Spaces:
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Sleeping
update milestone-3 notebook
Browse files
CS670_milestone_3_AyeThuzar.ipynb
CHANGED
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@@ -922,18 +922,18 @@
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| 922 |
"\n",
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| 923 |
"from transformers import pipeline, Trainer, TrainingArguments\n",
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| 924 |
"\n",
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-
"\n",
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| 926 |
"import torch\n",
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| 927 |
"import torch.nn.functional as F\n",
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| 928 |
"\n",
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| 929 |
"from transformers import logging\n",
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"\n",
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-
"logging.set_verbosity_warning()"
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],
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"metadata": {
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"id": "FxZeFFTlFvz1"
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},
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-
"execution_count":
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"outputs": []
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},
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{
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@@ -1163,7 +1163,7 @@
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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-
"height":
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},
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| 1168 |
"id": "jDBvcgmP5Puh",
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"outputId": "f4f73693-11f7-4918-a86d-2912e863b151"
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@@ -1193,7 +1193,7 @@
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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-
"height":
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},
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"id": "sBhSPSV-5XKS",
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"outputId": "0057e051-3b36-4705-8636-19e7850fa0a9"
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@@ -4118,7 +4118,7 @@
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"id": "h7bzRvkItdir",
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"outputId": "7495ec10-0ee5-4f1c-ffe9-50f4afe2cb83"
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},
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| 4121 |
-
"execution_count":
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"outputs": [
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{
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"output_type": "stream",
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@@ -4253,7 +4253,469 @@
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"batch_average_accuray: 0.5\n",
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"batch_average_accuray: 0.5\n",
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"batch_average_accuray: 0.625\n",
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-
"batch_average_accuray: 0.75\n"
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]
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}
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]
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@@ -4277,7 +4739,7 @@
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"metadata": {
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"id": "KefqatP-YDSC"
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},
