Sentence-BERT实现文本匹配【对比丧失函数】
引言照旧基于Sentence-BERT架构，大概说Bi-Encoder架构，但是本文使用的是参考2中提出的对比丧失函数。
架构
https://img-blog.csdnimg.cn/img_convert/daae0f81b2b919b0bd43258ca840723d.png
如上图，盘算两个句嵌入 u \pmb u u和 v \pmb v v之间的间隔(1-余弦相似度)，然后使用参考2中提出的对比丧失函数作为目标函数：
L = y × 1 2 ( distance ( u , v ) ) 2 + ( 1 − y ) × 1 2 { max ( 0 , m − distance ( u , v ) ) } 2 \mathcal L= y\times \frac{1}{2} (\text{distance}(\pmb u,\pmb v))^2 + (1-y)\times \frac{1}{2} \{ \max(0, m - \text{distance}(\pmb u,\pmb v)) \}^2\\ L=y×21(distance(u,v))2+(1−y)×21{max(0,m−distance(u,v))}2
这里的 y y y是真实标签，相似为1，不相似为0； m m m表示margin(隔断值)，默以为0.5。
这里 m m m的意思是，如果 u \pmb u u和 v \pmb v v不相似( y = 0 y=0 y=0)，那么它们之间的间隔只要充足大，大于即是隔断值0.5就好了。假设间隔为0.6，那么 max ( 0 , 0.5 − 0.6 ) = 0 \max(0,0.5-0.6)=0 max(0,0.5−0.6)=0，如果间隔不敷大( 0.2 0.2 0.2)，那么 max ( 0 , 0.5 − 0.2 ) = 0.3 \max(0,0.5-0.2)=0.3 max(0,0.5−0.2)=0.3，就会产生丧失值。
整个公式的目的是拉近相似的文本对，推远不相似的文本对到一定程度就可以了。实现的时间 max \max max可以用relu来表示。
实现
实现采用雷同Huggingface的情势，每个文件夹下面有一种模型。分为modeling、arguments、trainer等差别的文件。差别的架构放置在差别的文件夹内。
modeling.py:
from dataclasses import dataclass
import torch
from torch import Tensor, nn
from transformers.file_utils import ModelOutput
from transformers import (
AutoModel,
AutoTokenizer,
)
import numpy as np
from tqdm.autonotebook import trange
from typing import Optional
from enum import Enum
import torch.nn.functional as F
# 定义了三种距离函数
# 余弦相似度值越小表示越不相似，1减去它就变成了距离函数，越小(余弦越接近1)表示越相似。
class SiameseDistanceMetric(Enum):
"""The metric for the contrastive loss"""
EUCLIDEAN = lambda x, y: F.pairwise_distance(x, y, p=2)
MANHATTAN = lambda x, y: F.pairwise_distance(x, y, p=1)
COSINE_DISTANCE = lambda x, y: 1 - F.cosine_similarity(x, y)
@dataclass
class BiOutput(ModelOutput):
loss: Optional = None
scores: Optional = None
class SentenceBert(nn.Module):
def __init__(
self,
model_name: str,
trust_remote_code: bool = True,
max_length: int = None,
margin: float = 0.5,
distance_metric=SiameseDistanceMetric.COSINE_DISTANCE,
pooling_mode: str = "mean",
normalize_embeddings: bool = False,
) -> None:
super().__init__()
self.model_name = model_name
self.normalize_embeddings = normalize_embeddings
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.tokenizer = AutoTokenizer.from_pretrained(
model_name, trust_remote_code=trust_remote_code
)
self.model = AutoModel.from_pretrained(
model_name, trust_remote_code=trust_remote_code
).to(self.device)
self.max_length = max_length
self.pooling_mode = pooling_mode
self.distance_metric = distance_metric
self.margin = margin
def sentence_embedding(self, last_hidden_state, attention_mask):
if self.pooling_mode == "mean":
attention_mask = attention_mask.unsqueeze(-1).float()
return torch.sum(last_hidden_state * attention_mask, dim=1) / torch.clamp(
attention_mask.sum(1), min=1e-9
)
else:
# cls
return last_hidden_state[:, 0]
def encode(
self,
sentences: str | list,
batch_size: int = 64,
convert_to_tensor: bool = True,
show_progress_bar: bool = False,
):
if isinstance(sentences, str):
sentences =
all_embeddings = []
for start_index in trange(
0, len(sentences), batch_size, desc="Batches", disable=not show_progress_bar
):
batch = sentences
features = self.tokenizer(
batch,
padding=True,
truncation=True,
return_tensors="pt",
return_attention_mask=True,
max_length=self.max_length,
).to(self.device)
out_features = self.model(**features, return_dict=True)
embeddings = self.sentence_embedding(
out_features.last_hidden_state, features["attention_mask"]
)
if not self.training:
embeddings = embeddings.detach()
if self.normalize_embeddings:
embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
if not convert_to_tensor:
embeddings = embeddings.cpu()
all_embeddings.extend(embeddings)
if convert_to_tensor:
all_embeddings = torch.stack(all_embeddings)
else:
all_embeddings = np.asarray()
return all_embeddings
def compute_loss(self, source_embed, target_embed, labels):
