Copy a token from your Hugging Face\ntokens page and paste it below. Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
"
]
},
"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": [
""
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"text/html": [
"\n",
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" \n",
" \n",
" [4/4 00:01]\n",
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"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='
\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 \u001b[0mxticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_classes\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 187\u001b[0m \u001b[0myticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_classes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\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[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 \u001b[0;34m@\u001b[0m\u001b[0mcontextlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontextmanager\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;36m_internal_update\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m 1231\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mlack\u001b[0m \u001b[0mof\u001b[0m \u001b[0mprenormalization\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mto\u001b[0m \u001b[0mmaintain\u001b[0m \u001b[0mbackcompatibility\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1232\u001b[0m \"\"\"\n\u001b[0;32m-> 1233\u001b[0;31m return self._update_props(\n\u001b[0m\u001b[1;32m 1234\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"{cls.__name__}.set() got an unexpected keyword argument \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1235\u001b[0m \"{prop_name!r}\")\n",
"\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 \u001b[0mret\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1211\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpchanged\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mget_method\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[0margs\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 75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\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/axis.py\u001b[0m in \u001b[0;36mset_ticklabels\u001b[0;34m(self, labels, minor, fontdict, **kwargs)\u001b[0m\n\u001b[1;32m 2115\u001b[0m \u001b[0;31m# remove all tick labels, so only error for > 0 labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2116\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2117\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 2118\u001b[0m \u001b[0;34m\"The number of FixedLocator locations\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2119\u001b[0m \u001b[0;34mf\" ({len(locator.locs)}), usually from a call to\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: The number of FixedLocator locations (9), usually from a call to set_ticks, does not match the 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": []
}
]
}