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"cell_type": "code", + "source": [ + "from datasets import Dataset\n", + "\n", + "dataset = Dataset.from_pandas(df[['text', 'label']])\n", + "dataset = dataset.train_test_split(test_size=0.2, seed=42)\n" + ], + "metadata": { + "id": "iJQkAJ4aGm8q" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from transformers import DistilBertTokenizerFast\n", + "\n", + "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')\n", + "\n", + "def preprocess_function(examples):\n", + " return tokenizer(examples[\"text\"], truncation=True, padding=True, max_length=128)\n", + "\n", + "tokenized_dataset = dataset.map(preprocess_function, batched=True)\n", + "tokenized_dataset.set_format(type=\"torch\", columns=['input_ids', 'attention_mask', 'label'])\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 313, + "referenced_widgets": [ + "0e77a4bea8c24f52a74b8c32a596990b", + 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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" + ], + "application/javascript": [ + "\n", + " window._wandbApiKey = new Promise((resolve, reject) => {\n", + " function loadScript(url) {\n", + " return new Promise(function(resolve, reject) {\n", + " let newScript = document.createElement(\"script\");\n", + " newScript.onerror = reject;\n", + " newScript.onload = resolve;\n", + " document.body.appendChild(newScript);\n", + " newScript.src = url;\n", + " });\n", + " }\n", + " loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n", + " const iframe = document.createElement('iframe')\n", + " iframe.style.cssText = 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(Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize?ref=models\n", + "wandb: Paste an API key from your profile and hit enter:" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ··········\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: No netrc file found, creating one.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33msantman99\u001b[0m (\u001b[33msantman99-kivanetech\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. 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" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

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\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'eval_loss': 1.458617091178894, 'eval_accuracy': 0.8888888888888888, 'eval_precision': 1.0, 'eval_recall': 0.8888888888888888, 'eval_runtime': 1.9669, 'eval_samples_per_second': 13.727, 'eval_steps_per_second': 2.034, 'epoch': 5.0}\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login() # This will prompt you to authenticate with your Hugging Face token\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17, + "referenced_widgets": [ + "af21b422f0854aa6b88e017d6dcc193a", + "bbe57664edc84655aebf638ac780219a", + "1bc33cfde96846efa8426ea9deeffe1d", + "697be72dd9df4d148e825dcfa21fe6a2", + "d46e23cc83b9474fbc18c17c2411d875", + "3bc18938c7164864a967044e2dbfbb47", + "c0de169f104b41c18224f2f53168dafe", + "f2b5b17a29ec4f14ad2e5ea5797d8347", + "0ed700bdc4494c459cc52cba1c0e65be", + "ece01126ea744cd48d9e13dc46f5e1ac", + "e936cf7d5a414aabae3b2d92f1b81b3b", + "2fe1eaa0033b48ff9602b409bf2eec90", + "4642cf3ded6d46f096e08719936051a1", + "b2f7a31af1dc4d6f9e7c120e8a897252", + "143a645954e84ad3b07549ff860f9ac7", + "1d42bc542d0c44d494d3cd4fe7d021ff", + "ca02fd68454343c19baa2e8293265b89", + "61d4a2ffdd014349866897aa4b0cc8ca", + "66f1a7390a6f41638d94e1ddb9d9ca79", + "c771a920094044a08f942752847f361c" + ] + }, + "id": "nWW_ulcIIuUW", + "outputId": "a28f7c08-eb4d-4425-b987-8cbaca0d55d3" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
409\u001b[0;31m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 410\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1023\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1024\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m 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self._create_repo(\n\u001b[0m\u001b[1;32m 954\u001b[0m \u001b[0mrepo_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprivate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprivate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepo_url\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morganization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morganization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 955\u001b[0m )\n", + "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36m_create_repo\u001b[0;34m(self, repo_id, private, token, repo_url, organization)\u001b[0m\n\u001b[1;32m 755\u001b[0m \u001b[0mrepo_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"{organization}/{repo_id}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 756\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 757\u001b[0;31m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_repo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprivate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprivate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 758\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 759\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m 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DistilBertForSequenceClassification, DistilBertTokenizerFast\n", + "\n", + "def predict_intent(text):\n", + " inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=128)\n", + " with torch.no_grad():\n", + " logits = model(**inputs).logits\n", + " pred = logits.argmax(dim=1).item()\n", + " return le.inverse_transform([pred])[0]\n", + "\n", + "# Example\n", + "test_input = \"Share offer with Santhosh [context: {domain: HR, topic: onboarding, subject: offer letter}]\"\n", + "print(f\"Predicted Intent: {predict_intent(test_input)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "T1P4RCOnGmuS", + "outputId": "d690a1b8-f890-4953-d3fe-b627b62a1e46" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predicted Intent: generate-offer\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "results = trainer.evaluate()\n", + "print(results)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 109 + }, + "id": "ndyiuYrNGmqo", + "outputId": "cae75ca2-2788-431a-83a2-b04da9c065b9" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " \n", + " \n", + " [4/4 06:21]\n", + "
