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{ "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "HHDxm1QZDZp3", "outputId": "4a1a465f-f9f7-43f6-e144-3f9c43544bcc" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.52.2)\n", "Requirement already satisfied: datasets in /usr/local/lib/python3.11/dist-packages (2.14.4)\n", "Requirement already satisfied: accelerate in /usr/local/lib/python3.11/dist-packages (1.7.0)\n", "Collecting evaluate\n", " Downloading evaluate-0.4.3-py3-none-any.whl.metadata (9.2 kB)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.1)\n", "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.6.0+cu124)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.18.0)\n", "Requirement 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nvidia-cufft-cu12\n", " Found existing installation: nvidia-cufft-cu12 11.2.3.61\n", " Uninstalling nvidia-cufft-cu12-11.2.3.61:\n", " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n", " Attempting uninstall: nvidia-cuda-runtime-cu12\n", " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n", " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n", " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n", " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n", " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n", " Attempting uninstall: nvidia-cuda-cupti-cu12\n", " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n", " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n", " Attempting uninstall: nvidia-cublas-cu12\n", " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n", " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n", " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n", " Attempting uninstall: nvidia-cusparse-cu12\n", " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n", " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n", " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n", " Attempting uninstall: nvidia-cudnn-cu12\n", " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n", " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n", " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n", " Attempting uninstall: nvidia-cusolver-cu12\n", " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n", " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n", " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n", "Successfully installed evaluate-0.4.3 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n", "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.11/dist-packages (0.2.0)\n" ] } ], "source": [ "!pip install transformers datasets accelerate evaluate scikit-learn torch\n", "!pip install sentencepiece # for tokenizer support\n" ] }, { "cell_type": "markdown", "source": [ "Step 2: Import Datasets" ], "metadata": { "id": "i_bQfNzNFXNn" } }, { "cell_type": "code", "source": [ "!pip install datasets" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "FtcgVLm_EQKt", "outputId": "68a5236b-3f21-436b-e695-6631cf03dc51" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: datasets in /usr/local/lib/python3.11/dist-packages (2.14.4)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from 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urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.19.0->datasets) (2.4.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.19.0->datasets) (2025.4.26)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.9.0.post0)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.2)\n", "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.2)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n" ] } ] }, { "cell_type": "code", "source": [ "from google.colab import files\n", "uploaded = files.upload() # Upload the downloaded .parquet file here\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 73 }, "id": "owEs0yqPLZBq", "outputId": "a30b8853-925a-4412-ada5-9b6288bdd646" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \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", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving