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27c8ef8 | 1 | {"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceType":"datasetVersion","sourceId":15605737,"datasetId":9986581,"databundleVersionId":16539179}],"dockerImageVersionId":31329,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":5,"nbformat":4,"cells":[{"id":"a90a9242-0e9c-49c9-8d41-480c6e52038c","cell_type":"code","source":"!pip install datasets","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting datasets\n"," Downloading datasets-4.8.4-py3-none-any.whl.metadata (19 kB)\n","Collecting filelock (from datasets)\n"," Downloading filelock-3.25.2-py3-none-any.whl.metadata (2.0 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yarl-1.23.0\n"]}],"execution_count":2},{"id":"ba31f87c-7bc4-4016-8f69-3af02aa35091","cell_type":"code","source":"from datasets import load_dataset\n\ndataset = load_dataset(\"moujar/MentalHealth-Darija\")\ndf = dataset[\"train\"].to_pandas()\nprint(df.head())\n","metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["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":{"application/vnd.jupyter.widget-view+json":{"model_id":"f25668dddbc34cecb27dba0e7c7afd35","version_major":2,"version_minor":0},"text/plain":["README.md: 0.00B [00:00, ?B/s]"]},"metadata":{}},{"name":"stderr","output_type":"stream","text":["E:\\conda\\envs\\mental_env\\Lib\\site-packages\\huggingface_hub\\file_download.py:138: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\OS_10\\.cache\\huggingface\\hub\\datasets--moujar--MentalHealth-Darija. 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 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"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4366430192fc418193d388bf5423ab2c","version_major":2,"version_minor":0},"text/plain":["data.csv: 0%| | 0.00/54.9M [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"34fee8eb40dc44228e209ab1c9797eb2","version_major":2,"version_minor":0},"text/plain":["Generating train split: 0%| | 0/51093 [00:00<?, ? examples/s]"]},"metadata":{}},{"name":"stdout","output_type":"stream","text":[" text_en \\\n","0 oh my gosh \n","1 trouble sleeping, confused mind, restless hear... \n","2 All wrong, back off dear, forward doubt. Stay ... \n","3 I've shifted my focus to something else but I'... \n","4 I'm restless and restless, it's been a month n... \n","\n"," text label \n","0 يا ربي 0 \n","1 مشكل فالنعاس، عقل محتار، قلب قلقان. كولشي خارج... 0 \n","2 ماشي مشكل، رجع للور عزيزي، تقدم الشك. بقى فبلا... 0 \n","3 بدلت التركيز ديالي لشي حاجة خرى ولكن مازال قلقان 0 \n","4 انا مقلق وقلقان هادي شهر دابا ا ولدي اش كتقصد 0 \n"]}],"execution_count":3},{"id":"0e886bcf-0bbe-4b89-9c2a-96a585a1bc48","cell_type":"code","source":"import pandas as pd\n\ndf = dataset[\"train\"].to_pandas()\nprint(df.head())","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":[" text_en \\\n","0 oh my gosh \n","1 trouble sleeping, confused mind, restless hear... \n","2 All wrong, back off dear, forward doubt. Stay ... \n","3 I've shifted my focus to something else but I'... \n","4 I'm restless and restless, it's been a month n... \n","\n"," text label \n","0 يا ربي 0 \n","1 مشكل فالنعاس، عقل محتار، قلب قلقان. كولشي خارج... 0 \n","2 ماشي مشكل، رجع للور عزيزي، تقدم الشك. بقى فبلا... 0 \n","3 بدلت التركيز ديالي لشي حاجة خرى ولكن مازال قلقان 0 \n","4 انا مقلق وقلقان هادي شهر دابا ا ولدي اش كتقصد 0 \n"]}],"execution_count":4},{"id":"b85fe223-0d07-45f8-9905-96d93af8e75f","cell_type":"code","source":"df.to_csv(\"MentalHealth_Darija.csv\", index=False)","metadata":{},"outputs":[],"execution_count":5},{"id":"e6ba352f-5810-4d28-8f52-36c51942e670","cell_type":"code","source":"pip install googletrans==4.0.0-rc1","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting googletrans==4.0.0-rc1\n"," Downloading googletrans-4.0.0rc1.tar.gz (20 kB)\n"," Installing build dependencies: started\n"," Installing build dependencies: finished with status 'done'\n"," Getting requirements to build wheel: started\n"," Getting requirements to build wheel: finished with status 'done'\n"," Preparing metadata (pyproject.toml): started\n"," Preparing metadata (pyproject.toml): finished with status 'done'\n","Collecting httpx==0.13.3 (from googletrans==4.0.0-rc1)\n"," Downloading httpx-0.13.3-py3-none-any.whl.metadata (25 kB)\n","Requirement already satisfied: certifi in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from httpx==0.13.3->googletrans==4.0.0-rc1) (2026.1.4)\n","Collecting hstspreload (from httpx==0.13.3->googletrans==4.0.0-rc1)\n"," Downloading hstspreload-2025.1.1-py3-none-any.whl.metadata (2.1 kB)\n","Collecting sniffio (from httpx==0.13.3->googletrans==4.0.0-rc1)\n"," Downloading sniffio-1.3.1-py3-none-any.whl.metadata (3.9 kB)\n","Collecting chardet==3.* (from httpx==0.13.3->googletrans==4.0.0-rc1)\n"," Downloading chardet-3.0.4-py2.py3-none-any.whl.metadata (3.2 kB)\n","Collecting idna==2.* (from httpx==0.13.3->googletrans==4.0.0-rc1)\n"," Downloading idna-2.10-py2.py3-none-any.whl.metadata (9.1 kB)\n","Collecting rfc3986<2,>=1.3 (from httpx==0.13.3->googletrans==4.0.0-rc1)\n"," Downloading rfc3986-1.5.0-py2.py3-none-any.whl.metadata (6.5 kB)\n","Collecting httpcore==0.9.* (from httpx==0.13.3->googletrans==4.0.0-rc1)\n"," Downloading httpcore-0.9.1-py3-none-any.whl.metadata (4.6 kB)\n","Collecting h11<0.10,>=0.8 (from httpcore==0.9.*->httpx==0.13.3->googletrans==4.0.0-rc1)\n"," Downloading h11-0.9.0-py2.py3-none-any.whl.metadata (8.1 kB)\n","Collecting h2==3.* (from httpcore==0.9.*->httpx==0.13.3->googletrans==4.0.0-rc1)\n"," Downloading h2-3.2.0-py2.py3-none-any.whl.metadata (32 kB)\n","Collecting hyperframe<6,>=5.2.0 (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans==4.0.0-rc1)\n"," Downloading hyperframe-5.2.0-py2.py3-none-any.whl.metadata (7.2 kB)\n","Collecting hpack<4,>=3.0 (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans==4.0.0-rc1)\n"," Downloading hpack-3.0.0-py2.py3-none-any.whl.metadata (7.0 kB)\n","Downloading httpx-0.13.3-py3-none-any.whl (55 kB)\n","Downloading chardet-3.0.4-py2.py3-none-any.whl (133 kB)\n","Downloading httpcore-0.9.1-py3-none-any.whl (42 kB)\n","Downloading h11-0.9.0-py2.py3-none-any.whl (53 kB)\n","Downloading h2-3.2.0-py2.py3-none-any.whl (65 kB)\n","Downloading hpack-3.0.0-py2.py3-none-any.whl (38 kB)\n","Downloading hyperframe-5.2.0-py2.py3-none-any.whl (12 kB)\n","Downloading idna-2.10-py2.py3-none-any.whl (58 kB)\n","Downloading rfc3986-1.5.0-py2.py3-none-any.whl (31 kB)\n","Downloading sniffio-1.3.1-py3-none-any.whl (10 kB)\n","Downloading hstspreload-2025.1.1-py3-none-any.whl (1.3 MB)\n"," ---------------------------------------- 0.0/1.3 MB ? eta -:--:--\n"," ---------------------------------------- 0.0/1.3 MB ? eta -:--:--\n"," ---------------------------------------- 0.0/1.3 MB ? eta -:--:--\n"," -------- ------------------------------- 0.3/1.3 MB ? eta -:--:--\n"," -------- ------------------------------- 0.3/1.3 MB ? eta -:--:--\n"," 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Created wheel for googletrans: filename=googletrans-4.0.0rc1-py3-none-any.whl size=17517 sha256=b287284538d73b01e2147a580b46fe263456b1379f37d829d100f7500d95dd1d\n"," Stored in directory: c:\\users\\os_10\\appdata\\local\\pip\\cache\\wheels\\39\\17\\6f\\66a045ea3d168826074691b4b787b8f324d3f646d755443fda\n","Successfully built googletrans\n","Installing collected packages: rfc3986, hyperframe, hpack, h11, chardet, sniffio, idna, hstspreload, h2, httpcore, httpx, googletrans\n","\n"," ---------------------------------------- 0/12 [rfc3986]\n"," ---------------------------------------- 0/12 [rfc3986]\n"," ---------------------------------------- 0/12 [rfc3986]\n"," --- ------------------------------------ 1/12 [hyperframe]\n"," ------ --------------------------------- 2/12 [hpack]\n"," ------ --------------------------------- 2/12 [hpack]\n"," Attempting uninstall: h11\n"," ------ --------------------------------- 2/12 [hpack]\n"," Found existing installation: h11 0.16.0\n"," ------ 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[chardet]\n"," ------------- -------------------------- 4/12 [chardet]\n"," ------------- -------------------------- 4/12 [chardet]\n"," ---------------- ----------------------- 5/12 [sniffio]\n"," Attempting uninstall: idna\n"," ---------------- ----------------------- 5/12 [sniffio]\n"," Found existing installation: idna 3.11\n"," ---------------- ----------------------- 5/12 [sniffio]\n"," Uninstalling idna-3.11:\n"," ---------------- ----------------------- 5/12 [sniffio]\n"," Successfully uninstalled idna-3.11\n"," ---------------- ----------------------- 5/12 [sniffio]\n"," -------------------- ------------------- 6/12 [idna]\n"," -------------------- ------------------- 6/12 [idna]\n"," -------------------- ------------------- 6/12 [idna]\n"," -------------------------- ------------- 8/12 [h2]\n"," -------------------------- ------------- 8/12 [h2]\n"," -------------------------- ------------- 8/12 [h2]\n"," Attempting uninstall: httpcore\n"," -------------------------- ------------- 8/12 [h2]\n"," Found existing installation: httpcore 1.0.9\n"," -------------------------- ------------- 8/12 [h2]\n"," ------------------------------ --------- 9/12 [httpcore]\n"," Uninstalling httpcore-1.0.9:\n"," ------------------------------ --------- 9/12 [httpcore]\n"," Successfully uninstalled httpcore-1.0.9\n"," ------------------------------ --------- 9/12 [httpcore]\n"," ------------------------------ --------- 9/12 [httpcore]\n"," ------------------------------ --------- 9/12 [httpcore]\n"," ------------------------------ --------- 9/12 [httpcore]\n"," ------------------------------ --------- 9/12 [httpcore]\n"," Attempting uninstall: httpx\n"," ------------------------------ --------- 9/12 [httpcore]\n"," Found existing installation: httpx 0.28.1\n"," ------------------------------ --------- 9/12 [httpcore]\n"," --------------------------------- ------ 10/12 [httpx]\n"," Uninstalling httpx-0.28.1:\n"," --------------------------------- ------ 10/12 [httpx]\n"," --------------------------------- ------ 10/12 [httpx]\n"," Successfully uninstalled httpx-0.28.1\n"," --------------------------------- ------ 10/12 [httpx]\n"," --------------------------------- ------ 10/12 [httpx]\n"," --------------------------------- ------ 10/12 [httpx]\n"," --------------------------------- ------ 10/12 [httpx]\n"," ------------------------------------ --- 11/12 [googletrans]\n"," ---------------------------------------- 12/12 [googletrans]\n","\n","Successfully installed chardet-3.0.4 googletrans-4.0.0rc1 h11-0.9.0 h2-3.2.0 hpack-3.0.0 hstspreload-2025.1.1 httpcore-0.9.1 httpx-0.13.3 hyperframe-5.2.0 idna-2.10 rfc3986-1.5.0 sniffio-1.3.1\n","Note: you may need to restart the kernel to use updated packages.\n"]},{"name":"stderr","output_type":"stream","text":["ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","huggingface-hub 1.9.0 requires httpx<1,>=0.23.0, but you have httpx 0.13.3 which is incompatible.\n","jupyterlab 4.5.6 requires httpx<1,>=0.25.0, but you have httpx 0.13.3 which is incompatible.\n"]}],"execution_count":7},{"id":"a3752631-854c-41be-9015-5cae4e5948c6","cell_type":"code","source":"pip install deep-translator","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting deep-translator\n"," Downloading deep_translator-1.11.4-py3-none-any.whl.metadata (30 kB)\n","Requirement already satisfied: beautifulsoup4<5.0.0,>=4.9.1 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from deep-translator) (4.14.3)\n","Requirement already satisfied: requests<3.0.0,>=2.23.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from deep-translator) (2.33.1)\n","Requirement already satisfied: soupsieve>=1.6.1 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from beautifulsoup4<5.0.0,>=4.9.1->deep-translator) (2.5)\n","Requirement already satisfied: typing-extensions>=4.0.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from beautifulsoup4<5.0.0,>=4.9.1->deep-translator) (4.15.0)\n","Requirement already satisfied: charset_normalizer<4,>=2 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from requests<3.0.0,>=2.23.0->deep-translator) (3.4.4)\n","Requirement already satisfied: idna<4,>=2.5 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from requests<3.0.0,>=2.23.0->deep-translator) (2.10)\n","Requirement already satisfied: urllib3<3,>=1.26 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from requests<3.0.0,>=2.23.0->deep-translator) (2.6.3)\n","Requirement already satisfied: certifi>=2023.5.7 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from requests<3.0.0,>=2.23.0->deep-translator) (2026.1.4)\n","Downloading deep_translator-1.11.4-py3-none-any.whl (42 kB)\n","Installing collected packages: deep-translator\n","Successfully installed deep-translator-1.11.4\n","Note: you may need to restart the kernel to use updated packages.\n"]}],"execution_count":9},{"id":"e1dc5eab-61e9-4db1-9549-060835e28291","cell_type":"code","source":"import pandas as pd\n\ndf = pd.read_csv(\"/kaggle/input/datasets/tasneem33355/data33/MentalHealth_Darija.csv\")\n\ndf = df[['text', 'text_en', 'label']].dropna()\n\ndf['combined_text'] = df['text_en'] + \" [SEP] \" + df['text']\n\nprint(df[['combined_text', 'label']].head())\nprint(df.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-08T20:44:44.228508Z","iopub.execute_input":"2026-04-08T20:44:44.229032Z","iopub.status.idle":"2026-04-08T20:44:47.611393Z","shell.execute_reply.started":"2026-04-08T20:44:44.229001Z","shell.execute_reply":"2026-04-08T20:44:47.610615Z"}},"outputs":[{"name":"stdout","text":" combined_text label\n0 oh my gosh [SEP] يا ربي 0\n1 trouble sleeping, confused mind, restless hear... 0\n2 All wrong, back off dear, forward doubt. Stay ... 0\n3 I've shifted my focus to something else but I'... 0\n4 I'm restless and restless, it's been a month n... 0\n(51093, 4)\n","output_type":"stream"}],"execution_count":11},{"id":"30481726-ad0e-4853-95e6-c3548d78551c","cell_type":"code","source":"print(df['label'].unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-08T20:44:59.121361Z","iopub.execute_input":"2026-04-08T20:44:59.121671Z","iopub.status.idle":"2026-04-08T20:44:59.129977Z","shell.execute_reply.started":"2026-04-08T20:44:59.121645Z","shell.execute_reply":"2026-04-08T20:44:59.129192Z"}},"outputs":[{"name":"stdout","text":"[0 1 2 3 4 5 6]\n","output_type":"stream"}],"execution_count":12},{"id":"0b599ee4-4bcc-4b29-88e7-50cb33a81e28","cell_type":"code","source":"print(df[['label', 'text_en']].groupby('label').head(3))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-08T20:45:08.185821Z","iopub.execute_input":"2026-04-08T20:45:08.186117Z","iopub.status.idle":"2026-04-08T20:45:08.197926Z","shell.execute_reply.started":"2026-04-08T20:45:08.186090Z","shell.execute_reply":"2026-04-08T20:45:08.197149Z"}},"outputs":[{"name":"stdout","text":" label text_en\n0 0 oh my gosh\n1 0 trouble sleeping, confused mind, restless hear...