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first trainging
2022-06-22 00:46:19 +02:00
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"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting transformers\n",
" Downloading transformers-4.20.1-py3-none-any.whl (4.4 MB)\n",
"\u001b[K |████████████████████████████████| 4.4 MB 7.9 MB/s \n",
"\u001b[?25hCollecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
" Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
"\u001b[K |████████████████████████████████| 6.6 MB 47.5 MB/s \n",
"\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
"Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.11.4)\n",
"Collecting huggingface-hub<1.0,>=0.1.0\n",
" Downloading huggingface_hub-0.8.1-py3-none-any.whl (101 kB)\n",
"\u001b[K |████████████████████████████████| 101 kB 12.9 MB/s \n",
"\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.7.1)\n",
"Collecting pyyaml>=5.1\n",
" Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
"\u001b[K |████████████████████████████████| 596 kB 67.7 MB/s \n",
"\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.0)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.6.15)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
"Installing collected packages: pyyaml, tokenizers, huggingface-hub, transformers\n",
" Attempting uninstall: pyyaml\n",
" Found existing installation: PyYAML 3.13\n",
" Uninstalling PyYAML-3.13:\n",
" Successfully uninstalled PyYAML-3.13\n",
"Successfully installed huggingface-hub-0.8.1 pyyaml-6.0 tokenizers-0.12.1 transformers-4.20.1\n"
]
}
],
"source": [
"!pip install transformers\n",
"import re\n",
"import torch\n",
"import torch.nn as nn\n",
"import pandas as pd\n",
"import numpy as np\n",
"from transformers import pipeline, set_seed\n",
"from transformers import RobertaTokenizer, RobertaModel\n",
"from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
"from transformers import AutoModel, BertTokenizerFast"
]
},
{
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"def load_data(path):\n",
" #return pd.read_csv(path, sep='\\t', header=None)\n",
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" return f.readlines()"
],
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" f.write(f'{line}\\n')\n",
" print(f\"Data written {path}/out.tsv\")"
],
"metadata": {
"id": "crkUIjgLWiiO"
},
"execution_count": 3,
"outputs": []
},
{
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"source": [
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"device"
],
"metadata": {
"id": "mR2UfBRYdlMW",
"colab": {
"base_uri": "https://localhost:8080/"
},
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"output_type": "execute_result",
"data": {
"text/plain": [
"device(type='cuda')"
]
},
"metadata": {},
"execution_count": 4
}
]
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"source": [
"import pandas as pd\n",
"train_input = pd.read_csv(\"/content/drive/MyDrive/paranormal-or-skeptic/train/in.tsv\", sep = '\\t', names = ['text', 'label'], header=None, nrows=10000)\n",
"# train['text'] = train['text'].apply(lambda x: tokenizer(x, return_tensors='pt'))\n",
"train_input['label'] = pd.read_csv(\"/content/drive/MyDrive/paranormal-or-skeptic/train/expected.tsv\", header=None, nrows=10000)\n",
"train_input"
],
"metadata": {
"id": "pTjGxxAu6b-v",
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"height": 424
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"outputId": "61f5911c-4dd6-4cde-dbeb-43b58fde3f7e"
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"data": {
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" text label\n",
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"1 It's supposedly aluminum, barium, and strontiu... 0\n",
"2 Nobel prizes don't make you rich. 0\n",
"3 I came for the article, I stayed for the doctor. 0\n",
"4 you resorted to insults AND got owned directly... 0\n",
"... ... ...\n",
"9995 &gt;a very very very very very liberal college... 0\n",
"9996 To be fair, most of Newton's writings were on ... 0\n",
"9997 your elementary idea is brilliant 0\n",