train-00000-of-00001.parquet to train-00000-of-00001.parquet\n" ] } ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "\n", "# Load the uploaded Parquet file\n", "df = pd.read_parquet(\"train-00000-of-00001.parquet\")\n", "\n", "# Show all available column names\n", "print(df.columns)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "U_3xRD2GNNfm", "outputId": "ceda56ce-6528-4a4e-c700-5a8bdbaab6a3" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Index(['text', 'id', 'author', 'subreddit', 'link_id', 'parent_id',\n", " 'created_utc', 'rater_id', 'example_very_unclear', 'admiration',\n", " 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion',\n", " 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust',\n", " 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy',\n", " 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief',\n", " 'remorse', 'sadness', 'surprise', 'neutral'],\n", " dtype='object')\n" ] } ] }, { "cell_type": "markdown", "source": [ "Step 3: Dataset Formating and defining targets" ], "metadata": { "id": "f5O2G3qTFb3Q" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "from sklearn.preprocessing import LabelEncoder\n", "\n", "# Load the uploaded Parquet file\n", "df = pd.read_parquet(\"train-00000-of-00001.parquet\")\n", "\n", "# List of all emotion columns\n", "emotion_columns = [\n", " 'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring',\n", " 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval',\n", " 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief',\n", " 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization',\n", " 'relief', 'remorse', 'sadness', 'surprise', 'neutral'\n", "]\n", "\n", "# Extract active emotions (where value == 1)\n", "df[\"emotions\"] = df[emotion_columns].apply(\n", " lambda row: [emotion for emotion in emotion_columns if row[emotion] == 1],\n", " axis=1\n", ")\n", "\n", "# Emotion to sentiment mapping\n", "emotion_to_sentiment = {\n", " 'admiration': 'positive', 'amusement': 'positive', 'approval': 'positive',\n", " 'caring': 'positive', 'desire': 'positive', 'excitement': 'positive',\n", " 'gratitude': 'positive', 'joy': 'positive', 'love': 'positive',\n", " 'optimism': 'positive', 'pride': 'positive', 'relief': 'positive',\n", "\n", " 'anger': 'negative', 'annoyance': 'negative', 'disapproval': 'negative',\n", " 'disgust': 'negative', 'embarrassment': 'negative', 'fear': 'negative',\n", " 'grief': 'negative', 'nervousness': 'negative', 'remorse': 'negative',\n", " 'disappointment': 'negative', 'sadness': 'negative',\n", "\n", " 'confusion': 'neutral', 'curiosity': 'neutral', 'realization': 'neutral',\n", " 'surprise': 'neutral', 'neutral': 'neutral'\n", "}\n", "\n", "# Map to sentiment category\n", "df[\"sentiment\"] = df[\"emotions\"].apply(\n", " lambda emotions: next((emotion_to_sentiment[e] for e in emotions if e in emotion_to_sentiment), \"neutral\")\n", ")\n", "\n", "# Assign stress/anxiety scores\n", "df[\"stress\"] = df[\"emotions\"].apply(lambda x: 1.0 if \"fear\" in x or \"nervousness\" in x else 0.2)\n", "df[\"anxiety\"] = df[\"emotions\"].apply(lambda x: 1.0 if \"nervousness\" in x else 0.1)\n", "\n", "# Encode sentiment labels (0 = neg, 1 = neutral, 2 = pos)\n", "label_encoder = LabelEncoder()\n", "df[\"sentiment_label\"] = label_encoder.fit_transform(df[\"sentiment\"])\n", "\n", "# Final dataset\n", "final_df = df[[\"text\", \"sentiment_label\", \"stress\", \"anxiety\"]]\n", "print(\"✅ Final dataset prepared with\", len(final_df), \"entries.\")\n", "final_df.head(10)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 380 }, "id": "Norg8SEfDekp", "outputId": "a1228d13-e137-4d83-c2aa-d32ce9153a6e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Final dataset prepared with 211225 entries.