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-
"execution_count":
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"outputs": []
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},
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{
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"metadata": {
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"id": "Km8eScKJl4VP"
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},
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-
"execution_count":
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
|
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| 922 |
"\n",
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| 923 |
"from transformers import pipeline, Trainer, TrainingArguments\n",
|
| 924 |
"\n",
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| 925 |
+
"import numpy as np\n",
|
| 926 |
"import torch\n",
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| 927 |
"import torch.nn.functional as F\n",
|
| 928 |
"\n",
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| 929 |
"from transformers import logging\n",
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| 930 |
"\n",
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| 931 |
+
"logging.set_verbosity_warning()\n"
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| 932 |
],
|
| 933 |
"metadata": {
|
| 934 |
"id": "FxZeFFTlFvz1"
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| 935 |
},
|
| 936 |
+
"execution_count": 92,
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| 937 |
"outputs": []
|
| 938 |
},
|
| 939 |
{
|
|
|
|
| 1163 |
"metadata": {
|
| 1164 |
"colab": {
|
| 1165 |
"base_uri": "https://localhost:8080/",
|
| 1166 |
+
"height": 157
|
| 1167 |
},
|
| 1168 |
"id": "jDBvcgmP5Puh",
|
| 1169 |
"outputId": "f4f73693-11f7-4918-a86d-2912e863b151"
|
|
|
|
| 1193 |
"metadata": {
|
| 1194 |
"colab": {
|
| 1195 |
"base_uri": "https://localhost:8080/",
|
| 1196 |
+
"height": 105
|
| 1197 |
},
|
| 1198 |
"id": "sBhSPSV-5XKS",
|
| 1199 |
"outputId": "0057e051-3b36-4705-8636-19e7850fa0a9"
|
|
|
|
| 4118 |
"id": "h7bzRvkItdir",
|
| 4119 |
"outputId": "7495ec10-0ee5-4f1c-ffe9-50f4afe2cb83"
|
| 4120 |
},
|
| 4121 |
+
"execution_count": 88,
|
| 4122 |
"outputs": [
|
| 4123 |
{
|
| 4124 |
"output_type": "stream",
|
|
|
|
| 4253 |
"batch_average_accuray: 0.5\n",
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| 4254 |
"batch_average_accuray: 0.5\n",
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| 4255 |
"batch_average_accuray: 0.625\n",
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| 4256 |
+
"batch_average_accuray: 0.75\n",
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| 4257 |
+
"batch_average_accuray: 0.375\n",
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| 4258 |
+
"batch_average_accuray: 0.5\n",
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"batch_average_accuray: 0.25\n",
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"batch_average_accuray: 0.5625\n",
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"batch_average_accuray: 0.4375\n",
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"batch_average_accuray: 0.75\n",
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"batch_average_accuray: 0.375\n",
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"batch_average_accuray: 0.5625\n",
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"batch_average_accuray: 0.8125\n",
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"batch_average_accuray: 0.5625\n",
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"batch_average_accuray: 0.5625\n",
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"batch_average_accuray: 0.5\n",
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"batch_average_accuray: 0.625\n",
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"batch_average_accuray: 0.6875\n",
