Spaces:
Build error
Build error
File size: 5,742 Bytes
c5d2283 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
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
import pandas as pd
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="cuda:0"):
super(PairwiseModel, self).__init__()
self.max_length = max_length
self.batch_size = batch_size
self.device = device
self.model = AutoModel.from_pretrained(model_name, use_auth_token=AUTH_TOKEN)
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
|