\n2 0 All wrong, back off dear, forward doubt. Stay ...\n712 1 Gr gr dreaming of ex crush to be my game, God\n713 1 wkwkwk what a joke\n714 1 Leaves are also standby in front of the PC ......\n6895 2 I recently went through a breakup and she said...\n6896 2 I do not know how to navigate these feelings, ...\n6897 2 So I have been with my bf for 5 months , and h...\n6898 3 I am so exhausted of this. Just when I think I...\n6900 3 I am 20 year old with some good friends but I ...\n6903 3 it is looming around the corner again. It alwa...\n27244 4 He said he had not felt that way before, sugge...\n27246 4 My mom then hit me with the newspaper and it s...\n27247 4 until i met my new boyfriend, he is amazing, h...\n44966 5 What birth control are you on? I tried an IUD ...\n44967 5 Hypersexuality I was diagnosed with bipolar un...\n44968 5 Meditation I have been using the free meditati...\n49253 6 Is there anyone interested in joining a group ...\n49254 6 Anyone else have nothing in common with other ...\n49255 6 Being a ghost would be my ideal form of existe...\n","output_type":"stream"}],"execution_count":14},{"id":"f5310f81-c670-4f2e-aaca-e2f38ad7aba0","cell_type":"code","source":"df_filtered = df[df['label'].isin([0, 2, 3])].copy()\n\nlabel_map = {\n 3: \"depression\",\n 0: \"anxiety\",\n 2: \"stress\"\n}\n\ndf_filtered['target'] = df_filtered['label'].map(label_map)\n\nprint(df_filtered['target'].value_counts())\nprint(df_filtered[['combined_text', 'target']].head())\nprint(df_filtered.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-08T20:45:19.950831Z","iopub.execute_input":"2026-04-08T20:45:19.951542Z","iopub.status.idle":"2026-04-08T20:45:19.982702Z","shell.execute_reply.started":"2026-04-08T20:45:19.951512Z","shell.execute_reply":"2026-04-08T20:45:19.982099Z"}},"outputs":[{"name":"stdout","text":"target\nstress 15094\ndepression 10644\nanxiety 3623\nName: count, dtype: int64\n combined_text target\n0 oh my gosh [SEP] يا ربي anxiety\n1 trouble sleeping, confused mind, restless hear... anxiety\n2 All wrong, back off dear, forward doubt. Stay ... anxiety\n3 I've shifted my focus to something else but I'... anxiety\n4 I'm restless and restless, it's been a month n... anxiety\n(29361, 5)\n","output_type":"stream"}],"execution_count":15},{"id":"3a2dd055-6e8a-4fab-b9bf-850b542317e0","cell_type":"code","source":"df_filtered['target'].value_counts()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-08T20:45:25.004224Z","iopub.execute_input":"2026-04-08T20:45:25.004545Z","iopub.status.idle":"2026-04-08T20:45:25.013518Z","shell.execute_reply.started":"2026-04-08T20:45:25.004521Z","shell.execute_reply":"2026-04-08T20:45:25.012726Z"}},"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"target\nstress 15094\ndepression 10644\nanxiety 3623\nName: count, dtype: int64"},"metadata":{}}],"execution_count":16},{"id":"7ad78f64-1893-4efa-bdd1-8737708db7b8","cell_type":"code","source":"pip install scikit-learn","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting scikit-learn\n"," Downloading scikit_learn-1.8.0-cp311-cp311-win_amd64.whl.metadata (11 kB)\n","Requirement already satisfied: numpy>=1.24.1 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from scikit-learn) (2.4.4)\n","Collecting scipy>=1.10.0 (from scikit-learn)\n"," Downloading scipy-1.17.1-cp311-cp311-win_amd64.whl.metadata (60 kB)\n","Collecting joblib>=1.3.0 (from scikit-learn)\n"," Using cached joblib-1.5.3-py3-none-any.whl.metadata (5.5 kB)\n","Collecting threadpoolctl>=3.2.0 (from scikit-learn)\n"," Using cached threadpoolctl-3.6.0-py3-none-any.whl.metadata (13 kB)\n","Downloading scikit_learn-1.8.0-cp311-cp311-win_amd64.whl (8.1 MB)\n"," ---------------------------------------- 0.0/8.1 MB ? eta -:--:--\n"," ---------------------------------------- 0.0/8.1 MB ? eta -:--:--\n"," ---------------------------------------- 0.0/8.1 MB ? eta -:--:--\n"," - 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X_val.shape)\nprint(y_train.value_counts())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-08T20:45:45.193234Z","iopub.execute_input":"2026-04-08T20:45:45.193583Z","iopub.status.idle":"2026-04-08T20:45:47.901768Z","shell.execute_reply.started":"2026-04-08T20:45:45.193555Z","shell.execute_reply":"2026-04-08T20:45:47.901114Z"}},"outputs":[{"name":"stdout","text":"['anxiety' 'depression' 'stress']\n(23488,) (5873,)\nlabel_encoded\n2 12075\n1 8515\n0 2898\nName: count, dtype: int64\n","output_type":"stream"}],"execution_count":17},{"id":"218c19af-b7da-4589-b780-3d87c034eaa0","cell_type":"code","source":"pip install transformers datasets sentencepiece","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting transformers\n"," Downloading transformers-5.5.0-py3-none-any.whl.metadata (32 kB)\n","Requirement already satisfied: datasets in E:\\conda\\envs\\mental_env\\Lib\\site-packages (4.8.4)\n","Collecting sentencepiece\n"," Downloading 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httpx-0.28.1-py3-none-any.whl (73 kB)\n","Using cached httpcore-1.0.9-py3-none-any.whl (78 kB)\n","Downloading tokenizers-0.22.2-cp39-abi3-win_amd64.whl (2.7 MB)\n"," ---------------------------------------- 0.0/2.7 MB ? eta -:--:--\n"," ---------------------------------------- 0.0/2.7 MB ? eta -:--:--\n"," --- ------------------------------------ 0.3/2.7 MB ? eta -:--:--\n"," ------- -------------------------------- 0.5/2.7 MB 799.2 kB/s eta 0:00:03\n"," ------- -------------------------------- 0.5/2.7 MB 799.2 kB/s eta 0:00:03\n"," ----------- ---------------------------- 0.8/2.7 MB 838.9 kB/s eta 0:00:03\n"," ----------- ---------------------------- 0.8/2.7 MB 838.9 kB/s eta 0:00:03\n"," --------------- ------------------------ 1.0/2.7 MB 762.8 kB/s eta 0:00:03\n"," ------------------- -------------------- 1.3/2.7 MB 789.6 kB/s eta 0:00:02\n"," ------------------- -------------------- 1.3/2.7 MB 789.6 kB/s eta 0:00:02\n"," ---------------------- ----------------- 1.6/2.7 MB 798.7 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----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ----------------------------------- ---- 7/8 [transformers]\n"," ---------------------------------------- 8/8 [transformers]\n","\n","Successfully installed h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 regex-2026.4.4 safetensors-0.7.0 sentencepiece-0.2.1 tokenizers-0.22.2 transformers-5.5.0\n","Note: you may need to restart the kernel to use updated packages.\n"]},{"name":"stderr","output_type":"stream","text":["ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","googletrans 4.0.0rc1 requires httpx==0.13.3, but you have httpx 0.28.1 which is incompatible.\n"]}],"execution_count":10},{"id":"6511a590-c17d-4ddc-a16b-62c937e2a25c","cell_type":"code","source":"import sys\n!{sys.executable} -m pip install transformers datasets sentencepiece torch","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: transformers in E:\\conda\\envs\\mental_env\\Lib\\site-packages (5.5.0)\n","Requirement already satisfied: datasets in E:\\conda\\envs\\mental_env\\Lib\\site-packages (4.8.4)\n","Requirement already satisfied: sentencepiece in E:\\conda\\envs\\mental_env\\Lib\\site-packages (0.2.1)\n","Collecting torch\n"," Downloading torch-2.11.0-cp311-cp311-win_amd64.whl.metadata (29 kB)\n","Requirement already satisfied: huggingface-hub<2.0,>=1.5.