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " text sentiment_label stress \\\n", "0 That game hurt. 0 0.2 \n", "1 >sexuality shouldn’t be a grouping category I... 1 0.2 \n", "2 You do right, if you don't care then fuck 'em! 1 0.2 \n", "3 Man I love reddit. 2 0.2 \n", "4 [NAME] was nowhere near them, he was by the Fa... 1 0.2 \n", "5 Right? Considering it’s such an important docu... 2 0.2 \n", "6 He isn't as big, but he's still quite popular.... 0 0.2 \n", "7 That's crazy; I went to a super [RELIGION] hig... 2 0.2 \n", "8 that's adorable asf 2 0.2 \n", "9 \"Sponge Blurb Pubs Quaw Haha GURR ha AAa!\" fin... 2 0.2 \n", "\n", " anxiety \n", "0 0.1 \n", "1 0.1 \n", "2 0.1 \n", "3 0.1 \n", "4 0.1 \n", "5 0.1 \n", "6 0.1 \n", "7 0.1 \n", "8 0.1 \n", "9 0.1 " ], "text/html": [ "\n", "
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textsentiment_labelstressanxiety
0That game hurt.00.20.1
1>sexuality shouldn’t be a grouping category I...10.20.1
2You do right, if you don't care then fuck 'em!10.20.1
3Man I love reddit.20.20.1
4[NAME] was nowhere near them, he was by the Fa...10.20.1
5Right? Considering it’s such an important docu...20.20.1
6He isn't as big, but he's still quite popular....00.20.1
7That's crazy; I went to a super [RELIGION] hig...20.20.1
8that's adorable asf20.20.1
9\"Sponge Blurb Pubs Quaw Haha GURR ha AAa!\" fin...20.20.1
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "final_df" } }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "from sklearn.preprocessing import LabelEncoder\n", "\n", "# Load the uploaded Parquet file\n", "df = pd.read_parquet(\"train-00000-of-00001.parquet\")\n", "\n", "# List of all emotion columns\n", "emotion_columns = [\n", " 'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring',\n", " 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval',\n", " 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief',\n", " 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization',\n", " 'relief', 'remorse', 'sadness', 'surprise', 'neutral'\n", "]\n", "\n", "# Extract active emotions\n", "df[\"emotions\"] = df[emotion_columns].apply(\n", " lambda row: [emotion for emotion in emotion_columns if row[emotion] == 1],\n", " axis=1\n", ")\n", "\n", "# Map to sentiment\n", "emotion_to_sentiment = {\n", " 'admiration': 'positive', 'amusement': 'positive', 'approval': 'positive',\n", " 'caring': 'positive', 'desire': 'positive', 'excitement': 'positive',\n", " 'gratitude': 'positive', 'joy': 'positive', 'love': 'positive',\n", " 'optimism': 'positive', 'pride': 'positive', 'relief': 'positive',\n", "\n", " 'anger': 'negative', 'annoyance': 'negative', 'disapproval': 'negative',\n", " 'disgust': 'negative', 'embarrassment': 'negative', 'fear': 'negative',\n", " 'grief': 'negative', 'nervousness': 'negative', 'remorse': 'negative',\n", " 'disappointment': 'negative', 'sadness': 'negative',\n", "\n", " 'confusion': 'neutral', 'curiosity': 'neutral', 'realization': 'neutral',\n", " 'surprise': 'neutral', 'neutral': 'neutral'\n", "}\n", "\n", "df[\"sentiment\"] = df[\"emotions\"].apply(\n", " lambda emotions: next((emotion_to_sentiment[e] for e in emotions if e in emotion_to_sentiment), \"neutral\")\n", ")\n", "\n", "# Expanded emotion sets\n", "high_stress_emotions = {\n", " \"fear\", \"nervousness\", \"grief\", \"sadness\", \"embarrassment\", \"disgust\", \"remorse\", \"anger\", \"disappointment\"\n", "}\n", "high_anxiety_emotions = {\n", " \"nervousness\", \"fear\", \"embarrassment\", \"grief\", \"remorse\", \"sadness\"\n", "}\n", "\n", "# Smarter scoring\n", "df[\"stress\"] = df[\"emotions\"].apply(\n", " lambda x: 1.0 if any(e in high_stress_emotions for e in x) else 0.2\n", ")\n", "df[\"anxiety\"] = df[\"emotions\"].apply(\n", " lambda x: 1.0 if any(e in high_anxiety_emotions for e in x) else 0.1\n", ")\n", "\n", "# Encode sentiment: 0 = negative, 1 = neutral, 2 = positive\n", "label_encoder = LabelEncoder()\n", "df[\"sentiment_label\"] = label_encoder.fit_transform(df[\"sentiment\"])\n", "\n", "# Final dataset\n", "final_df = df[[\"text\", \"sentiment_label\", \"stress\", \"anxiety\"]]\n", "print(\"✅ Final dataset prepared with\", len(final_df), \"entries.