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{
"cells": [
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2024-12-16T01:56:57.350322Z",
"start_time": "2024-12-16T01:56:56.339076Z"
}
},
"source": [
"import pandas as pd\n",
"from datasets import Dataset\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
"from torch.utils.data import DataLoader\n",
"import torch\n",
"import evaluate\n",
"from tqdm import tqdm\n",
"from datasets import load_dataset\n",
"\n",
"# 1. Load the model and tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(\"CIS5190ml/bert4\")\n",
"model = AutoModelForSequenceClassification.from_pretrained(\"CIS5190ml/bert4\")\n",
"\n",
"# 2. Load the dataset\n",
"import pandas as pd \n",
"\n",
"ds = load_dataset(\"CIS5190ml/NewData\")\n"
],
"outputs": [],
"execution_count": 44
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-16T01:56:22.105429Z",
"start_time": "2024-12-16T01:56:22.089923Z"
}
},
"cell_type": "code",
"source": [
"#choose test dataset\n",
"ds = ds[\"test\"]"
],
"id": "fd95d0347ad1665a",
"outputs": [],
"execution_count": 41
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-16T01:56:24.245992Z",
"start_time": "2024-12-16T01:56:23.609377Z"
}
},
"cell_type": "code",
"source": [
"# Preprocessing function\n",
"def preprocess_function(examples):\n",
" return tokenizer(examples[\"title\"], truncation=True, padding=\"max_length\")\n",
"\n",
"encoded_ds = ds.map(preprocess_function, batched=True)\n",
"\n",
"# Keep only the necessary columns (input_ids, attention_mask, labels)\n",
"desired_cols = [\"input_ids\", \"attention_mask\", \"labels\"]\n",
"encoded_ds = encoded_ds.remove_columns([col for col in encoded_ds.column_names if col not in desired_cols])\n",
"encoded_ds.set_format(\"torch\")\n",
"\n",
"# Create DataLoader\n",
"test_loader = DataLoader(encoded_ds, batch_size=8)\n",
"\n",
"# Load accuracy metric\n",
"accuracy = evaluate.load(\"accuracy\")\n",
"\n",
"# Set device\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"model.to(device)\n"
],
"id": "dfefbe70a4ff8696",
"outputs": [
{
"data": {
"text/plain": [
"BertForSequenceClassification(\n",
" (bert): BertModel(\n",
" (embeddings): BertEmbeddings(\n",
" (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
" (position_embeddings): Embedding(512, 768)\n",
" (token_type_embeddings): Embedding(2, 768)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (encoder): BertEncoder(\n",
" (layer): ModuleList(\n",
" (0-11): 12 x BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSdpaSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" (intermediate_act_fn): GELUActivation()\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (pooler): BertPooler(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (activation): Tanh()\n",
" )\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
")"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 42
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-16T01:56:35.444373Z",
"start_time": "2024-12-16T01:56:26.083442Z"
}
},
"cell_type": "code",
"source": [
"# Evaluate\n",
"model.eval()\n",
"for batch in tqdm(test_loader, desc=\"Evaluating\"):\n",
" input_ids = batch[\"input_ids\"].to(device)\n",
" attention_mask = batch[\"attention_mask\"].to(device)\n",
" labels = batch[\"labels\"].to(device)\n",
"\n",
" with torch.no_grad():\n",
" outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
" preds = torch.argmax(outputs.logits, dim=-1)\n",
" accuracy.add_batch(predictions=preds, references=labels)\n",
"\n",
"final_accuracy = accuracy.compute()\n",
"print(\"Accuracy:\", final_accuracy[\"accuracy\"])"
],
"id": "c6e4fd03bd73664f",
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Evaluating: 100%|██████████| 95/95 [00:09<00:00, 10.21it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.9182058047493403\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"execution_count": 43
}
],
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