"9998 I know! I was like ...Simon Pegg?? 1\n",
"9999 You seem to have missed the purpose of my post... 0\n",
"\n",
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"dev = pd.read_csv(\"/content/drive/MyDrive/paranormal-or-skeptic/dev-0/in.tsv\", sep = '\\t', names = ['text', 'label'], header=None)\n",
"# test['text'] = test['text'].apply(lambda x: tokenizer(x, return_tensors='pt'))\n",
"dev['label'] = pd.read_csv(\"/content/drive/MyDrive/paranormal-or-skeptic/dev-0/expected.tsv\", header=None)\n",
"dev"
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"# test['text'] = test['text'].apply(lambda x: tokenizer(x, return_tensors='pt'))\n",
"# test['label'] = pd.read_csv(\"/content/drive/MyDrive/paranormal-or-skeptic/test-A/expected.tsv\", header=None)\n",
"test = test.drop(['label'], axis=1)\n",
"test"
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"# import BERT-base pretrained model\n",
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"\n",
"# Load the BERT tokenizer\n",
"tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')"
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{
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{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight']\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"
]
},
{
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"# get length of all the messages in the train set\n",
"seq_len = [len(i.split()) for i in train_input['text']]\n",
"\n",
"pd.Series(seq_len).hist(bins = 30)"
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},
"id": "hUH8nba5EBpU",
"outputId": "7bec6eb1-627d-4d20-9a9b-ea41ffe9a08c"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3bd4213750>"
]
},
"metadata": {},
"execution_count": 9
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"max_seq_len = 100"
],
"metadata": {
"id": "JB3VM8WuEQ4O"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# tokenize and encode sequences in the sets\n",
"tokens_train = tokenizer.batch_encode_plus(\n",
" train_input['text'].tolist(), \n",
" max_length = max_seq_len,\n",
" pad_to_max_length=True,\n",
" truncation=True,\n",
" return_token_type_ids=False\n",
")\n",
"\n",
"tokens_dev = tokenizer.batch_encode_plus(\n",
" dev['text'].tolist(), \n",
" max_length = max_seq_len,\n",
" pad_to_max_length=True,\n",
" truncation=True,\n",
" return_token_type_ids=False\n",
")\n",
"\n",
"tokens_test = tokenizer.batch_encode_plus(\n",
" test['text'].tolist(), \n",
" max_length = max_seq_len,\n",
" pad_to_max_length=True,\n",
" truncation=True,\n",
" return_token_type_ids=False\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "921Msq7VEUH4",
"outputId": "0a92a3da-eedc-44e8-b6b9-9d4f28958e72"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2307: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
" FutureWarning,\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# for train set\n",
"train_seq = torch.tensor(tokens_train['input_ids'])\n",
"train_mask = torch.tensor(tokens_train['attention_mask'])\n",
"train_y = torch.tensor(train_input['label'].tolist())\n",
"\n",
"# for validation set\n",
"val_seq = torch.tensor(tokens_dev['input_ids'])\n",
"val_mask = torch.tensor(tokens_dev['attention_mask'])\n",
"val_y = torch.tensor(dev['label'].tolist())\n",
"\n",
"# for test set\n",
"test_seq = torch.tensor(tokens_test['input_ids'])\n",
"test_mask = torch.tensor(tokens_test['attention_mask'])\n",
"test_y = torch.tensor([])"
],
"metadata": {
"id": "_6hI_UZdFtEn"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
"\n",
"#define a batch size\n",
"batch_size = 16\n",
"\n",
"# wrap tensors\n",
"train_data = TensorDataset(train_seq, train_mask, train_y)\n",
"\n",
"# sampler for sampling the data during training\n",
"train_sampler = RandomSampler(train_data)\n",
"\n",
"# dataLoader for train set\n",
"train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n",
"\n",
"# wrap tensors\n",
"val_data = TensorDataset(val_seq, val_mask, val_y)\n",
"\n",
"# sampler for sampling the data during training\n",
"val_sampler = SequentialSampler(val_data)\n",
"\n",
"# dataLoader for validation set\n",