\")\n", "final_df.head(10)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 380 }, "id": "cT5Kfhy7suqo", "outputId": "5d574ae9-3925-4588-9988-e98da00fe635" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Final dataset prepared with 211225 entries.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " text sentiment_label stress \\\n", "0 That game hurt. 0 1.0 \n", "1 >sexuality shouldn’t be a grouping category I... 1 0.2 \n", "2 You do right, if you don't care then fuck 'em! 1 0.2 \n", "3 Man I love reddit. 2 0.2 \n", "4 [NAME] was nowhere near them, he was by the Fa... 1 0.2 \n", "5 Right? Considering it’s such an important docu... 2 0.2 \n", "6 He isn't as big, but he's still quite popular.... 0 0.2 \n", "7 That's crazy; I went to a super [RELIGION] hig... 2 0.2 \n", "8 that's adorable asf 2 0.2 \n", "9 \"Sponge Blurb Pubs Quaw Haha GURR ha AAa!\" fin... 2 0.2 \n", "\n", " anxiety \n", "0 1.0 \n", "1 0.1 \n", "2 0.1 \n", "3 0.1 \n", "4 0.1 \n", "5 0.1 \n", "6 0.1 \n", "7 0.1 \n", "8 0.1 \n", "9 0.1 " ], "text/html": [ "\n", "
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textsentiment_labelstressanxiety
0That game hurt.01.01.0
1>sexuality shouldn’t be a grouping category I...10.20.1
2You do right, if you don't care then fuck 'em!10.20.1
3Man I love reddit.20.20.1
4[NAME] was nowhere near them, he was by the Fa...10.20.1
5Right? Considering it’s such an important docu...20.20.1
6He isn't as big, but he's still quite popular....00.20.1
7That's crazy; I went to a super [RELIGION] hig...20.20.1
8that's adorable asf20.20.1
9\"Sponge Blurb Pubs Quaw Haha GURR ha AAa!\" fin...20.20.1
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "final_df" } }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "markdown", "source": [ "Step 4: Main Model" ], "metadata": { "id": "RCdr8KVVN-ie" } }, { "cell_type": "code", "source": [ "import torch\n", "import torch.nn as nn\n", "from transformers import AutoModel, AutoTokenizer\n", "\n", "class MultiTaskSentimentStressModel(nn.Module):\n", " def __init__(self, model_name=\"distilbert-base-uncased\", num_sentiment_classes=3):\n", " super(MultiTaskSentimentStressModel, self).__init__()\n", " self.base_model = AutoModel.from_pretrained(model_name)\n", " self.hidden_size = self.base_model.config.hidden_size\n", "\n", " # Heads\n", " self.dropout = nn.Dropout(0.3)\n", " self.sentiment_classifier = nn.Linear(self.hidden_size, num_sentiment_classes)\n", " self.stress_regressor = nn.Linear(self.hidden_size, 1)\n", " self.anxiety_regressor = nn.Linear(self.hidden_size, 1)\n", "\n", " def forward(self, input_ids, attention_mask):\n", " output = self.base_model(input_ids=input_ids, attention_mask=attention_mask)\n", " cls_output = self.dropout(output.last_hidden_state[:, 0, :]) # Use [CLS] token output\n", "\n", " sentiment_logits = self.sentiment_classifier(cls_output)\n", " stress_score = torch.sigmoid(self.stress_regressor(cls_output))\n", " anxiety_score = torch.sigmoid(self.anxiety_regressor(cls_output))\n", "\n", " return sentiment_logits, stress_score, anxiety_score\n", "\n", "# 🔧 Initialize tokenizer and model\n", "model_name = \"distilbert-base-uncased\"\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "model = MultiTaskSentimentStressModel(model_name)\n", "\n", "# ✅ Quick test with dummy input\n", "text = \"I feel really anxious and overwhelmed today.