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from transformers) (1.9.0)\n","Requirement 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-------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," 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-------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," -------------------------------- ------- 4/5 [torch]\n"," ---------------------------------------- 5/5 [torch]\n","\n","Successfully installed mpmath-1.3.0 networkx-3.6.1 setuptools-81.0.0 sympy-1.14.0 torch-2.11.0\n"]}],"execution_count":11},{"id":"4a176d6c-e747-4f1a-90b3-8ef14ebe43c6","cell_type":"code","source":"!pip install -U accelerate","metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["ERROR: Could not find a version that satisfies the requirement accelerate (from versions: none)\n","ERROR: No matching distribution found for accelerate\n"]}],"execution_count":14},{"id":"2f0de29a-c4ab-4b04-a00d-35999095c219","cell_type":"code","source":"!pip install -U accelerate","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting accelerate\n"," Downloading accelerate-1.13.0-py3-none-any.whl.metadata (19 kB)\n","Requirement already satisfied: numpy>=1.17 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from accelerate) (2.4.4)\n","Requirement already satisfied: packaging>=20.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from accelerate) (26.0)\n","Requirement already satisfied: psutil in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from accelerate) (7.0.0)\n","Requirement already satisfied: pyyaml in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from accelerate) (6.0.3)\n","Requirement already satisfied: torch>=2.0.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from accelerate) (2.11.0)\n","Requirement already satisfied: huggingface_hub>=0.21.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from accelerate) (1.9.0)\n","Requirement already satisfied: safetensors>=0.4.3 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from accelerate) (0.7.0)\n","Requirement already satisfied: filelock>=3.10.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from huggingface_hub>=0.21.0->accelerate) (3.25.2)\n","Requirement already satisfied: fsspec>=2023.5.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from huggingface_hub>=0.21.0->accelerate) (2026.2.0)\n","Requirement already satisfied: hf-xet<2.0.0,>=1.4.3 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from huggingface_hub>=0.21.0->accelerate) (1.4.3)\n","Requirement already satisfied: httpx<1,>=0.23.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from huggingface_hub>=0.21.0->accelerate) (0.28.1)\n","Requirement already satisfied: tqdm>=4.42.1 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from huggingface_hub>=0.21.0->accelerate) (4.67.3)\n","Requirement already satisfied: typer in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from huggingface_hub>=0.21.0->accelerate) 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already satisfied: click>=8.2.1 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from typer->transformers[torch]) (8.3.2)\n","Requirement already satisfied: shellingham>=1.3.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from typer->transformers[torch]) (1.5.4)\n","Requirement already satisfied: rich>=12.3.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from typer->transformers[torch]) (14.3.3)\n","Requirement already satisfied: annotated-doc>=0.0.2 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from typer->transformers[torch]) (0.0.4)\n","Requirement already satisfied: markdown-it-py>=2.2.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from rich>=12.3.0->typer->transformers[torch]) (4.0.0)\n","Requirement already satisfied: pygments<3.0.0,>=2.13.0 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from rich>=12.3.0->typer->transformers[torch]) (2.20.0)\n","Requirement already satisfied: mdurl~=0.1 in E:\\conda\\envs\\mental_env\\Lib\\site-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->transformers[torch]) (0.1.2)\n"]}],"execution_count":17},{"id":"2e80fa44-48ce-4a50-9256-51cfba923431","cell_type":"code","source":"\nimport os, pickle, warnings, numpy as np, pandas as pd, torch\nfrom datasets import Dataset\nfrom transformers import (\n AutoTokenizer, AutoModelForSequenceClassification,\n Trainer, TrainingArguments, EarlyStoppingCallback,\n DataCollatorWithPadding,\n)\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.utils.class_weight import compute_class_weight\nfrom sklearn.metrics import accuracy_score, f1_score, classification_report\nwarnings.filterwarnings(\"ignore\")\n\n# ── CONFIG ──────────────────────────────────────────────────────────\nCSV_PATH = \"/kaggle/input/datasets/tasneem33355/data33/MentalHealth_Darija.csv\"\nCKPT_PATH = \"./mental_xlmr_best\" \nOUTPUT_DIR = \"./mental_xlmr_v2\"\nMAX_LEN = 256\nBATCH_SIZE = 16\nEPOCHS = 6 \nLR = 5e-6 \nSEED = 42\nLABEL_MAP = {0: \"anxiety\", 2: \"stress\", 3: \"depression\"}\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(f\"✅ Device: {device}\")\n\n# ── PREPROCESS ────────────────────────────────────────\ndf = pd.read_csv(CSV_PATH)\ndf = df[['text', 'text_en', 'label']].dropna()\ndf['combined_text'] = df['text_en'].str.strip() + \" [SEP] \" + df['text'].str.strip()\ndf_filtered = df[df['label'].isin(LABEL_MAP.keys())].copy()\ndf_filtered['target'] = df_filtered['label'].map(LABEL_MAP)\n\nle = LabelEncoder()\ndf_filtered['label_encoded'] = le.fit_transform(df_filtered['target'])\n\nX_train, X_val, y_train, y_val = train_test_split(\n df_filtered['combined_text'].values,\n df_filtered['label_encoded'].values,\n test_size=0.2, random_state=SEED,\n stratify=df_filtered['label_encoded'],\n)\nprint(f\"Train: {len(X_train)} | Val: {len(X_val)}\")\n\n# ── DATASETS ────────────────────────────────────────────────────────\ntokenizer = AutoTokenizer.from_pretrained(CKPT_PATH)\n\ndef tokenize(batch):\n return tokenizer(batch[\"text\"], padding=False,\n truncation=True, max_length=MAX_LEN)\n\ntrain_hf = Dataset.from_dict({\"text\": list(X_train), \"labels\": list(y_train)})\nval_hf = Dataset.from_dict({\"text\": list(X_val), \"labels\": list(y_val)})\ntrain_hf = train_hf.map(tokenize, batched=True, remove_columns=[\"text\"])\nval_hf = val_hf.map(tokenize, batched=True, remove_columns=[\"text\"])\ntrain_hf.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\nval_hf.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n\n# ── CLASS WEIGHTS ──────────────────────────────────────────\nunique_labels = np.unique(y_train)\nnum_labels = len(unique_labels)\nclass_weights = compute_class_weight(\"balanced\", classes=unique_labels, y=y_train)\n\n# stress index = 2\nclass_weights[1] *= 1.4 # boost depression\nclass_weights[2] *= 1.2 # boost stress قليلاً\nclass_weights_tensor = torch.tensor(class_weights, dtype=torch.float).to(device)\n\nprint(\"⚖️ Adjusted class weights:\")\nfor i, c in enumerate(le.classes_):\n print(f\" {c}: {class_weights[i]:.3f}\")\n\n# ── MODEL ────────────────────────────────────────\nmodel = AutoModelForSequenceClassification.from_pretrained(CKPT_PATH)\nmodel = model.to(device)\n\n\nfor name, param in model.named_parameters():\n if any(f\"layer.