"val_dataloader = DataLoader(val_data, sampler = val_sampler, batch_size=batch_size)\n"
],
"metadata": {
"id": "KSNdUlPLHiw0"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# freeze all the parameters\n",
"for param in bert.parameters():\n",
" param.requires_grad = False"
],
"metadata": {
"id": "4rU2lm6MHqin"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class BERT_Arch(nn.Module):\n",
"\n",
" def __init__(self, bert):\n",
" \n",
" super(BERT_Arch, self).__init__()\n",
"\n",
" self.bert = bert \n",
" \n",
" # dropout layer\n",
" self.dropout = nn.Dropout(0.1)\n",
" \n",
" # relu activation function\n",
" self.relu = nn.ReLU()\n",
"\n",
" # dense layer 1\n",
" self.fc1 = nn.Linear(768,512)\n",
" \n",
" # dense layer 2 (Output layer)\n",
" self.fc2 = nn.Linear(512,2)\n",
"\n",
" #softmax activation function\n",
" self.softmax = nn.LogSoftmax(dim=1)\n",
"\n",
" #define the forward pass\n",
" def forward(self, sent_id, mask):\n",
"\n",
" #pass the inputs to the model \n",
" _, cls_hs = self.bert(sent_id, attention_mask=mask, return_dict=False)\n",
" \n",
" x = self.fc1(cls_hs)\n",
"\n",
" x = self.relu(x)\n",
"\n",
" x = self.dropout(x)\n",
"\n",
" # output layer\n",
" x = self.fc2(x)\n",
" \n",
" # apply softmax activation\n",
" x = self.softmax(x)\n",
"\n",
" return x"
],
"metadata": {
"id": "YFohRUDdHryu"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# pass the pre-trained BERT to our define architecture\n",
"model = BERT_Arch(bert)\n",
"\n",
"# push the model to GPU\n",
"model = model.to(device)"
],
"metadata": {
"id": "rDPdkzf0HtTH"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# optimizer from hugging face transformers\n",
"from transformers import AdamW\n",
"\n",
"# define the optimizer\n",
"optimizer = AdamW(model.parameters(), lr = 1e-3)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "k-Rj8zh-HyNv",
"outputId": "f15878b3-3a36-4966-a2d0-58ab8268b3d1"
},
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" FutureWarning,\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.utils.class_weight import compute_class_weight\n",
"\n",
"#compute the class weights\n",
"class_wts = compute_class_weight('balanced', classes = np.unique(train_input['label']), y = train_input['label'])\n",
"\n",
"print(class_wts)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mNAZd44jH3rf",
"outputId": "bdb3aed8-31f4-4653-c6c5-43864f546ae1"
},
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[0.7732756 1.41482739]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# convert class weights to tensor\n",
"weights= torch.tensor(class_wts,dtype=torch.float)\n",
"weights = weights.to(device)\n",
"\n",
"# loss function\n",
"cross_entropy = nn.NLLLoss(weight=weights) \n",
"\n",
"# number of training epochs\n",
"epochs = 3"
],
"metadata": {
"id": "JAjsPk2QH46H"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# function to train the model\n",
"def train():\n",
" \n",
" model.train()\n",
"\n",
" total_loss, total_accuracy = 0, 0\n",
" \n",
" # empty list to save model predictions\n",
" total_preds=[]\n",
" \n",
" # iterate over batches\n",
" for step,batch in enumerate(train_dataloader):\n",
" \n",
" # progress update after every 50 batches.\n",
" if step % 50 == 0 and not step == 0:\n",
" print(' Batch {:>5,} of {:>5,}.'.format(step, len(train_dataloader)))\n",
"\n",
" # push the batch to gpu\n",
" batch = [r.to(device) for r in batch]\n",
" \n",
" sent_id, mask, labels = batch\n",
"\n",
" # clear previously calculated gradients \n",
" model.zero_grad() \n",
"\n",
" # get model predictions for the current batch\n",
" preds = model(sent_id, mask)\n",
"\n",
" # compute the loss between actual and predicted values\n",
" loss = cross_entropy(preds, labels)\n",
"\n",
" # add on to the total loss\n",
" total_loss = total_loss + loss.item()\n",
"\n",
" # backward pass to calculate the gradients\n",
" loss.backward()\n",
"\n",
" # clip the the gradients to 1.0. It helps in preventing the exploding gradient problem\n",
" torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
"\n",
" # update parameters\n",