\"\n", "tokens = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True)\n", "with torch.no_grad():\n", " sentiment_logits, stress_pred, anxiety_pred = model(**tokens)\n", "\n", "print(\"Sentiment:\", torch.argmax(sentiment_logits).item())\n", "print(\"Stress Score:\", stress_pred.item())\n", "print(\"Anxiety Score:\", anxiety_pred.item())\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 388, "referenced_widgets": [ "8f34c421431a4d4c97c29fe4800aa8fd", "ed323cb334064fe28dbacaf88cc3c139", "a1978eb178e84d8cae9c38134e7cdea4", "707b538fc1114174867a808a38872e01", "3efb4cc6b154495ab82c63f00c81603f", "dec9b9fd879447bc805d5a9572921263", "0002e08627d24db3b092c40c30a6a347", "754298495d914d199f5d1fd823f4528c", "88fdfab31f1f42d7877555867dd5d48a", "33e393d7b0a74f179ea971a70cc1762d", "91b25583a01248509996b8a6ca9beccf", "de63978f54914d9b999919d939793c2d", "a067244a762d4a1184e69b51e322952d", "53f8b4c300f64949966193b323ce6221", "7b4b14ef34a6451fb14fd7a55376c730", "2172cb1229d74b7aa31776ed7e6d00e1", "8701f62da5624e048eac93e53d83a8ae", "f22cc841efd4494196899aafd8724892", "42f9ee3682ca4e20b69b0cd81bb632d1", "3064577854ea469181026e0ff84f9b65", "0e6c583b516446a1887b623687fac0a4", "a02e47e0d2b04b0197db0870c91b6a85", "a7ecd33b52be4a19b1daf33eafcbcbda", "ddcb83d2a782473599c7044d07195f31", "4ef440d048e945d7b3e85d87cf40a876", "bec86ed5d46f47e9b13a16232a145b70", "6f4585f67a1140b3b04bcbef6195daee", "6aac9ee101124f289aba8630a00c3158", "0e5404ecad5f406e89fb7214eaf7b849", "1df6b9ff646541719ff1645911417318", "54cc725e6f124b1fa792fe6e67be01df", "09055588add4465c905e5855dca09091", "7a395d852c404f40afcac3eaf6bd9122", "31a428509d5040ffaebc15adc947e342", "3b25dd247d4045b2ae4b4032d834787f", "852d9468d9914bb58ef33fa3a496fff5", "4346d384a3524f2aaead80b32703c089", "d67adb915cc24423a63e174ee4a5eb61", "ddfe5af65f1c443e8d5037e5b4bf33be", "e473ed64ad8d42619b1f652e52eefd8d", "2632b994b5224674a0b941f9bcd8148a", "472aac9fe4d349049b6000188916ff8b", "08830eedfada49dcaf73313245408709", "6077bf47c897451481bfbfef18102c76", "57bd952d24e64a94b965b12620af793d", "85dea0b51f6142469d1e780678793bd2", "5711d1a875ee42b48f3b6762ae76f13d", "b5e9cf7a300d41b3816df820d921a1f2", "fd0b5d6bb8fa46aaab10d774acf4c320", "1a11e64636c9446fad00f0636a218c7c", "77e4d2749fa04d59bdccaa68ada6809d", "2af8aed39ffb411dbe22f7e7d9a9b73b", "065e1ed96b724032a846bb2cdbfb010e", "8f1ae2a3ebdf49d2b22977349514784a", "18cc7ea9cbfc4858a0bfe7c0a7235987" ] }, "id": "4IEB1azaDpkn", "outputId": "6467548c-66e4-4892-cfd9-887703731a36" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.11/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": [ "tokenizer_config.json: 0%| | 0.00/48.0 [00:00= max_batches:\n", " break\n", " input_ids = batch[\"input_ids\"].to(device)\n", " attention_mask = batch[\"attention_mask\"].to(device)\n", " sentiment = batch[\"sentiment_label\"].to(device)\n", " stress = batch[\"stress\"].to(device)\n", " anxiety = batch[\"anxiety\"].to(device)\n", "\n", " optimizer.zero_grad()\n", " sentiment_logits, stress_pred, anxiety_pred = model(input_ids, attention_mask)\n", "\n", " loss_sentiment = ce_loss(sentiment_logits, sentiment)\n", " loss_stress = mse_loss(stress_pred.squeeze(), stress)\n", " loss_anxiety = mse_loss(anxiety_pred.squeeze(), anxiety)\n", "\n", " loss = loss_sentiment + loss_stress + loss_anxiety\n", " loss.backward()\n", " optimizer.step()\n", " total_loss += loss.item()\n", " return total_loss / max_batches\n", "\n", "# Step 6.5 – Evaluation function (LIMITED batches)\n", "def eval_epoch(model, dataloader, max_batches=100):\n", " model.eval()\n", " total_loss = 0\n", " with torch.no_grad():\n", " for i, batch in enumerate(tqdm(dataloader, desc=\"Evaluating\")):\n", " if i >= max_batches:\n", " break\n", " input_ids = batch[\"input_ids\"].to(device)\n", " attention_mask = batch[\"attention_mask\"].to(device)\n", " sentiment = batch[\"sentiment_label\"].to(device)\n", " stress = batch[\"stress\"].to(device)\n", " anxiety = batch[\"anxiety\"].to(device)\n", "\n", " sentiment_logits, stress_pred, anxiety_pred = model(input_ids, attention_mask)\n", "\n", " loss_sentiment = ce_loss(sentiment_logits, sentiment)\n", " loss_stress = mse_loss(stress_pred.squeeze(), stress)\n", " loss_anxiety = mse_loss(anxiety_pred.squeeze(), anxiety)\n", "\n", " loss = loss_sentiment + loss_stress + loss_anxiety\n", " total_loss += loss.item()\n", " return total_loss / max_batches\n", "\n", "# Step 6.6 – Run training\n", "EPOCHS = 2\n", "for epoch in range(EPOCHS):\n", " print(f\"\\n🔁 Epoch {epoch + 1}/{EPOCHS}\")\n", " train_loss = train_epoch(model, train_loader, max_batches=100)\n", " val_loss = eval_epoch(model, val_loader, max_batches=100)\n", " print(f\"✅ Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gTJCFaLoOquA", "outputId": "adf9d145-1cc4-4020-a8c3-6a90d5cd1146" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "🔁 Epoch 1/2\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Training: 32%|███▏ | 100/313 [12:24<26:25, 7.44s/it]\n", "Evaluating: 100%|██████████| 63/63 [02:17<00:00, 2.18s/it]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "✅ Train Loss: 1.1532, Val Loss: 0.6480\n", "\n", "🔁 Epoch 2/2\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Training: 32%|███▏ | 100/313 [11:58<25:30, 7.18s/it]\n", "Evaluating: 100%|██████████| 63/63 [02:18<00:00, 2.19s/it]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "✅ Train Loss: 0.9415, Val Loss: 0.6263\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] } ] }, { "cell_type": "markdown", "source": [ "Step 7: Save, predict and evaluate" ], "metadata": { "id": "xpoMCOPtbwZ3" } }, { "cell_type": "code", "source": [ "import torch\n", "import os\n", "from sklearn.metrics import accuracy_score, mean_absolute_error\n", "\n", "# Step 7.1 – Save model and tokenizer\n", "save_dir = \"multi_task_model_v1\"\n", "os.makedirs(save_dir, exist_ok=True)\n", "torch.save(model.state_dict(), os.path.join(save_dir, \"pytorch_model.bin\"))\n", "tokenizer.save_pretrained(save_dir)\n", "print(\"✅ Model and tokenizer saved to\", save_dir)\n", "\n", "# Step 7.2 – Use only first N batches for prediction\n", "N_BATCHES = 50 # You can reduce/increase as needed\n", "\n", "model.eval()\n", "true_sentiments, pred_sentiments = [], []\n", "true_stress, pred_stress = [], []\n", "true_anxiety, pred_anxiety = [], []\n", "\n", "with torch.no_grad():\n", " for i, batch in enumerate(tqdm(test_loader, desc=\"📊 Predicting on test set\")):\n", " if i >= N_BATCHES:\n", " break\n", " input_ids = batch[\"input_ids\"].to(device)\n", " attention_mask = batch[\"attention_mask\"].to(device)\n", " sentiment = batch[\"sentiment_label\"].to(device)\n", " stress = batch[\"stress\"].to(device)\n", " anxiety = batch[\"anxiety\"].to(device)\n", "\n", " sentiment_logits, stress_pred, anxiety_pred = model(input_ids, attention_mask)\n", "\n", " true_sentiments.extend(sentiment.cpu().tolist())\n", " pred_sentiments.extend(torch.argmax(sentiment_logits, dim=1).cpu().tolist())\n", " true_stress.extend(stress.cpu().tolist())\n", " pred_stress.extend(stress_pred.squeeze().cpu().tolist())\n", " true_anxiety.extend(anxiety.cpu().tolist())\n", " pred_anxiety.extend(anxiety_pred.squeeze().cpu().tolist())\n", "\n", "# Step 7.3 – Evaluation\n", "acc = accuracy_score(true_sentiments, pred_sentiments)\n", "mae_stress = mean_absolute_error(true_stress, pred_stress)\n", "mae_anxiety = mean_absolute_error(true_anxiety, pred_anxiety)\n", "\n", "print(f\"\\n📊 Evaluation on {len(true_sentiments)} test samples:\")\n", "print(f\"✅ Sentiment Accuracy: {acc:.4f}\")\n", "print(f\"✅ Stress MAE: {mae_stress:.4f}\")\n", "print(f\"✅ Anxiety MAE: {mae_anxiety:.4f}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aYlwPhbsP4ri", "outputId": "0b25e2ff-4d16-4791-e337-ce593ef9613a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Model and tokenizer saved to multi_task_model_v1\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "📊 Predicting on test set: 4%|▍ | 50/1321 [01:50<46:37, 2.20s/it]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "📊 Evaluation on 800 test samples:\n", "✅ Sentiment Accuracy: 0.5900\n", "✅ Stress MAE: 0.2076\n", "✅ Anxiety MAE: 0.1460\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] } ] }, { "cell_type": "markdown", "source": [ "Save to Hugging Face" ], "metadata": { "id": "g26hqSBC3aR1" } }, { "cell_type": "code", "source": [ "# ✅ Install dependencies\n", "!pip install -q huggingface_hub transformers\n", "\n", "# ✅ Login to Hugging Face Hub\n", "from huggingface_hub