Text-Similarity

Overview

• Dataset: 中文/English 语料, ☞ 点这里
• Paper: 相关论文详解, ☞ 点这里
• The implemented method is as follows:：
  • TF-IDF
  • BM25
  • LSH
  • SIF/uSIF
  • FastText
  • RNN Base (Siamese RNN, Stack RNN)
  • CNN Base (Fast Text, Text CNN, Char CNN, VDCNN)
  • Bert Base
  • Albert
  • NEZHA
  • RoBERTa
  • SimCSE
  • Poly-Encoder
  • ColBERT
  • RE2（Simple-Effective-Text-Matching）

Usages

1：examples目录下有不同模型对应的 preprocess/train/evalute代码，可自行修改
2：如下示例从examples中引入actuator方法，准备好对应的模型配置文件即可执行
3：examples目录下的inference.py为训练好的模型推理代码

TF-IDF

# Example
# Sklearn version
from examples.run_tfidf_sklearn import actuator
actuator("./corpus/chinese/breeno/train.tsv", query1="12 23 4160 276", query2="29 23 169 1495")

# Custom version
from examples.run_tfidf import actuator
actuator("./corpus/chinese/breeno/train.tsv", query1="12 23 4160 276", query2="29 23 169 1495")

# 工具调用
from sim.tf_idf import TFIdf

tokens_list = ["这是 一个 什么 样 的 工具", "..."]
query = ["非常 好用 的 工具"]

tf_idf = TFIdf(tokens_list, split=" ")
print(tf_idf.get_score(query, 0))  # score
print(tf_idf.get_score_list(query, 10))  # [(index, score), ...]
print(tf_idf.weight())  # list or numpy array

BM25

# Example
from examples.run_bm25 import actuator
actuator("./corpus/chinese/breeno/train.tsv", query1="12 23 4160 276", query2="29 23 169 1495")

# 工具调用
from sim.bm25 import BM25

tokens_list = ["这是 一个 什么 样 的 工具", "..."]
query = ["非常 好用 的 工具"]

bm25 = BM25(tokens_list, split=" ")
print(bm25.get_score(query, 0))  # score
print(bm25.get_score_list(query, 10))  # [(index, score), ...]
print(bm25.weight())  # list or numpy array

LSH

from sim.lsh import E2LSH
from sim.lsh import MinHash

e2lsh = E2LSH()
min_hash = MinHash()

candidates = [[3.6216, 8.6661, -2.8073, -0.44699, 0], ...]
query = [-2.7769, -5.6967, 5.9179, 0.37671, 1]
print(e2lsh.search(candidates, query))  # index in candidates
print(min_hash.search(candidates, query))  # index in candidates

SIF

• A Simple But Tough-To-Beat Baseline For Sentence Embeddings
• Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline

sentences = [["token1", "token2", "..."], ...]
vector = [[[1, 1, 1], [2, 2, 2], [...]], ...]
from sim.sif_usif import SIF
from sim.sif_usif import uSIF

sif = SIF(n_components=5, component_type="svd")
sif.fit(tokens_list=sentences, vector_list=vector)

usif = uSIF(n_components=5, n=1, component_type="svd")
usif.fit(tokens_list=sentences, vector_list=vector)

FastText

• Bag of Tricks for Efficient Text Classification

# TensorFlow version
from examples.tensorflow.run_fast_text import actuator
actuator(execute_type="train", model_type="bert", model_dir="./data/chinese_wwm_L-12_H-768_A-12")

# Pytorch version
from examples.pytorch.run_fast_text import actuator
actuator(execute_type="train", model_type="bert", model_dir="./data/chinese_wwm_pytorch")

RNN Base

• Siamese Recurrent Architectures for Learning Sentence Similarity
• Learning Text Similarity with Siamese Recurrent Networks

# TensorFlow version
from examples.tensorflow.run_siamese_rnn import actuator
actuator("./data/config/siamse_rnn.json", execute_type="train")

# Pytorch version
from examples.pytorch.run_siamese_rnn import actuator
actuator("./data/config/siamse_rnn.json", execute_type="train")

