56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
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import pandas as pd
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from transformers import BertTokenizer, AdamW, AutoModelForSequenceClassification
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import torch
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import matplotlib.pyplot as plt
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from torch.utils.data import TensorDataset, DataLoader, RandomSampler
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import torch.nn as nn
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from sklearn.utils.class_weight import compute_class_weight
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import numpy as np
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from model import BERT_Arch
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from sklearn.metrics import classification_report
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from sklearn.metrics import accuracy_score
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path = 'saved_weights.pt'
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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device = torch.device("cuda")
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bert = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
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model = BERT_Arch(bert)
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model.load_state_dict(torch.load(path))
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model.to(device)
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test_data = pd.read_csv("dev-0/in.tsv", sep="\t")
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test_data.columns = ["text", "d"]
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test_target = pd.read_csv("dev-0/expected.tsv", sep="\t")
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tokens_train = tokenizer.batch_encode_plus(
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test_data["text"],
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max_length = 25,
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padding='max_length',
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truncation=True
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)
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test_seq = torch.tensor(tokens_train['input_ids'])
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test_mask = torch.tensor(tokens_train['attention_mask'])
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#define a batch size
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batch_size = 32
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# wrap tensors
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test_data = TensorDataset(test_seq, test_mask)
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# sampler for sampling the data during training
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test_sampler = RandomSampler(test_data)
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# dataLoader for train set
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test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
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with torch.no_grad():
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preds = model(test_seq.to(device), test_mask.to(device))
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preds = preds.detach().cpu().numpy()
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preds = np.argmax(preds, axis = 1)
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print(classification_report(test_target['0'], preds))
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print(accuracy_score(test_target['0'], preds))
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