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"batch_average_accuray: 0.4375\n",
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"batch_average_accuray: 0.625\n",
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"batch_average_accuray: 0.625\n",
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"batch_average_accuray: 0.5625\n",
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"batch_average_accuray: 0.5\n",
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"batch_average_accuray: 0.5\n",
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"batch_average_accuray: 0.75\n",
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"batch_average_accuray: 0.5625\n",
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"batch_average_accuray: 0.5625\n",
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"batch_average_accuray: 0.5625\n",
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"batch_average_accuray: 0.375\n",
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"batch_average_accuray: 0.5625\n",
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+
"batch_average_accuray: 0.625\n",
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"batch_average_accuray: 0.375\n",
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"batch_average_accuray: 0.6875\n",
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"batch_average_accuray: 0.5\n",
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"batch_average_accuray: 0.625\n",
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"batch_average_accuray: 0.4375\n",
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"batch_average_accuray: 0.375\n",
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"batch_average_accuray: 0.375\n",
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"batch_average_accuray: 0.5625\n",
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"batch_average_accuray: 0.5625\n",
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"batch_average_accuray: 0.375\n",
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| 4294 |
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"batch_average_accuray: 0.4375\n",
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"batch_average_accuray: 0.75\n",
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| 4296 |
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"batch_average_accuray: 0.4375\n",
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"batch_average_accuray: 0.4375\n",
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"batch_average_accuray: 0.5625\n",
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"batch_average_accuray: 0.4375\n",
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| 4586 |
+
"batch_average_accuray: 0.8125\n",
|
| 4587 |
+
"batch_average_accuray: 0.8125\n",
|
| 4588 |
+
"batch_average_accuray: 0.5625\n",
|
| 4589 |
+
"batch_average_accuray: 0.5625\n",
|
| 4590 |
+
"batch_average_accuray: 0.5625\n",
|
| 4591 |
+
"batch_average_accuray: 0.6875\n",
|
| 4592 |
+
"batch_average_accuray: 0.375\n",
|
| 4593 |
+
"batch_average_accuray: 0.5625\n",
|
| 4594 |
+
"batch_average_accuray: 0.5625\n",
|
| 4595 |
+
"batch_average_accuray: 0.375\n",
|
| 4596 |
+
"batch_average_accuray: 0.625\n",
|
| 4597 |
+
"batch_average_accuray: 0.4375\n",
|
| 4598 |
+
"batch_average_accuray: 0.375\n",
|
| 4599 |
+
"batch_average_accuray: 0.5625\n",
|
| 4600 |
+
"batch_average_accuray: 0.6875\n",
|
| 4601 |
+
"batch_average_accuray: 0.625\n",
|
| 4602 |
+
"batch_average_accuray: 0.375\n",
|
| 4603 |
+
"batch_average_accuray: 0.625\n",
|
| 4604 |
+
"batch_average_accuray: 0.5625\n",
|
| 4605 |
+
"batch_average_accuray: 0.5\n",
|
| 4606 |
+
"batch_average_accuray: 0.625\n",
|
| 4607 |
+
"batch_average_accuray: 0.4375\n",
|
| 4608 |
+
"batch_average_accuray: 0.5\n",
|
| 4609 |
+
"batch_average_accuray: 0.5625\n",
|
| 4610 |
+
"batch_average_accuray: 0.5\n",
