt379acOXOUmppq9q8IQBVBQgJUYgEBAVq7dq0aNGignj17qmnTpho8eLBOnTrlrpg89thj6t+/vwYOHCiHw6GaNWvq73//+5/OO3PmTPXu3Vv//Oc/1aRJEz300EM6efKkJOnyyy/XM888o8cff1zh4eEaPny4JOnZZ5/VU089peTkZDVt2lSdO3fW0qVLFRUVJUlq0KCBPvroIy1cuFDNmzdXSkqKJk6caOJvB0BVYnOdb+UbAABABaFCAgAALEdCAgAALEdCAgAALEdCAgAALEdCAgAALEdCAgAALEdCAgAALEdCAgAALEdCAgAALEdCAgAALEdCAgAALPf/AMax+FyBVneSAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"code","source":["!pip install huggingface_hub\n","from huggingface_hub import login\n","\n","login()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":540,"referenced_widgets":["58ffb29df1224800af661a783895dbf2","77e6c975fd854616ad772a7955db95b6","df17ab96d13b4a0ab46dfe66d3c3f555","060f142b116b4994a0fbb0fd9850e92a","dd79e90250ee4fc283fadb9c7ccfcc6a","91aa1b6da74b44dfb7db753984ff5ec0","d5f7344defb64d3ba7d04ded877cfc8d","e6c3666b548646889234a89c725f4261","384a504e63a7493da1e83e4b96b092be","5796c2e0a4404e5da87e21b2fc86e129","199ea1cfc9cc4e1fa4fbc7f5ccf91a90","dc724d10d2f54334bce1211051b69ae6","b8e9d4a0c14646d29d63cbe82d52bcfe","9b59916ec1db4fec996d323dbf5224c3","79199804215344cb8336a063b5d167b4","0bc4d3a9332b45b393b92003a4db11e2","05d60773b6904763af7d5076b50b3d5e","f89185c6c9ea4cb19af928f9338f4678","092bc1b8733c473dab5993f1cbd14745","28e388aee94d4a2983368695ef23a6fe"]},"id":"PVEkFZPH18QU","executionInfo":{"status":"ok","timestamp":1772082763012,"user_tz":-330,"elapsed":12914,"user":{"displayName":"jaimy","userId":"07332807517928872066"}},"outputId":"513516c7-83a3-45fa-f5b7-a15eab237889"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.12/dist-packages (1.4.1)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (3.24.2)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (2025.3.0)\n","Requirement already satisfied: hf-xet<2.0.0,>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (1.2.0)\n","Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (0.28.1)\n","Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (26.0)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (6.0.3)\n","Requirement already satisfied: shellingham in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (1.5.4)\n","Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (4.67.3)\n","Requirement already satisfied: typer-slim in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (0.24.0)\n","Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (4.15.0)\n","Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (4.12.1)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (2026.1.4)\n","Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (1.0.9)\n","Requirement already satisfied: idna in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (3.11)\n","Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface_hub) (0.16.0)\n","Requirement already satisfied: typer>=0.24.0 in /usr/local/lib/python3.12/dist-packages (from typer-slim->huggingface_hub) (0.24.0)\n","Requirement already satisfied: click>=8.2.1 in /usr/local/lib/python3.12/dist-packages (from typer>=0.24.0->typer-slim->huggingface_hub) (8.3.1)\n","Requirement already satisfied: rich>=12.3.0 in /usr/local/lib/python3.12/dist-packages (from typer>=0.24.0->typer-slim->huggingface_hub) (13.9.4)\n","Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer>=0.24.0->typer-slim->huggingface_hub) (0.0.4)\n","Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer>=0.24.0->typer-slim->huggingface_hub) (4.0.0)\n","Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer>=0.24.0->typer-slim->huggingface_hub) (2.19.2)\n","Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer>=0.24.0->typer-slim->huggingface_hub) (0.1.2)\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"58ffb29df1224800af661a783895dbf2"}},"metadata":{}}]},{"cell_type":"code","source":["model.push_to_hub(\"Jaimy12/albert-comments\")\n","tokenizer.push_to_hub(\"Jaimy12/albert-comments\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":158},"id":"YolPfr3-19Cn","executionInfo":{"status":"error","timestamp":1772082777117,"user_tz":-330,"elapsed":46,"user":{"displayName":"jaimy","userId":"07332807517928872066"}},"outputId":"d9975cc0-6fcd-4ee6-fdab-ceac2f12aa02"},"execution_count":2,"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'model' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m/tmp/ipython-input-543/1567909340.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Jaimy12/albert-comments\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Jaimy12/albert-comments\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"]}]}]}
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