labels = torch.tensor(labels).float().to(self.device)
# 计算距离
distances = self.distance_metric(source_embed, target_embed)
# 实现损失函数
loss = 0.5 * (
labels * distances.pow(2)
+ (1 - labels) * F.relu(self.margin - distances).pow(2)
)
return loss.mean()
def forward(self, source, target, labels) -> BiOutput:
"""
Args:
source :
target :
"""
source_embed = self.encode(source)
target_embed = self.encode(target)
loss = self.compute_loss(source_embed, target_embed, labels)
return BiOutput(loss, None)
def save_pretrained(self, output_dir: str):
state_dict = self.model.state_dict()
state_dict = type(state_dict)(
{k: v.clone().cpu().contiguous() for k, v in state_dict.items()}
)
self.model.save_pretrained(output_dir, state_dict=state_dict)
整个模型的实现放到modeling.py文件中。
arguments.py:
from dataclasses import dataclass, field
from typing import Optional
import os
@dataclass
class ModelArguments:
model_name_or_path: str = field(
metadata={
"help": "Path to pretrained model"
}
)
config_name: Optional = field(
default=None,
metadata={
"help": "Pretrained config name or path if not the same as model_name"
},
)
tokenizer_name: Optional = field(
default=None,
metadata={
"help": "Pretrained tokenizer name or path if not the same as model_name"
},
)
@dataclass
class DataArguments:
train_data_path: str = field(
default=None, metadata={"help": "Path to train corpus"}
)
eval_data_path: str = field(default=None, metadata={"help": "Path to eval corpus"})
max_length: int = field(
default=512,
metadata={
"help": "The maximum total input sequence length after tokenization for input text."
},
)
def __post_init__(self):
if not os.path.exists(self.train_data_path):
raise FileNotFoundError(
f"cannot find file: {self.train_data_path}, please set a true path"
)
if not os.path.exists(self.eval_data_path):
raise FileNotFoundError(
f"cannot find file: {self.eval_data_path}, please set a true path"
)
定义了模型和数据相关参数。
dataset.py:
from torch.utils.data import Dataset
from datasets import Dataset as dt
import pandas as pd
from utils import build_dataframe_from_csv
class PairDataset(Dataset):
def __init__(self, data_path: str) -> None:
df = build_dataframe_from_csv(data_path)
self.dataset = dt.from_pandas(df, split="train")
self.total_len = len(self.dataset)
def __len__(self):
return self.total_len
def __getitem__(self, index) -> dict:
query1 = self.dataset["query1"]
query2 = self.dataset["query2"]
label = self.dataset["label"]
return {"query1": query1, "query2": query2, "label": label}
class PairCollator:
def __call__(self, features) -> dict]:
queries1 = []
queries2 = []
labels = []
for feature in features:
queries1.append(feature["query1"])
queries2.append(feature["query2"])
labels.append(feature["label"])
return {"source": queries1, "target": queries2, "labels": labels}
数据集类思量了LCQMC数据集的格式，即成对的语句和一个数值标签。雷同：
Hello. Hi. 1
Nice to see you. Nice 0
trainer.py:
import torch
from transformers.trainer import Trainer
from typing import Optional
import os
import logging
from modeling import SentenceBert
TRAINING_ARGS_NAME = "training_args.bin"
logger = logging.getLogger(__name__)
class BiTrainer(Trainer):
def compute_loss(self, model: SentenceBert, inputs, return_outputs=False):
outputs = model(**inputs)
loss = outputs.loss
return (loss, outputs) if return_outputs else loss
def _save(self, output_dir: Optional = None, state_dict=None):
# If we are executing this function, we are the process zero, so we don't check for that.
output_dir = output_dir if output_dir is not None else self.args.output_dir
os.makedirs(output_dir, exist_ok=True)
logger.info(f"Saving model checkpoint to {output_dir}")
self.model.save_pretrained(output_dir)
if self.tokenizer is not None:
self.tokenizer.save_pretrained(output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
继承
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