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import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from transformers import AutoModel, AutoConfig
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModel
import pandas as pd
import os
AUTH_TOKEN = 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
tokenizer = AutoTokenizer.from_pretrained('nguyenvulebinh/vi-mrc-base',
use_auth_token=AUTH_TOKEN)
pad_token_id = tokenizer.pad_token_id
class PairwiseModel(nn.Module):
def __init__(self, model_name, max_length=384, batch_size=16, device="cpu"):
super(PairwiseModel, self).__init__()
self.max_length = max_length
self.batch_size = batch_size
self.device = device
self.model = ORTModel.from_pretrained(model_name, use_auth_token=AUTH_TOKEN, from_transformers=True)
self.model.to(self.device)
self.model.eval()
self.config = AutoConfig.from_pretrained(model_name, use_auth_token=AUTH_TOKEN)
self.fc = nn.Linear(768, 1).to(self.device)
def forward(self, ids, masks):
out = self.model(input_ids=ids,
attention_mask=masks,
output_hidden_states=False).last_hidden_state
out = out[:, 0]
outputs = self.fc(out)
return outputs
def stage1_ranking(self, question, texts):
tmp = pd.DataFrame()
tmp["text"] = [" ".join(x.split()) for x in texts]
tmp["question"] = question
valid_dataset = SiameseDatasetStage1(tmp, tokenizer, self.max_length, is_test=True)
valid_loader = DataLoader(valid_dataset, batch_size=self.batch_size, collate_fn=collate_fn,
num_workers=0, shuffle=False, pin_memory=True)
preds = []
with torch.no_grad():
bar = enumerate(valid_loader)
for step, data in bar:
ids = data["ids"].to(self.device)
masks = data["masks"].to(self.device)
preds.append(torch.sigmoid(self(ids, masks)).view(-1))
preds = torch.concat(preds)
return preds.cpu().numpy()
def stage2_ranking(self, question, answer, titles, texts):
tmp = pd.DataFrame()
tmp["candidate"] = texts
tmp["question"] = question
tmp["answer"] = answer
tmp["title"] = titles
valid_dataset = SiameseDatasetStage2(tmp, tokenizer, self.max_length, is_test=True)
valid_loader = DataLoader(valid_dataset, batch_size=self.batch_size, collate_fn=collate_fn,
num_workers=0, shuffle=False, pin_memory=True)
preds = []
with torch.no_grad():
bar = enumerate(valid_loader)
for step, data in bar:
ids = data["ids"].to(self.device)
masks = data["masks"].to(self.device)
preds.append(torch.sigmoid(self(ids, masks)).view(-1))
preds = torch.concat(preds)
return preds.cpu().numpy()
class SiameseDatasetStage1(Dataset):
def __init__(self, df, tokenizer, max_length, is_test=False):
self.df = df
self.max_length = max_length
self.tokenizer = tokenizer
self.is_test = is_test
self.content1 = tokenizer.batch_encode_plus(list(df.question.values), max_length=max_length, truncation=True)[
"input_ids"]
self.content2 = tokenizer.batch_encode_plus(list(df.text.values), max_length=max_length, truncation=True)[
"input_ids"]
if not self.is_test:
self.targets = self.df.label
def __len__(self):
return len(self.df)
def __getitem__(self, index):
return {
'ids1': torch.tensor(self.content1[index], dtype=torch.long),
'ids2': torch.tensor(self.content2[index][1:], dtype=torch.long),
'target': torch.tensor(0) if self.is_test else torch.tensor(self.targets[index], dtype=torch.float)
}
class SiameseDatasetStage2(Dataset):
def __init__(self, df, tokenizer, max_length, is_test=False):
self.df = df
self.max_length = max_length
self.tokenizer = tokenizer
self.is_test = is_test
self.df["content1"] = self.df.apply(lambda row: row.question + f" {tokenizer.sep_token} " + row.answer, axis=1)
self.df["content2"] = self.df.apply(lambda row: row.title + f" {tokenizer.sep_token} " + row.candidate, axis=1)
self.content1 = tokenizer.batch_encode_plus(list(df.content1.values), max_length=max_length, truncation=True)[
"input_ids"]
self.content2 = tokenizer.batch_encode_plus(list(df.content2.values), max_length=max_length, truncation=True)[
"input_ids"]
if not self.is_test:
self.targets = self.df.label
def __len__(self):
return len(self.df)
def __getitem__(self, index):
return {
'ids1': torch.tensor(self.content1[index], dtype=torch.long),
'ids2': torch.tensor(self.content2[index][1:], dtype=torch.long),
'target': torch.tensor(0) if self.is_test else torch.tensor(self.targets[index], dtype=torch.float)
}
def collate_fn(batch):
ids = [torch.cat([x["ids1"], x["ids2"]]) for x in batch]
targets = [x["target"] for x in batch]
max_len = np.max([len(x) for x in ids])
masks = []
for i in range(len(ids)):
if len(ids[i]) < max_len:
ids[i] = torch.cat((ids[i], torch.tensor([pad_token_id, ] * (max_len - len(ids[i])), dtype=torch.long)))
masks.append(ids[i] != pad_token_id)
# print(tokenizer.decode(ids[0]))
outputs = {
"ids": torch.vstack(ids),
"masks": torch.vstack(masks),
"target": torch.vstack(targets).view(-1)
}
return outputs
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