" optimizer.step()\n",
"\n",
" # model predictions are stored on GPU. So, push it to CPU\n",
" preds=preds.detach().cpu().numpy()\n",
"\n",
" # append the model predictions\n",
" total_preds.append(preds)\n",
"\n",
" # compute the training loss of the epoch\n",
" avg_loss = total_loss / len(train_dataloader)\n",
" \n",
" # predictions are in the form of (no. of batches, size of batch, no. of classes).\n",
" # reshape the predictions in form of (number of samples, no. of classes)\n",
" total_preds = np.concatenate(total_preds, axis=0)\n",
"\n",
" #returns the loss and predictions\n",
" return avg_loss, total_preds"
],
"metadata": {
"id": "VFzCbFmmIhhd"
},
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import time\n",
"# function for evaluating the model\n",
"def evaluate():\n",
" \n",
" print(\"\\nEvaluating...\")\n",
" \n",
" # deactivate dropout layers\n",
" model.eval()\n",
"\n",
" total_loss, total_accuracy = 0, 0\n",
" \n",
" # empty list to save the model predictions\n",
" total_preds = []\n",
"\n",
" # iterate over batches\n",
" for step,batch in enumerate(val_dataloader):\n",
" \n",
" # Progress update every 50 batches.\n",
" if step % 50 == 0 and not step == 0:\n",
" \n",
" # Calculate elapsed time in minutes.\n",
" #elapsed = format_time(time.time() - t0)\n",
" \n",
" # Report progress.\n",
" print(' Batch {:>5,} of {:>5,}.'.format(step, len(val_dataloader)))\n",
"\n",
" # push the batch to gpu\n",
" batch = [t.to(device) for t in batch]\n",
"\n",
" sent_id, mask, labels = batch\n",
"\n",
" # deactivate autograd\n",
" with torch.no_grad():\n",
" \n",
" # model predictions\n",
" preds = model(sent_id, mask)\n",
"\n",
" # compute the validation loss between actual and predicted values\n",
" loss = cross_entropy(preds,labels)\n",
"\n",
" total_loss = total_loss + loss.item()\n",
"\n",
" preds = preds.detach().cpu().numpy()\n",
"\n",
" total_preds.append(preds)\n",
"\n",
" # compute the validation loss of the epoch\n",
" avg_loss = total_loss / len(val_dataloader) \n",
"\n",
" # reshape the predictions in form of (number of samples, no. of classes)\n",
" total_preds = np.concatenate(total_preds, axis=0)\n",
"\n",
" return avg_loss, total_preds"
],
"metadata": {
"id": "lnVTBlprIjE_"
},
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# set initial loss to infinite\n",
"best_valid_loss = float('inf')\n",
"\n",
"# empty lists to store training and validation loss of each epoch\n",
"train_losses=[]\n",
"valid_losses=[]\n",
"\n",
"#for each epoch\n",
"for epoch in range(epochs):\n",
" \n",
" print('\\n Epoch {:} / {:}'.format(epoch + 1, epochs))\n",
" \n",
" #train model\n",
" train_loss, _ = train()\n",
" \n",
" #evaluate model\n",
" valid_loss, _ = evaluate()\n",
" \n",
" #save the best model\n",
" if valid_loss < best_valid_loss:\n",
" best_valid_loss = valid_loss\n",
" torch.save(model.state_dict(), 'saved_weights_10k.pt')\n",
" \n",
" # append training and validation loss\n",
" train_losses.append(train_loss)\n",
" valid_losses.append(valid_loss)\n",
" \n",
" print(f'\\nTraining Loss: {train_loss:.3f}')\n",
" print(f'Validation Loss: {valid_loss:.3f}')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "S7R_IWk1Ilk_",
"outputId": "c4ee3b7c-ecae-4baf-91b9-6cc6f77dc481"
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" Epoch 1 / 3\n",
" Batch 50 of 625.\n",
" Batch 100 of 625.\n",
" Batch 150 of 625.\n",
" Batch 200 of 625.\n",
" Batch 250 of 625.\n",
" Batch 300 of 625.\n",
" Batch 350 of 625.\n",
" Batch 400 of 625.\n",
" Batch 450 of 625.\n",
" Batch 500 of 625.\n",
" Batch 550 of 625.\n",
" Batch 600 of 625.\n",
"\n",
"Evaluating...\n",
" Batch 50 of 330.\n",
" Batch 100 of 330.\n",
" Batch 150 of 330.\n",
" Batch 200 of 330.\n",
" Batch 250 of 330.\n",
" Batch 300 of 330.\n",
"\n",
"Training Loss: 0.663\n",
"Validation Loss: 0.617\n",
"\n",
" Epoch 2 / 3\n",
" Batch 50 of 625.\n",
" Batch 100 of 625.\n",
" Batch 150 of 625.\n",
" Batch 200 of 625.\n",
" Batch 250 of 625.\n",
" Batch 300 of 625.\n",
" Batch 350 of 625.\n",
" Batch 400 of 625.\n",
" Batch 450 of 625.\n",
" Batch 500 of 625.\n",