CNN Base

• Convolutional Neural Networks for Sentence Classification
• Character-Aware Neural Language Models
• Highway Networks
• Very Deep Convolutional Networks for Text Classification

# TensorFlow version
from examples.tensorflow.run_cnn_base import actuator
actuator(execute_type="train", model_type="bert", model_dir="./data/chinese_wwm_L-12_H-768_A-12")

# Pytorch version
from examples.pytorch.run_cnn_base import actuator
actuator(execute_type="train", model_type="bert", model_dir="./data/chinese_wwm_pytorch")

Bert Base

• Attention Is All You Need

# TensorFlow version
from examples.tensorflow.run_basic_bert import actuator
actuator(model_dir="./data/chinese_wwm_L-12_H-768_A-12", execute_type="train")

# Pytorch version
from examples.pytorch.run_basic_bert import actuator
actuator(model_dir="./data/chinese_wwm_pytorch", execute_type="train")

Albert

• ALBERT: A Lite BERT For Self-superpised Learning Of Language Representations

# TensorFlow version
from examples.tensorflow.run_albert import actuator
actuator(model_dir="./data/albert_small_zh_google", execute_type="train")

# Pytorch version
from examples.pytorch.run_albert import actuator
actuator(model_dir="./data/albert_chinese_small", execute_type="train")

NEZHA

• NEZHA: Neural Contextualized Representation For Chinese Language Understanding

# TensorFlow version
from examples.tensorflow.run_nezha import actuator
actuator(model_dir="./data/NEZHA-Base-WWM", execute_type="train")

# Pytorch version
from examples.pytorch.run_nezha import actuator
actuator(model_dir="./data/nezha-base-wwm", execute_type="train")

RoBERTa

• RoBERTa: A Robustly Optimized BERT Pretraining Approach

# TensorFlow version
from examples.tensorflow.run_basic_bert import actuator
actuator(model_dir="./data/chinese_roberta_L-6_H-384_A-12", execute_type="train")

# Pytorch version
from examples.pytorch.run_basic_bert import actuator
actuator(model_dir="./data/chinese-roberta-wwm-ext", execute_type="train")

SimCSE

• SimCSE: Simple Contrastive Learning of Sentence Embeddings

# TensorFlow version
from examples.tensorflow.run_simcse import actuator
actuator(model_dir="./data/chinese_wwm_L-12_H-768_A-12", execute_type="train", model_type="bert")

# Pytorch version
from examples.pytorch.run_simcse import actuator
actuator(model_dir="./data/chinese_wwm_pytorch", execute_type="train", model_type="bert")

Poly-Encoder

• Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring

# TensorFlow version
from examples.tensorflow.run_poly_encoder import actuator
actuator(model_dir="./data/chinese_wwm_L-12_H-768_A-12", execute_type="train", model_type="bert")

# Pytorch version
from examples.pytorch.run_poly_encoder import actuator
actuator(model_dir="./data/chinese_wwm_pytorch", execute_type="train", model_type="bert")

ColBERT

• ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

# TensorFlow version
from examples.tensorflow.run_colbert import actuator
actuator(model_dir="./data/chinese_wwm_L-12_H-768_A-12", execute_type="train", model_type="bert")

# Pytorch version
from examples.pytorch.run_colbert import actuator
actuator(model_dir="./data/chinese_wwm_pytorch", execute_type="train", model_type="bert")

RE2

• Simple and Effective Text Matching with Richer Alignment Features

# TensorFlow version
from examples.tensorflow.run_re2 import actuator
actuator("./data/config/re2.json", execute_type="train")

# Pytorch version
from examples.pytorch.run_re2 import actuator
actuator("./data/config/re2.json", execute_type="train")

Cite

@misc{text-similarity,
  title={text-similarity},
  author={Bocong Deng},
  year={2021},
  howpublished={\url{https://github.com/DengBoCong/text-similarity}},
}

Reference

• bert4keras
• albert_zh
• HuggingFace
• Self-Attention with Relative Position Representations