{i}\" in name for i in range(9)):\n param.requires_grad = False\n else:\n param.requires_grad = True\n\ntrainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\ntotal = sum(p.numel() for p in model.parameters())\nprint(f\"\\n🔧 Trainable params: {trainable:,} / {total:,} ({100*trainable/total:.1f}%)\")\n\n# ── WEIGHTED TRAINER ────────────────────────────────────────────────\nclass WeightedTrainer(Trainer):\n def __init__(self, class_weights, **kwargs):\n super().__init__(**kwargs)\n self.class_weights = class_weights\n\n def compute_loss(self, model, inputs, return_outputs=False, **kwargs):\n labels = inputs.pop(\"labels\")\n outputs = model(**inputs)\n loss = torch.nn.CrossEntropyLoss(weight=self.class_weights)(\n outputs.logits, labels\n )\n return (loss, outputs) if return_outputs else loss\n\n# ── METRICS ─────────────────────────────────────────────────────────\ndef compute_metrics(eval_pred):\n logits, labels = eval_pred\n preds = np.argmax(logits, axis=-1)\n return {\n \"accuracy\": round(accuracy_score(labels, preds), 4),\n \"f1_macro\": round(f1_score(labels, preds, average=\"macro\"), 4),\n }\n\n# ── TRAINING ARGS ───────────────────────────────────────────────────\ntotal_steps = (len(train_hf) // BATCH_SIZE) * EPOCHS\nwarmup_steps = int(total_steps * 0.05) \n\ntraining_args = TrainingArguments(\n output_dir=OUTPUT_DIR,\n num_train_epochs=EPOCHS,\n learning_rate=LR,\n per_device_train_batch_size=BATCH_SIZE,\n per_device_eval_batch_size=BATCH_SIZE * 2,\n weight_decay=0.01,\n warmup_steps=warmup_steps,\n lr_scheduler_type=\"cosine\",\n logging_steps=50,\n save_strategy=\"epoch\",\n eval_strategy=\"epoch\",\n save_total_limit=2,\n load_best_model_at_end=True,\n metric_for_best_model=\"f1_macro\",\n greater_is_better=True,\n fp16=torch.cuda.is_available(),\n gradient_checkpointing=True,\n gradient_checkpointing_kwargs={\"use_reentrant\": False},\n dataloader_num_workers=2,\n report_to=\"none\",\n seed=SEED,\n)\n\n# ── TRAINER ─────────────────────────────────────────────────────────\ntrainer = WeightedTrainer(\n class_weights=class_weights_tensor,\n model=model,\n args=training_args,\n train_dataset=train_hf,\n eval_dataset=val_hf,\n compute_metrics=compute_metrics,\n data_collator=DataCollatorWithPadding(tokenizer=tokenizer),\n callbacks=[EarlyStoppingCallback(early_stopping_patience=2)],\n)\n\n# ── TRAIN ────────────────────────────────────────────────────────────\nprint(\"\\n🚀 Continuing training from checkpoint...\")\ntrainer.train()\n\n# ── FINAL EVAL ───────────────────────────────────────────────────────\nresults = trainer.evaluate()\nprint(\"\\n📊 Final Evaluation:\")\nfor k, v in results.items():\n if isinstance(v, float): print(f\" {k}: {round(v,4)}\")\n\nraw_preds = trainer.predict(val_hf)\npreds = np.argmax(raw_preds.predictions, axis=-1)\nprint(\"\\n📋 Classification Report:\")\nprint(classification_report(y_val, preds, target_names=le.classes_))\n\n# ── SAVE ─────────────────────────────────────────────────────────────\nos.makedirs(OUTPUT_DIR, exist_ok=True)\ntrainer.save_model(OUTPUT_DIR)\ntokenizer.save_pretrained(OUTPUT_DIR)\nwith open(os.path.join(OUTPUT_DIR, \"label_encoder.pkl\"), \"wb\") as f:\n pickle.dump(le, f)\n\nprint(f\"\\n✅ v2 saved to {OUTPUT_DIR}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-08T23:52:26.489390Z","iopub.execute_input":"2026-04-08T23:52:26.489724Z","iopub.status.idle":"2026-04-09T00:49:37.045564Z","shell.execute_reply.started":"2026-04-08T23:52:26.489693Z","shell.execute_reply":"2026-04-09T00:49:37.044685Z"}},"outputs":[{"name":"stdout","text":"✅ Device: cuda\nTrain: 23488 | Val: 5873\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Map: 0%| | 0/23488 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2bab2b1fcdf04195b0a338f8c87031c2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Map: 0%| | 0/5873 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6dc2cb8b2973467fb73ae9281cd5657d"}},"metadata":{}},{"name":"stdout","text":"⚖️ Adjusted class weights:\n anxiety: 2.702\n depression: 1.287\n stress: 0.778\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Loading weights: 0%| | 0/201 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"35378fd08f054e96a1861fce3d9b3db3"}},"metadata":{}},{"name":"stdout","text":"\n🔧 Trainable params: 200,079,363 / 278,045,955 (72.0%)\n\n🚀 Continuing training from checkpoint...\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n <div>\n \n <progress value='2936' max='4404' style='width:300px; height:20px; vertical-align: middle;'></progress>\n [2936/4404 54:57 < 27:29, 0.89 it/s, Epoch 4/6]\n </div>\n <table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: left;\">\n <th>Epoch</th>\n <th>Training Loss</th>\n <th>Validation Loss</th>\n <th>Accuracy</th>\n <th>F1 Macro</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>1</td>\n <td>0.276373</td>\n <td>0.399544</td>\n <td>0.798900</td>\n <td>0.833400</td>\n </tr>\n <tr>\n <td>2</td>\n <td>0.254537</td>\n <td>0.422038</td>\n <td>0.801000</td>\n <td>0.835600</td>\n </tr>\n <tr>\n <td>3</td>\n <td>0.219580</td>\n <td>0.442508</td>\n <td>0.799600</td>\n <td>0.834000</td>\n </tr>\n <tr>\n <td>4</td>\n <td>0.287847</td>\n <td>0.409739</td>\n <td>0.798900</td>\n <td>0.834600</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":"53eaa689603243aea70794570f70bf33"}},"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":"52eba823e40e4d079dada94c64c944d7"}},"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":"388b95466da6475caed1c159a3c92d35"}},"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":"bc4199a7d710422fae7cad191f01a407"}},"metadata":{}},{"name":"stderr","text":"There were missing keys in the checkpoint model loaded: ['roberta.embeddings.LayerNorm.weight', 'roberta.embeddings.LayerNorm.bias', 'roberta.encoder.layer.0.attention.output.LayerNorm.weight', 'roberta.encoder.layer.0.attention.output.LayerNorm.bias', 'roberta.encoder.layer.0.output.LayerNorm.weight', 'roberta.encoder.layer.0.output.LayerNorm.bias', 'roberta.encoder.layer.1.attention.output.LayerNorm.weight', 'roberta.encoder.layer.1.attention.output.LayerNorm.bias', 'roberta.encoder.layer.1.output.LayerNorm.weight', 'roberta.encoder.layer.1.output.LayerNorm.bias', 'roberta.encoder.layer.2.attention.output.LayerNorm.weight', 'roberta.encoder.layer.2.attention.output.LayerNorm.bias', 'roberta.encoder.layer.2.output.LayerNorm.weight', 'roberta.encoder.layer.2.output.LayerNorm.bias', 'roberta.encoder.layer.3.attention.output.LayerNorm.weight', 'roberta.encoder.layer.3.attention.output.LayerNorm.bias', 'roberta.encoder.layer.3.output.LayerNorm.weight', 'roberta.encoder.layer.3.output.LayerNorm.bias', 'roberta.encoder.layer.4.attention.output.LayerNorm.weight', 'roberta.encoder.layer.4.attention.output.LayerNorm.bias', 'roberta.encoder.layer.4.output.LayerNorm.weight', 'roberta.encoder.layer.4.output.LayerNorm.bias', 'roberta.encoder.layer.5.attention.output.LayerNorm.weight', 'roberta.encoder.layer.5.attention.output.LayerNorm.bias', 'roberta.encoder.layer.5.output.LayerNorm.weight', 'roberta.encoder.layer.5.output.LayerNorm.bias', 'roberta.encoder.layer.6.attention.output.LayerNorm.weight', 'roberta.encoder.layer.6.attention.output.LayerNorm.bias', 'roberta.encoder.layer.6.output.LayerNorm.weight', 'roberta.encoder.layer.6.output.LayerNorm.bias', 'roberta.encoder.layer.7.attention.output.LayerNorm.weight', 'roberta.encoder.layer.7.attention.output.LayerNorm.bias', 'roberta.encoder.layer.7.output.LayerNorm.weight', 'roberta.encoder.layer.7.output.LayerNorm.bias', 'roberta.encoder.layer.8.attention.output.LayerNorm.weight', 'roberta.encoder.layer.8.attention.output.LayerNorm.bias', 'roberta.encoder.layer.8.output.LayerNorm.weight', 'roberta.encoder.layer.8.output.LayerNorm.bias', 'roberta.encoder.layer.9.attention.output.LayerNorm.weight', 'roberta.encoder.layer.9.attention.output.LayerNorm.bias', 'roberta.encoder.layer.9.output.LayerNorm.weight', 'roberta.encoder.layer.9.output.LayerNorm.bias', 'roberta.encoder.layer.10.attention.output.LayerNorm.weight', 'roberta.encoder.layer.10.attention.output.LayerNorm.bias', 'roberta.encoder.layer.10.output.LayerNorm.weight', 'roberta.encoder.layer.10.output.LayerNorm.bias', 'roberta.encoder.layer.11.attention.output.LayerNorm.weight', 'roberta.encoder.layer.11.attention.output.LayerNorm.bias', 'roberta.encoder.layer.11.output.LayerNorm.weight', 'roberta.encoder.layer.11.output.LayerNorm.bias'].