"b:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"outputs": [],
"source": [
"import datasets\n",
"import torch\n",
...
...
@@ -29,7 +20,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
...
...
@@ -38,17 +29,9 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cuda\n"
]
}
],
"outputs": [],
"source": [
"# Use GPU if available\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
" # print(\"Epoch\", e, \"batch:\",i_batch, ', loss =', '{:.6f}'.format(loss))\n",
" print(e+1, epoch_loss / len(sample_batched))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
"- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
b:\Programs\Miniconda\envs\tdde19\lib\site-packages\tqdm\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
c:\Users\Albin\Documents\TDDE19\codebase\Neural graph module\ngm.ipynb Cell 11 in <cell line: 3>()
<a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X13sZmlsZQ%3D%3D?line=0'>1</a> # Train with data loader.
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
<a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X15sZmlsZQ%3D%3D?line=2'>3</a> with torch.no_grad():