\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'eval_loss': 1.458617091178894, 'eval_accuracy': 0.8888888888888888, 'eval_precision': 1.0, 'eval_recall': 0.8888888888888888, 'eval_runtime': 1.406, 'eval_samples_per_second': 19.203, 'eval_steps_per_second': 2.845, 'epoch': 5.0}\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Accuracy: 0.8889\n", + "Precision: 1.0\n", + "Recall: 0.8889\n", + "Loss: 1.4586" + ], + "metadata": { + "id": "TYzxYtHZJ7Va" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Get predictions on the evaluation set\n", + "predictions = trainer.predict(tokenized_dataset[\"test\"])\n", + "predicted_labels = np.argmax(predictions.predictions, axis=1)\n", + "true_labels = predictions.label_ids\n", + "\n", + "# Compute the confusion matrix\n", + "cm = confusion_matrix(true_labels, predicted_labels)\n", + "\n", + "# Display the confusion matrix\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=le.classes_)\n", + "fig, ax = plt.subplots(figsize=(8, 8))\n", + "disp.plot(cmap=plt.cm.Blues, ax=ax)\n", + "\n", + "# Get unique labels present in both true and predicted labels\n", + "unique_labels = np.unique(np.concatenate((true_labels, predicted_labels)))\n", + "\n", + "# Set ticks and labels based on unique labels\n", + "ax.set_xticks(np.arange(len(unique_labels)))\n", + "ax.set_yticks(np.arange(len(unique_labels)))\n", + "ax.set_xticklabels(le.inverse_transform(unique_labels), rotation=90)\n", + "ax.set_yticklabels(le.inverse_transform(unique_labels))\n", + "\n", + "plt.title(\"Confusion Matrix\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "iD8_EcdXGmna", + "outputId": "74f49fc7-f58d-4cc5-fb51-0ccd0ce71d66" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [] + }, + "metadata": {} + }, + { + "output_type": "error", + "ename": "ValueError", + "evalue": "The number of FixedLocator locations (9), usually from a call to set_ticks, does not match the number of labels (12).", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipython-input-3760369866.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mdisp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConfusionMatrixDisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplay_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mdisp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBlues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;31m# Get unique labels present in both true and predicted labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_plot/confusion_matrix.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, include_values, cmap, xticks_rotation, values_format, ax, colorbar, im_kw, text_kw)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcolorbar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m ax.set(\n\u001b[0m\u001b[1;32m 186\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mArtist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 147\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"set\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__qualname__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"{cls.__qualname__}.set\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mset\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 1239\u001b[0m \u001b[0;31m# Artist._update_set_signature_and_docstring() at the end of the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1240\u001b[0m \u001b[0;31m# module.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1241\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_internal_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1243\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m_update_props\u001b[0;34m(self, props, errfmt)\u001b[0m\n\u001b[1;32m 1207\u001b[0m \u001b[0merrfmt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1208\u001b[0m name=k)\n\u001b[0;32m-> 1209\u001b[0;31m \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1210\u001b[0m \u001b[0;32mif\u001b[0m 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number of labels (12)." + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "MHLtobASGmko" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "_WXeafz2Gmh0" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "K_aIIi2PGme5" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "f45VW3IJGmb3" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "I7wVw6J9GmZQ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5db1cb2c" + }, + "source": [ + "## Model Validation Report\n", + "\n", + "This document summarizes the trained model and its performance on the validation dataset.\n", + "\n", + "**Model Type:** DistilBERT for Sequence Classification\n", + "\n", + "**Training Data:** The model was trained on a dataset containing queries and their corresponding contexts, labeled with intents.\n", + "\n", + "**Validation Data:** A portion of the original dataset (20%) was used for validation.\n", + "\n", + "**Evaluation Metrics:**\n", + "\n", + "The following metrics were used to evaluate the model's performance on the validation set:\n", + "\n", + "* **Accuracy:** The proportion of correctly classified instances.\n", + "* **Precision (weighted):** The ability of the model to return only relevant instances, averaged across all classes and weighted by the number of true instances in each class.\n", + "* **Recall (weighted):** The ability of the model to find all the relevant instances, averaged across all classes and weighted by the number of true instances in each class.\n", + "* **Loss:** A measure of how well the model is performing, with lower values indicating better performance.\n", + "\n", + "**Validation Results:**\n", + "\n", + "Based on the evaluation on the validation dataset, the model achieved the following metrics:\n", + "\n", + "* **Accuracy:** 0.8889\n", + "* **Precision (weighted):** 1\n", + "* **Recall (weighted):** 0.8889\n", + "* **Loss:** 1.4586\n", + "\n", + "**Summary:**\n", + "\n", + "The model demonstrated strong performance on the validation dataset, achieving high accuracy, precision, and recall. This suggests that the model has learned to effectively classify the intents based on the provided queries and contexts.\n", + "\n", + "**Further Steps:**\n", + "\n", + "* Consider evaluating the model on a separate, unseen test dataset to get a more robust estimate of its performance.\n", + "* Explore potential areas for improvement, such as hyperparameter tuning or using a larger dataset.\n", + "* Deploy the model for making predictions on new data." + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "uOzzFY16KVFr" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file