\nThere were unexpected keys in the checkpoint model loaded: ['roberta.embeddings.LayerNorm.beta', 'roberta.embeddings.LayerNorm.gamma', 'roberta.encoder.layer.0.attention.output.LayerNorm.beta', 'roberta.encoder.layer.0.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.0.output.LayerNorm.beta', 'roberta.encoder.layer.0.output.LayerNorm.gamma', 'roberta.encoder.layer.1.attention.output.LayerNorm.beta', 'roberta.encoder.layer.1.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.1.output.LayerNorm.beta', 'roberta.encoder.layer.1.output.LayerNorm.gamma', 'roberta.encoder.layer.2.attention.output.LayerNorm.beta', 'roberta.encoder.layer.2.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.2.output.LayerNorm.beta', 'roberta.encoder.layer.2.output.LayerNorm.gamma', 'roberta.encoder.layer.3.attention.output.LayerNorm.beta', 'roberta.encoder.layer.3.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.3.output.LayerNorm.beta', 'roberta.encoder.layer.3.output.LayerNorm.gamma', 'roberta.encoder.layer.4.attention.output.LayerNorm.beta', 'roberta.encoder.layer.4.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.4.output.LayerNorm.beta', 'roberta.encoder.layer.4.output.LayerNorm.gamma', 'roberta.encoder.layer.5.attention.output.LayerNorm.beta', 'roberta.encoder.layer.5.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.5.output.LayerNorm.beta', 'roberta.encoder.layer.5.output.LayerNorm.gamma', 'roberta.encoder.layer.6.attention.output.LayerNorm.beta', 'roberta.encoder.layer.6.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.6.output.LayerNorm.beta', 'roberta.encoder.layer.6.output.LayerNorm.gamma', 'roberta.encoder.layer.7.attention.output.LayerNorm.beta', 'roberta.encoder.layer.7.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.7.output.LayerNorm.beta', 'roberta.encoder.layer.7.output.LayerNorm.gamma', 'roberta.encoder.layer.8.attention.output.LayerNorm.beta', 'roberta.encoder.layer.8.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.8.output.LayerNorm.beta', 'roberta.encoder.layer.8.output.LayerNorm.gamma', 'roberta.encoder.layer.9.attention.output.LayerNorm.beta', 'roberta.encoder.layer.9.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.9.output.LayerNorm.beta', 'roberta.encoder.layer.9.output.LayerNorm.gamma', 'roberta.encoder.layer.10.attention.output.LayerNorm.beta', 'roberta.encoder.layer.10.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.10.output.LayerNorm.beta', 'roberta.encoder.layer.10.output.LayerNorm.gamma', 'roberta.encoder.layer.11.attention.output.LayerNorm.beta', 'roberta.encoder.layer.11.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.11.output.LayerNorm.beta', 'roberta.encoder.layer.11.output.LayerNorm.gamma'].\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":""},"metadata":{}},{"name":"stdout","text":"\n📊 Final Evaluation:\n eval_loss: 0.4215\n eval_accuracy: 0.8001\n eval_f1_macro: 0.835\n eval_runtime: 54.2312\n eval_samples_per_second: 108.296\n eval_steps_per_second: 1.696\n epoch: 4.0\n\n📋 Classification Report:\n precision recall f1-score support\n\n anxiety 0.94 0.97 0.95 725\n depression 0.70 0.83 0.76 2129\n stress 0.85 0.74 0.79 3019\n\n accuracy 0.80 5873\n macro avg 0.83 0.84 0.83 5873\nweighted avg 0.81 0.80 0.80 5873\n\n","output_type":"stream"},{"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":"c9755ab2977c4426bf391516b835fbf1"}},"metadata":{}},{"name":"stdout","text":"\n✅ v2 saved to ./mental_xlmr_v2\n","output_type":"stream"}],"execution_count":35},{"id":"aa1b25b9-ed2f-464e-b5c5-434fad9cebd7","cell_type":"code","source":"import os, pickle, warnings, numpy as np, pandas as pd, torch, zipfile\nfrom datasets import Dataset\nfrom transformers import (\n AutoTokenizer, AutoModelForSequenceClassification,\n Trainer, TrainingArguments, EarlyStoppingCallback,\n DataCollatorWithPadding,\n)\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.utils.class_weight import compute_class_weight\nfrom sklearn.metrics import accuracy_score, f1_score, classification_report\nfrom IPython.display import FileLink\nwarnings.filterwarnings(\"ignore\")\n\nCSV_PATH = \"/kaggle/input/datasets/tasneem33355/data33/MentalHealth_Darija.csv\"\nMODEL_NAME = \"xlm-roberta-base\"\nOUTPUT_DIR = \"/kaggle/working/mental_xlmr_final\"\nMAX_LEN = 256\nBATCH_SIZE = 16\nEPOCHS = 6\nLR = 2e-5\nSEED = 42\nLABEL_MAP = {0: \"anxiety\", 2: \"stress\", 3: \"depression\"}\n\ntorch.manual_seed(SEED)\nnp.random.seed(SEED)\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(f\"Device: {device}\")\n\ndf = pd.read_csv(CSV_PATH)\ndf = df[['text', 'text_en', 'label']].dropna()\ndf['combined_text'] = df['text_en'].str.strip() + \" [SEP] \" + df['text'].str.strip()\ndf_filtered = df[df['label'].isin(LABEL_MAP.keys())].copy()\ndf_filtered['target'] = df_filtered['label'].map(LABEL_MAP)\n\nprint(\"\\nClass distribution:\")\nprint(df_filtered['target'].value_counts())\n\nle = LabelEncoder()\ndf_filtered['label_encoded'] = le.fit_transform(df_filtered['target'])\nprint(f\"Classes: {le.classes_}\")\n\nX_train, X_val, y_train, y_val = train_test_split(\n df_filtered['combined_text'].values,\n df_filtered['label_encoded'].values,\n test_size=0.2, random_state=SEED,\n stratify=df_filtered['label_encoded'],\n)\nprint(f\"Train: {len(X_train)} | Val: {len(X_val)}\")\n\nunique_labels = np.unique(y_train)\nclass_weights = compute_class_weight(\"balanced\", classes=unique_labels, y=y_train)\nclass_weights[1] *= 1.4\nclass_weights[2] *= 1.2\nclass_weights_tensor = torch.tensor(class_weights, dtype=torch.float).to(device)\n\nprint(\"\\nClass weights:\")\nfor i, c in enumerate(le.classes_):\n print(f\" {c}: {class_weights[i]:.3f}\")\n\ntokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n\ndef tokenize(batch):\n return tokenizer(batch[\"text\"], padding=False, truncation=True, max_length=MAX_LEN)\n\ntrain_hf = Dataset.from_dict({\"text\": list(X_train), \"labels\": list(y_train)})\nval_hf = Dataset.from_dict({\"text\": list(X_val), \"labels\": list(y_val)})\ntrain_hf = train_hf.map(tokenize, batched=True, remove_columns=[\"text\"])\nval_hf = val_hf.map(tokenize, batched=True, remove_columns=[\"text\"])\ntrain_hf.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\nval_hf.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n\nmodel = AutoModelForSequenceClassification.from_pretrained(\n MODEL_NAME, num_labels=3, ignore_mismatched_sizes=True,\n)\nmodel = model.to(device)\nprint(f\"Params: {sum(p.numel() for p in model.parameters()):,}\")\n\nclass WeightedTrainer(Trainer):\n def __init__(self, class_weights, **kwargs):\n super().