import login\n", "login(token=\"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\") # ⬅️ Replace with your real token\n" ], "metadata": { "id": "1R4_YWuH3cVo" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ✅ Step 1: Manually save model and config\n", "from transformers import AutoConfig, AutoTokenizer, AutoModelForSequenceClassification\n", "import torch\n", "import os\n", "import json\n", "\n", "save_dir = \"multi_task_model_v1\"\n", "os.makedirs(save_dir, exist_ok=True)\n", "\n", "# 🧠 Your fine-tuned model (assumed already defined)\n", "# model = ... # should already exist in your code\n", "\n", "# ✅ 1. Save model weights\n", "torch.save(model.state_dict(), f\"{save_dir}/pytorch_model.bin\")\n", "\n", "# ✅ 2. Save config manually\n", "config = AutoConfig.from_pretrained(\"distilbert-base-uncased\", num_labels=3)\n", "config.save_pretrained(save_dir)\n", "\n", "# ✅ 3. Save tokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n", "tokenizer.save_pretrained(save_dir)\n", "\n", "# ✅ 4. Reload and push\n", "model = AutoModelForSequenceClassification.from_pretrained(save_dir, config=config)\n", "model.push_to_hub(\"Sohan2004/TextSentimentClassifierV1\")\n", "tokenizer.push_to_hub(\"Sohan2004/TextSentimentClassifierV1\")\n", "\n", "print(\"✅ Model and tokenizer pushed to Hugging Face at https://huggingface.co/Sohan2004/TextSentimentClassifierV1\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 153, "referenced_widgets": [ "62e56261a3b44ed5af3099ae9b475059", "66db52571efc44fe9a6274dc11bfa748", "05ba0865734e4792b107fab3f627d295", "7f74827cec3f48f29c481cb14bb5aaaf", "077ad9922d5042768c0a7da830526075", "aeb06fc548d345fcb24c3187a87c87fc", "4748bf71ea454b6b8728ad0c9941fc68", "0c4e0dccc35948379bf6a43bc451fd60", "0c4045c9a1da4a529811e847b140125b", "53cb93ea61a8407384b3d8d6413aca2a", "2e11a33bbf944ff4a60a5bf7a5cd8c21", "9aa477a3de0e437c9c1daf8959413455", "4a95b589b33742aeb2237f86d6da74ff", "8fc50fd9d2994d5b96ec3edc4afe389a", "9bfd5c06db1448cbb52bab62247b31cc", "fb396f06e938427bb29b69ad91041d99", "567d00e1e49e49c39ce5a2eff539d1fd", "edb7b5cc2f684e25a518a3884d8ee967", "2bc5b0b1d2df49738be84bf5f951d1ea", "19a1e14ea52b40e4a3712c069e0dbbf7", "2fb1e007f0d4410997380d51bf1b7ba4", "885d0efaee1b474788fd4dca030460d5" ] }, "id": "2-WNYnPq3f62", "outputId": "f005cd18-ffb3-4ad1-9108-0b8cb2cef65b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at multi_task_model_v1 and are newly initialized: ['classifier.bias', 'classifier.weight', 'distilbert.embeddings.LayerNorm.bias', 'distilbert.embeddings.LayerNorm.weight', 'distilbert.embeddings.position_embeddings.weight', 'distilbert.embeddings.word_embeddings.weight', 'distilbert.transformer.layer.0.attention.k_lin.bias', 'distilbert.transformer.layer.0.attention.k_lin.weight', 'distilbert.transformer.layer.0.attention.out_lin.bias', 'distilbert.transformer.layer.0.attention.out_lin.weight', 'distilbert.transformer.layer.0.attention.q_lin.bias', 'distilbert.transformer.layer.0.attention.q_lin.weight', 'distilbert.transformer.layer.0.attention.v_lin.bias', 'distilbert.transformer.layer.0.attention.v_lin.weight', 'distilbert.transformer.layer.0.ffn.lin1.bias', 'distilbert.transformer.layer.0.ffn.lin1.weight', 'distilbert.transformer.layer.0.ffn.lin2.bias', 'distilbert.transformer.layer.0.ffn.lin2.weight', 'distilbert.transformer.layer.0.output_layer_norm.bias', 'distilbert.transformer.layer.0.output_layer_norm.weight', 'distilbert.transformer.layer.0.sa_layer_norm.bias', 'distilbert.transformer.layer.0.sa_layer_norm.weight', 'distilbert.transformer.layer.1.attention.k_lin.bias', 'distilbert.transformer.layer.1.attention.k_lin.weight', 'distilbert.transformer.layer.1.attention.out_lin.bias', 'distilbert.transformer.layer.1.attention.out_lin.weight', 'distilbert.transformer.layer.1.attention.q_lin.bias', 'distilbert.transformer.layer.1.attention.q_lin.weight', 'distilbert.transformer.layer.1.attention.v_lin.bias', 'distilbert.transformer.layer.1.attention.v_lin.weight', 