" Batch 550 of 625.\n",
" Batch 600 of 625.\n",
"\n",
"Evaluating...\n",
" Batch 50 of 330.\n",
" Batch 100 of 330.\n",
" Batch 150 of 330.\n",
" Batch 200 of 330.\n",
" Batch 250 of 330.\n",
" Batch 300 of 330.\n",
"\n",
"Training Loss: 0.608\n",
"Validation Loss: 0.586\n",
"\n",
" Epoch 3 / 3\n",
" Batch 50 of 625.\n",
" Batch 100 of 625.\n",
" Batch 150 of 625.\n",
" Batch 200 of 625.\n",
" Batch 250 of 625.\n",
" Batch 300 of 625.\n",
" Batch 350 of 625.\n",
" Batch 400 of 625.\n",
" Batch 450 of 625.\n",
" Batch 500 of 625.\n",
" Batch 550 of 625.\n",
" Batch 600 of 625.\n",
"\n",
"Evaluating...\n",
" Batch 50 of 330.\n",
" Batch 100 of 330.\n",
" Batch 150 of 330.\n",
" Batch 200 of 330.\n",
" Batch 250 of 330.\n",
" Batch 300 of 330.\n",
"\n",
"Training Loss: 0.585\n",
"Validation Loss: 0.598\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#load weights of best model\n",
"path = './saved_weights_10k.pt'\n",
"model.load_state_dict(torch.load(path))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OMNZqvqzIozP",
"outputId": "533d1d3e-0004-4df3-95b2-b1b57c65f55c"
},
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"metadata": {},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"source": [
"# get predictions for test data\n",
"with torch.no_grad():\n",
" preds = model(test_seq[:1000].to(device), test_mask[:1000].to(device))\n",
" preds = preds.detach().cpu().numpy()"
],
"metadata": {
"id": "8GuauaHOs53n"
},
"execution_count": 34,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def predict(model, seq, mask):\n",
" result = []\n",
" \n",
" with torch.no_grad():\n",
" for i in range(0, len(seq), 16):\n",
" s = seq[i:i+16]\n",
" m = mask[i:i+16]\n",
" preds = model(s[i].to(device), m[i].to(device))\n",
" preds = preds.detach().cpu().numpy()\n",
" preds = np.argmax(preds, axis = 1)\n",
" result.extend(preds)\n",
"\n",
" return result"
],
"metadata": {
"id": "zm0g6fChWCvq"
},
"execution_count": 43,
"outputs": []
},
{
"cell_type": "code",
"source": [
"result = []\n",
"for i in range(int(len(test_seq)/5)):\n",
" x0 = i*int(len(test_seq)/5)\n",
" x1 = (i+1)*int(len(test_seq)/5)\n",
" preds = model(test_seq[x0:x1].to(device), test_mask[x0:x1].to(device))\n",
" preds = preds.detach().cpu().numpy()\n",
" preds = np.argmax(preds, axis = 1)\n",
" result.extend(preds)\n",
"result"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "slZZ0gkTXxLm",
"outputId": "014e4da6-5d53-4307-f3ac-d10870b6ef58"
},
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
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" 0,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 0,\n",
" 1,\n",
" 1,\n",
" 1,\n",
" 0,\n",
" 1,\n",
" 1,\n",
" 1,\n",
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" 0,\n",
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" 1,\n",
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" 0,\n",
" 0,\n",
" 0,\n",
" 1,\n",
" ...]"
]
},
"metadata": {},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"source": [
"write_res(result, './test_out.tsv')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lqDJ2A29ZwZ3",
"outputId": "d22ec4f9-fbde-45d8-ea1d-970f6ec57fba"
},
"execution_count": 53,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Data written ./test_out.tsv/out.tsv\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"result_dev = []\n",
"for i in range(int(len(val_seq)/5)):\n",
" x0 = i*int(len(val_seq)/5)\n",
" x1 = (i+1)*int(len(val_seq)/5)\n",
" preds = model(val_seq[x0:x1].to(device), val_mask[x0:x1].to(device))\n",
" preds = preds.detach().cpu().numpy()\n",
" preds = np.argmax(preds, axis = 1)\n",
" result_dev.extend(preds)\n",
"write_res(result_dev, './dev_out.tsv')\n",
"len(result_dev)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ktm90qCvapDH",
"outputId": "077fa023-890a-4a29-e881-e6a95366e7ad"
},
"execution_count": 55,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Data written ./dev_out.tsv/out.tsv\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"5272"
]
},
"metadata": {},
"execution_count": 55
}
]
}
]
}
Reference in New Issue Copy Permalink