|
| 4611 |
+
"batch_average_accuray: 0.4375\n",
|
| 4612 |
+
"batch_average_accuray: 0.4375\n",
|
| 4613 |
+
"batch_average_accuray: 0.3125\n",
|
| 4614 |
+
"batch_average_accuray: 0.75\n",
|
| 4615 |
+
"batch_average_accuray: 0.75\n",
|
| 4616 |
+
"batch_average_accuray: 0.625\n",
|
| 4617 |
+
"batch_average_accuray: 0.5\n",
|
| 4618 |
+
"batch_average_accuray: 0.25\n",
|
| 4619 |
+
"batch_average_accuray: 0.5625\n",
|
| 4620 |
+
"batch_average_accuray: 0.75\n",
|
| 4621 |
+
"batch_average_accuray: 0.625\n",
|
| 4622 |
+
"batch_average_accuray: 0.375\n",
|
| 4623 |
+
"batch_average_accuray: 0.625\n",
|
| 4624 |
+
"batch_average_accuray: 0.625\n",
|
| 4625 |
+
"batch_average_accuray: 0.5625\n",
|
| 4626 |
+
"batch_average_accuray: 0.625\n",
|
| 4627 |
+
"batch_average_accuray: 0.625\n",
|
| 4628 |
+
"batch_average_accuray: 0.4375\n",
|
| 4629 |
+
"batch_average_accuray: 0.5\n",
|
| 4630 |
+
"batch_average_accuray: 0.75\n",
|
| 4631 |
+
"batch_average_accuray: 0.4375\n",
|
| 4632 |
+
"batch_average_accuray: 0.625\n",
|
| 4633 |
+
"batch_average_accuray: 0.375\n",
|
| 4634 |
+
"batch_average_accuray: 0.625\n",
|
| 4635 |
+
"batch_average_accuray: 0.625\n",
|
| 4636 |
+
"batch_average_accuray: 0.4375\n",
|
| 4637 |
+
"batch_average_accuray: 0.5625\n",
|
| 4638 |
+
"batch_average_accuray: 0.3125\n",
|
| 4639 |
+
"batch_average_accuray: 0.5625\n",
|
| 4640 |
+
"batch_average_accuray: 0.75\n",
|
| 4641 |
+
"batch_average_accuray: 0.6875\n",
|
| 4642 |
+
"batch_average_accuray: 0.375\n",
|
| 4643 |
+
"batch_average_accuray: 0.5625\n",
|
| 4644 |
+
"batch_average_accuray: 0.6875\n",
|
| 4645 |
+
"batch_average_accuray: 0.625\n",
|
| 4646 |
+
"batch_average_accuray: 0.625\n",
|
| 4647 |
+
"batch_average_accuray: 0.5625\n",
|
| 4648 |
+
"batch_average_accuray: 0.375\n",
|
| 4649 |
+
"batch_average_accuray: 0.5\n",
|
| 4650 |
+
"batch_average_accuray: 0.5\n",
|
| 4651 |
+
"batch_average_accuray: 0.5625\n",
|
| 4652 |
+
"batch_average_accuray: 0.5625\n",
|
| 4653 |
+
"batch_average_accuray: 0.5625\n",
|
| 4654 |
+
"batch_average_accuray: 0.4375\n",
|
| 4655 |
+
"batch_average_accuray: 0.5625\n",
|
| 4656 |
+
"batch_average_accuray: 0.5\n",
|
| 4657 |
+
"batch_average_accuray: 0.6875\n",
|
| 4658 |
+
"batch_average_accuray: 0.375\n",
|
| 4659 |
+
"batch_average_accuray: 0.4375\n",
|
| 4660 |
+
"batch_average_accuray: 0.5625\n",
|
| 4661 |
+
"batch_average_accuray: 0.4375\n",
|
| 4662 |
+
"batch_average_accuray: 0.6875\n",
|
| 4663 |
+
"batch_average_accuray: 0.5\n",
|
| 4664 |
+
"batch_average_accuray: 0.5625\n",
|
| 4665 |
+
"batch_average_accuray: 0.875\n",
|
| 4666 |
+
"batch_average_accuray: 0.75\n",
|
| 4667 |
+
"batch_average_accuray: 0.25\n",
|
| 4668 |
+
"batch_average_accuray: 0.5\n",
|
| 4669 |
+
"batch_average_accuray: 0.625\n",
|
| 4670 |
+
"batch_average_accuray: 0.375\n",
|
| 4671 |
+
"batch_average_accuray: 0.5625\n",
|
| 4672 |
+
"batch_average_accuray: 0.5625\n",
|
| 4673 |
+
"batch_average_accuray: 0.5625\n",
|
| 4674 |
+
"batch_average_accuray: 0.4375\n",
|
| 4675 |
+
"batch_average_accuray: 0.5625\n",
|
| 4676 |
+
"batch_average_accuray: 0.625\n",
|
| 4677 |
+
"batch_average_accuray: 0.4375\n",
|
| 4678 |
+
"batch_average_accuray: 0.5625\n",
|
| 4679 |
+
"batch_average_accuray: 0.375\n",
|
| 4680 |
+
"batch_average_accuray: 0.625\n",
|
| 4681 |
+
"batch_average_accuray: 0.4375\n",
|
| 4682 |
+
"batch_average_accuray: 0.625\n",
|
| 4683 |
+
"batch_average_accuray: 0.6875\n",
|
| 4684 |
+
"batch_average_accuray: 0.375\n",
|
| 4685 |
+
"batch_average_accuray: 0.6875\n",
|
| 4686 |
+
"batch_average_accuray: 0.5625\n",
|
| 4687 |
+
"batch_average_accuray: 0.6875\n",
|
| 4688 |
+
"batch_average_accuray: 0.6875\n",
|
| 4689 |
+
"batch_average_accuray: 0.4375\n",
|
| 4690 |
+
"batch_average_accuray: 0.5\n",
|
| 4691 |
+
"batch_average_accuray: 0.625\n",
|
| 4692 |
+
"batch_average_accuray: 0.5625\n",