__init__(**kwargs)\n self.class_weights = class_weights\n\n def compute_loss(self, model, inputs, return_outputs=False, **kwargs):\n labels = inputs.pop(\"labels\")\n outputs = model(**inputs)\n loss = torch.nn.CrossEntropyLoss(weight=self.class_weights)(outputs.logits, labels)\n return (loss, outputs) if return_outputs else loss\n\ndef compute_metrics(eval_pred):\n logits, labels = eval_pred\n preds = np.argmax(logits, axis=-1)\n return {\n \"accuracy\": round(accuracy_score(labels, preds), 4),\n \"f1_macro\": round(f1_score(labels, preds, average=\"macro\"), 4),\n }\n\ntotal_steps = (len(train_hf) // BATCH_SIZE) * EPOCHS\nwarmup_steps = int(total_steps * 0.05)\n\ntraining_args = TrainingArguments(\n output_dir=OUTPUT_DIR,\n num_train_epochs=EPOCHS,\n learning_rate=LR,\n per_device_train_batch_size=BATCH_SIZE,\n per_device_eval_batch_size=32,\n weight_decay=0.01,\n warmup_steps=warmup_steps,\n lr_scheduler_type=\"cosine\",\n logging_steps=50,\n save_strategy=\"epoch\",\n eval_strategy=\"epoch\",\n save_total_limit=1,\n load_best_model_at_end=True,\n metric_for_best_model=\"f1_macro\",\n greater_is_better=True,\n fp16=True,\n gradient_checkpointing=True,\n gradient_checkpointing_kwargs={\"use_reentrant\": False},\n dataloader_num_workers=2,\n report_to=\"none\",\n seed=SEED,\n save_only_model=True,\n)\n\ntrainer = WeightedTrainer(\n class_weights=class_weights_tensor,\n model=model,\n args=training_args,\n train_dataset=train_hf,\n eval_dataset=val_hf,\n compute_metrics=compute_metrics,\n data_collator=DataCollatorWithPadding(tokenizer=tokenizer),\n callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],\n)\n\nprint(\"\\nStarting training...\")\ntrainer.train()\n\nresults = trainer.evaluate()\nprint(\"\\nFinal Evaluation:\")\nfor k, v in results.items():\n if isinstance(v, float): print(f\" {k}: {round(v, 4)}\")\n\nraw_preds = trainer.predict(val_hf)\npreds = np.argmax(raw_preds.predictions, axis=-1)\nprint(\"\\nClassification Report:\")\nprint(classification_report(y_val, preds, target_names=le.classes_))\n\nos.makedirs(OUTPUT_DIR, exist_ok=True)\ntrainer.save_model(OUTPUT_DIR)\ntokenizer.save_pretrained(OUTPUT_DIR)\nwith open(os.path.join(OUTPUT_DIR, \"label_encoder.pkl\"), \"wb\") as f:\n pickle.dump(le, f)\nprint(f\"\\nSaved to {OUTPUT_DIR}\")\n\nzip_path = \"/kaggle/working/mental_xlmr_final.zip\"\nwith zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zf:\n for root, dirs, files in os.walk(OUTPUT_DIR):\n for file in files:\n file_path = os.path.join(root, file)\n zf.write(file_path, os.path.relpath(file_path, \"/kaggle/working\"))\nprint(f\"Zip size: {os.path.getsize(zip_path)/1e9:.2f} GB\")\nFileLink(zip_path)\n\ndef predict_proba(texts: list) -> list:\n model.eval()\n out = []\n for text in texts:\n enc = tokenizer(text, return_tensors=\"pt\", truncation=True, max_length=MAX_LEN).to(device)\n with torch.no_grad():\n probs = torch.softmax(model(**enc).logits, dim=-1).squeeze().cpu().numpy()\n out.append({c: round(float(p), 4) for c, p in zip(le.classes_, probs)})\n return out\n\nprint(\"\\nSample prediction:\")\nprint(predict_proba([X_val[0]]))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-11T12:58:08.412267Z","iopub.execute_input":"2026-04-11T12:58:08.412999Z","iopub.status.idle":"2026-04-11T14:35:31.904845Z","shell.execute_reply.started":"2026-04-11T12:58:08.412950Z","shell.execute_reply":"2026-04-11T14:35:31.903928Z"}},"outputs":[{"name":"stdout","text":"Device: cuda\n\nClass distribution:\ntarget\nstress 15094\ndepression 10644\nanxiety 3623\nName: count, dtype: int64\nClasses: ['anxiety' 'depression' 'stress']\nTrain: 23488 | Val: 5873\n\nClass weights:\n anxiety: 2.702\n depression: 1.287\n stress: 0.778\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"config.json: 0%| | 0.00/615 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f5e891ff000d4e458c29bd7f02817c8c"}},"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":"be68f0b56be541de9687c31beb7e0dfc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"sentencepiece.bpe.model: 0%| | 0.00/5.07M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2c5b6fed03bf48a1a1ba39d97ebb1589"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9ab8a76379a24e928d47fffdad7ec5d4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Map: 0%| | 0/23488 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a1393c6fde5842ccae67bee76b6b34d2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Map: 0%| | 0/5873 [00:00<?, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"db8e5382f07f41a49d75b8e69ba63800"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors: 0%| | 0.00/1.12G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8a027132c38a4a78b8cffe7033b49b9c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading weights: 0%| | 0/197 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"115bc8ca34204a868e50e06a9c52573f"}},"metadata":{}},{"name":"stderr","text":"XLMRobertaForSequenceClassification LOAD REPORT from: xlm-roberta-base\nKey | Status | \n----------------------------+------------+-\nlm_head.dense.weight | UNEXPECTED | \nlm_head.bias | UNEXPECTED | \nlm_head.layer_norm.weight | UNEXPECTED | \nroberta.pooler.dense.weight | UNEXPECTED | \nlm_head.layer_norm.bias | UNEXPECTED | \nlm_head.dense.bias | UNEXPECTED | \nroberta.pooler.dense.bias | UNEXPECTED | \nclassifier.dense.weight | MISSING | \nclassifier.out_proj.bias | MISSING | \nclassifier.dense.bias | MISSING | \nclassifier.out_proj.weight | MISSING | \n\nNotes:\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","output_type":"stream"},{"name":"stdout","text":"Params: 278,045,955\n\nStarting training...\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n <div>\n \n <progress value='4404' max='4404' style='width:300px; height:20px; vertical-align: middle;'></progress>\n [4404/4404 1:32:45, Epoch 6/6]\n </div>\n <table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: left;\">\n <th>Epoch</th>\n <th>Training Loss</th>\n <th>Validation Loss</th>\n <th>Accuracy</th>\n <th>F1 Macro</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>1</td>\n <td>0.437816</td>\n <td>0.397521</td>\n <td>0.768600</td>\n <td>0.807500</td>\n </tr>\n <tr>\n <td>2</td>\n <td>0.386207</td>\n <td>0.406955</td>\n <td>0.794000</td>\n <td>0.827200</td>\n </tr>\n <tr>\n <td>3</td>\n <td>0.329776</td>\n <td>0.392873</td>\n <td>0.793300</td>\n <td>0.830900</td>\n </tr>\n <tr>\n <td>4</td>\n <td>0.270477</td>\n <td>0.445115</td>\n <td>0.796400</td>\n <td>0.830000</td>\n </tr>\n <tr>\n <td>5</td>\n <td>0.181897</td>\n <td>0.511371</td>\n <td>0.798400</td>\n <td>0.831800</td>\n </tr>\n <tr>\n <td>6</td>\n <td>0.189214</td>\n <td>0.525796</td>\n <td>0.797200</td>\n <td>0.830900</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":"95bcdb626d404e30a838c0c1f48b65aa"}},"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":"8d64d2fb4ae94ef9b6a5c81aa13d0bcd"}},"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":"e2a1ef9b469a4ce786afb0398d4d5468"}},"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":"8a8d2f9c947341baa18cabda2fca8b6c"}},"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":"deaeb35b62f74ccdb94ee909250c5b58"}},"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":"97a648ca4a874b25870e5112f4e4a0fa"}},"metadata":{}},{"name":"stderr","text":"There