'distilbert.transformer.layer.1.ffn.lin1.bias', 'distilbert.transformer.layer.1.ffn.lin1.weight', 'distilbert.transformer.layer.1.ffn.lin2.bias', 'distilbert.transformer.layer.1.ffn.lin2.weight', 'distilbert.transformer.layer.1.output_layer_norm.bias', 'distilbert.transformer.layer.1.output_layer_norm.weight', 'distilbert.transformer.layer.1.sa_layer_norm.bias', 'distilbert.transformer.layer.1.sa_layer_norm.weight', 'distilbert.transformer.layer.2.attention.k_lin.bias', 'distilbert.transformer.layer.2.attention.k_lin.weight', 'distilbert.transformer.layer.2.attention.out_lin.bias', 'distilbert.transformer.layer.2.attention.out_lin.weight', 'distilbert.transformer.layer.2.attention.q_lin.bias', 'distilbert.transformer.layer.2.attention.q_lin.weight', 'distilbert.transformer.layer.2.attention.v_lin.bias', 'distilbert.transformer.layer.2.attention.v_lin.weight', 'distilbert.transformer.layer.2.ffn.lin1.bias', 'distilbert.transformer.layer.2.ffn.lin1.weight', 'distilbert.transformer.layer.2.ffn.lin2.bias', 'distilbert.transformer.layer.2.ffn.lin2.weight', 'distilbert.transformer.layer.2.output_layer_norm.bias', 'distilbert.transformer.layer.2.output_layer_norm.weight', 'distilbert.transformer.layer.2.sa_layer_norm.bias', 'distilbert.transformer.layer.2.sa_layer_norm.weight', 'distilbert.transformer.layer.3.attention.k_lin.bias', 'distilbert.transformer.layer.3.attention.k_lin.weight', 'distilbert.transformer.layer.3.attention.out_lin.bias', 'distilbert.transformer.layer.3.attention.out_lin.weight', 'distilbert.transformer.layer.3.attention.q_lin.bias', 'distilbert.transformer.layer.3.attention.q_lin.weight', 'distilbert.transformer.layer.3.attention.v_lin.bias', 'distilbert.transformer.layer.3.attention.v_lin.weight', 'distilbert.transformer.layer.3.ffn.lin1.bias', 'distilbert.transformer.layer.3.ffn.lin1.weight', 'distilbert.transformer.layer.3.ffn.lin2.bias', 'distilbert.transformer.layer.3.ffn.lin2.weight', 'distilbert.transformer.layer.3.output_layer_norm.bias', 'distilbert.transformer.layer.3.output_layer_norm.weight', 'distilbert.transformer.layer.3.sa_layer_norm.bias', 'distilbert.transformer.layer.3.sa_layer_norm.weight', 'distilbert.transformer.layer.4.attention.k_lin.bias', 'distilbert.transformer.layer.4.attention.k_lin.weight', 'distilbert.transformer.layer.4.attention.out_lin.bias', 'distilbert.transformer.layer.4.attention.out_lin.weight', 'distilbert.transformer.layer.4.attention.q_lin.bias', 'distilbert.transformer.layer.4.attention.q_lin.weight', 'distilbert.transformer.layer.4.attention.v_lin.bias', 'distilbert.transformer.layer.4.attention.v_lin.weight', 'distilbert.transformer.layer.4.ffn.lin1.bias', 'distilbert.transformer.layer.4.ffn.lin1.weight', 'distilbert.transformer.layer.4.ffn.lin2.bias', 'distilbert.transformer.layer.4.ffn.lin2.weight', 'distilbert.transformer.layer.4.output_layer_norm.bias', 'distilbert.transformer.layer.4.output_layer_norm.weight', 'distilbert.transformer.layer.4.sa_layer_norm.bias', 'distilbert.transformer.layer.4.sa_layer_norm.weight', 'distilbert.transformer.layer.5.attention.k_lin.bias', 'distilbert.transformer.layer.5.attention.k_lin.weight', 'distilbert.transformer.layer.5.attention.out_lin.bias', 'distilbert.transformer.layer.5.attention.out_lin.weight', 'distilbert.transformer.layer.5.attention.q_lin.bias', 'distilbert.transformer.layer.5.attention.q_lin.weight', 'distilbert.transformer.layer.5.attention.v_lin.bias', 'distilbert.transformer.layer.5.attention.v_lin.weight', 'distilbert.transformer.layer.5.ffn.lin1.bias', 'distilbert.transformer.layer.5.ffn.lin1.weight', 'distilbert.transformer.layer.5.ffn.lin2.bias', 'distilbert.transformer.layer.5.ffn.lin2.weight', 'distilbert.transformer.layer.5.output_layer_norm.bias', 'distilbert.transformer.layer.5.output_layer_norm.weight', 'distilbert.transformer.layer.5.sa_layer_norm.bias', 'distilbert.transformer.layer.5.sa_layer_norm.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/268M [00:00