|
| 4693 |
+
"batch_average_accuray: 0.5625\n",
|
| 4694 |
+
"batch_average_accuray: 0.5625\n",
|
| 4695 |
+
"batch_average_accuray: 0.125\n"
|
| 4696 |
+
]
|
| 4697 |
+
}
|
| 4698 |
+
]
|
| 4699 |
+
},
|
| 4700 |
+
{
|
| 4701 |
+
"cell_type": "code",
|
| 4702 |
+
"source": [
|
| 4703 |
+
"print(f\"average accuracy: {np.mean(accuracy)}\")"
|
| 4704 |
+
],
|
| 4705 |
+
"metadata": {
|
| 4706 |
+
"colab": {
|
| 4707 |
+
"base_uri": "https://localhost:8080/"
|
| 4708 |
+
},
|
| 4709 |
+
"id": "-Ow1N7MnEc98",
|
| 4710 |
+
"outputId": "01fddc67-f273-4659-ecfa-fd89e6c78935"
|
| 4711 |
+
},
|
| 4712 |
+
"execution_count": 93,
|
| 4713 |
+
"outputs": [
|
| 4714 |
+
{
|
| 4715 |
+
"output_type": "stream",
|
| 4716 |
+
"name": "stdout",
|
| 4717 |
+
"text": [
|
| 4718 |
+
"average accuracy: 0.5421792618629174\n"
|
| 4719 |
]
|
| 4720 |
}
|
| 4721 |
]
|
|
|
|
| 4739 |
"metadata": {
|
| 4740 |
"id": "KefqatP-YDSC"
|
| 4741 |
},
|
| 4742 |
+
"execution_count": 94,
|
| 4743 |
"outputs": []
|
| 4744 |
},
|
| 4745 |
{
|
|
|
|
| 4751 |
"metadata": {
|
| 4752 |
"id": "Km8eScKJl4VP"
|
| 4753 |
},
|
| 4754 |
+
"execution_count": 95,
|
| 4755 |
"outputs": []
|
| 4756 |
},
|
| 4757 |
+
{
|
| 4758 |
+
"cell_type": "markdown",
|
| 4759 |
+
"source": [
|
| 4760 |
+
"## Testing the saved model"
|
| 4761 |
+
],
|
| 4762 |
+
"metadata": {
|
| 4763 |
+
"id": "dCZQwr_ZE-cB"
|
| 4764 |
+
}
|
| 4765 |
+
},
|
| 4766 |
+
{
|
| 4767 |
+
"cell_type": "code",
|
| 4768 |
+
"source": [
|
| 4769 |
+
"with torch.no_grad():\n",
|
| 4770 |
+
" outputs = model_saved(batch['input_ids']).logits\n",
|
| 4771 |
+
" print(outputs)\n",
|
| 4772 |
+
" predictions = F.softmax(outputs, dim = 1)\n",
|
| 4773 |
+
" print(predictions)\n",
|
| 4774 |
+
" labels = torch.argmax(predictions, dim = 1)\n",
|
| 4775 |
+
" print(labels)\n",
|
| 4776 |
+
" print(\"--------\")\n",
|
| 4777 |
+
" print(batch['decision'])\n",
|
| 4778 |
+
" print(\"--------\")\n",
|
| 4779 |
+
" res = labels == batch['decision']\n",
|
| 4780 |
+
" print(res)\n",
|
| 4781 |
+
" print(res.sum() / batch_size)"
|
| 4782 |
+
],
|
| 4783 |
+
"metadata": {
|
| 4784 |
+
"colab": {
|
| 4785 |
+
"base_uri": "https://localhost:8080/"
|
| 4786 |
+
},
|
| 4787 |
+
"id": "u_iN3BSHFB27",
|
| 4788 |
+
"outputId": "d73153a7-f156-413c-9e3c-6f2930e8905d"
|
| 4789 |
+
},
|
| 4790 |
+
"execution_count": 96,
|
| 4791 |
+
"outputs": [
|
| 4792 |
+
{
|
| 4793 |
+
"output_type": "stream",
|
| 4794 |
+
"name": "stdout",
|
| 4795 |
+
"text": [
|
| 4796 |
+
"tensor([[-0.2934, 0.9680, 4.0130, -8.2634, -8.1291, -8.6447],\n",
|
| 4797 |
+
" [ 0.5176, 3.2941, 1.8334, -8.3832, -8.6352, -8.5553],\n",
|
| 4798 |
+
" [-0.4728, 0.9731, 4.1658, -8.1353, -7.9516, -8.5336],\n",
|
| 4799 |
+
" [-0.4363, 1.1413, 4.1972, -8.3214, -8.2106, -8.7486],\n",
|
| 4800 |
+
" [-0.3831, 1.4167, 4.0593, -8.5625, -8.5613, -9.0239],\n",
|
| 4801 |
+
" [ 0.3174, 3.2739, 2.2290, -8.6113, -8.8512, -8.8537]])\n",
|
| 4802 |
+
"tensor([[1.2706e-02, 4.4856e-02, 9.4243e-01, 4.3923e-06, 5.0237e-06, 2.9996e-06],\n",
|
| 4803 |
+
" [4.8101e-02, 7.7258e-01, 1.7930e-01, 6.5550e-06, 5.0946e-06, 5.5186e-06],\n",
|
| 4804 |
+
" [9.2039e-03, 3.9077e-02, 9.5171e-01, 4.3269e-06, 5.1996e-06, 2.9054e-06],\n",
|
| 4805 |
+
" [9.1980e-03, 4.4548e-02, 9.4624e-01, 3.4612e-06, 3.8667e-06, 2.2579e-06],\n",
|
| 4806 |
+
" [1.0866e-02, 6.5728e-02, 9.2340e-01, 3.0465e-06, 3.0504e-06, 1.9206e-06],\n",
|
| 4807 |
+
" [3.7043e-02, 7.1237e-01, 2.5057e-01, 4.9094e-06, 3.8624e-06, 3.8528e-06]])\n",
|
| 4808 |
+
"tensor([2, 1, 2, 2, 2, 1])\n",
|
| 4809 |
+
"--------\n",
|
| 4810 |
+
"tensor([2, 2, 0, 1, 1, 1])\n",
|
| 4811 |
+
"--------\n",
|
| 4812 |
+
"tensor([ True, False, False, False, False, True])\n",
|
| 4813 |
+
"tensor(0.1250)\n"
|
| 4814 |
+
]
|
| 4815 |
+
}
|
| 4816 |
+
]
|
| 4817 |
+
},
|
| 4818 |
{
|
| 4819 |
"cell_type": "markdown",
|
| 4820 |
"source": [
|