were missing keys in the checkpoint model loaded: ['roberta.embeddings.LayerNorm.weight', 'roberta.embeddings.LayerNorm.bias', 'roberta.encoder.layer.0.attention.output.LayerNorm.weight', 'roberta.encoder.layer.0.attention.output.LayerNorm.bias', 'roberta.encoder.layer.0.output.LayerNorm.weight', 'roberta.encoder.layer.0.output.LayerNorm.bias', 'roberta.encoder.layer.1.attention.output.LayerNorm.weight', 'roberta.encoder.layer.1.attention.output.LayerNorm.bias', 'roberta.encoder.layer.1.output.LayerNorm.weight', 'roberta.encoder.layer.1.output.LayerNorm.bias', 'roberta.encoder.layer.2.attention.output.LayerNorm.weight', 'roberta.encoder.layer.2.attention.output.LayerNorm.bias', 'roberta.encoder.layer.2.output.LayerNorm.weight', 'roberta.encoder.layer.2.output.LayerNorm.bias', 'roberta.encoder.layer.3.attention.output.LayerNorm.weight', 'roberta.encoder.layer.3.attention.output.LayerNorm.bias', 'roberta.encoder.layer.3.output.LayerNorm.weight', 'roberta.encoder.layer.3.output.LayerNorm.bias', 'roberta.encoder.layer.4.attention.output.LayerNorm.weight', 'roberta.encoder.layer.4.attention.output.LayerNorm.bias', 'roberta.encoder.layer.4.output.LayerNorm.weight', 'roberta.encoder.layer.4.output.LayerNorm.bias', 'roberta.encoder.layer.5.attention.output.LayerNorm.weight', 'roberta.encoder.layer.5.attention.output.LayerNorm.bias', 'roberta.encoder.layer.5.output.LayerNorm.weight', 'roberta.encoder.layer.5.output.LayerNorm.bias', 'roberta.encoder.layer.6.attention.output.LayerNorm.weight', 'roberta.encoder.layer.6.attention.output.LayerNorm.bias', 'roberta.encoder.layer.6.output.LayerNorm.weight', 'roberta.encoder.layer.6.output.LayerNorm.bias', 'roberta.encoder.layer.7.attention.output.LayerNorm.weight', 'roberta.encoder.layer.7.attention.output.LayerNorm.bias', 'roberta.encoder.layer.7.output.LayerNorm.weight', 'roberta.encoder.layer.7.output.LayerNorm.bias', 'roberta.encoder.layer.8.attention.output.LayerNorm.weight', 'roberta.encoder.layer.8.attention.output.LayerNorm.bias', 'roberta.encoder.layer.8.output.LayerNorm.weight', 'roberta.encoder.layer.8.output.LayerNorm.bias', 'roberta.encoder.layer.9.attention.output.LayerNorm.weight', 'roberta.encoder.layer.9.attention.output.LayerNorm.bias', 'roberta.encoder.layer.9.output.LayerNorm.weight', 'roberta.encoder.layer.9.output.LayerNorm.bias', 'roberta.encoder.layer.10.attention.output.LayerNorm.weight', 'roberta.encoder.layer.10.attention.output.LayerNorm.bias', 'roberta.encoder.layer.10.output.LayerNorm.weight', 'roberta.encoder.layer.10.output.LayerNorm.bias', 'roberta.encoder.layer.11.attention.output.LayerNorm.weight', 'roberta.encoder.layer.11.attention.output.LayerNorm.bias', 'roberta.encoder.layer.11.output.LayerNorm.weight', 'roberta.encoder.layer.11.output.LayerNorm.bias'].\nThere were unexpected keys in the checkpoint model loaded: ['roberta.embeddings.LayerNorm.beta', 'roberta.embeddings.LayerNorm.gamma', 'roberta.encoder.layer.0.attention.output.LayerNorm.beta', 'roberta.encoder.layer.0.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.0.output.LayerNorm.beta', 'roberta.encoder.layer.0.output.LayerNorm.gamma', 'roberta.encoder.layer.1.attention.output.LayerNorm.beta', 'roberta.encoder.layer.1.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.1.output.LayerNorm.beta', 'roberta.encoder.layer.1.output.LayerNorm.gamma', 'roberta.encoder.layer.2.attention.output.LayerNorm.beta', 'roberta.encoder.layer.2.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.2.output.LayerNorm.beta', 'roberta.encoder.layer.2.output.LayerNorm.gamma', 'roberta.encoder.layer.3.attention.output.LayerNorm.beta', 'roberta.encoder.layer.3.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.3.output.LayerNorm.beta', 'roberta.encoder.layer.3.output.LayerNorm.gamma', 'roberta.encoder.layer.4.attention.output.LayerNorm.beta', 'roberta.encoder.layer.4.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.4.output.LayerNorm.beta', 'roberta.encoder.layer.4.output.LayerNorm.gamma', 'roberta.encoder.layer.5.attention.output.LayerNorm.beta', 'roberta.encoder.layer.5.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.5.output.LayerNorm.beta', 'roberta.encoder.layer.5.output.LayerNorm.gamma', 'roberta.encoder.layer.6.attention.output.LayerNorm.beta', 'roberta.encoder.layer.6.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.6.output.LayerNorm.beta', 'roberta.encoder.layer.6.output.LayerNorm.gamma', 'roberta.encoder.layer.7.attention.output.LayerNorm.beta', 'roberta.encoder.layer.7.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.7.output.LayerNorm.beta', 'roberta.encoder.layer.7.output.LayerNorm.gamma', 'roberta.encoder.layer.8.attention.output.LayerNorm.beta', 'roberta.encoder.layer.8.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.8.output.LayerNorm.beta', 'roberta.encoder.layer.8.output.LayerNorm.gamma', 'roberta.encoder.layer.9.attention.output.LayerNorm.beta', 'roberta.encoder.layer.9.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.9.output.LayerNorm.beta', 'roberta.encoder.layer.9.output.LayerNorm.gamma', 'roberta.encoder.layer.10.attention.output.LayerNorm.beta', 'roberta.encoder.layer.10.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.10.output.LayerNorm.beta', 'roberta.encoder.layer.10.output.LayerNorm.gamma', 'roberta.encoder.layer.11.attention.output.LayerNorm.beta', 'roberta.encoder.layer.11.attention.output.LayerNorm.gamma', 'roberta.encoder.layer.11.output.LayerNorm.beta', 'roberta.encoder.layer.11.output.LayerNorm.gamma'].\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":""},"metadata":{}},{"name":"stdout","text":"\nFinal Evaluation:\n eval_loss: 0.5114\n eval_accuracy: 0.7981\n eval_f1_macro: 0.8316\n eval_runtime: 50.7706\n eval_samples_per_second: 115.677\n eval_steps_per_second: 1.812\n epoch: 6.0\n\nClassification Report:\n precision recall f1-score support\n\n anxiety 0.94 0.96 0.95 725\n depression 0.72 0.77 0.74 2129\n stress 0.82 0.78 0.80 3019\n\n accuracy 0.80 5873\n macro avg 0.83 0.84 0.83 5873\nweighted avg 0.80 0.80 0.80 5873\n\n","output_type":"stream"},{"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":"e4d5964cf1cc4491bf590eece4d2a2a8"}},"metadata":{}},{"name":"stdout","text":"\nSaved to /kaggle/working/mental_xlmr_final\nZip size: 1.72 GB\n\nSample prediction:\n[{'anxiety': 0.0002, 'depression': 0.9732, 'stress': 0.0265}]\n","output_type":"stream"}],"execution_count":3},{"id":"da2d68cb-54f4-445c-b293-4889d3c8cdcf","cell_type":"code","source":"from IPython.display import Javascript\n\nJS = \"\"\"\nvar link = document.createElement('a');\nlink.href = '/kaggle/working/mental_xlmr_final.zip';\nlink.download = 'mental_xlmr_final.zip';\ndocument.body.appendChild(link);\nlink.click();\ndocument.body.removeChild(link);\n\"\"\"\ndisplay(Javascript(JS))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-11T14:57:58.633732Z","iopub.execute_input":"2026-04-11T14:57:58.634811Z","iopub.status.idle":"2026-04-11T14:57:58.639932Z","shell.execute_reply.started":"2026-04-11T14:57:58.634764Z","shell.execute_reply":"2026-04-11T14:57:58.639330Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"\nvar link = document.createElement('a');\nlink.href = '/kaggle/working/mental_xlmr_final.zip';\nlink.download = 'mental_xlmr_final.zip';\ndocument.body.appendChild(link);\nlink.click();\ndocument.body.removeChild(link);\n"},"metadata":{}}],"execution_count":7}]} |