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update demo and readme.

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shibing624 2022-03-12 14:00:51 +08:00
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commit e5a588d38f
6 changed files with 145 additions and 143 deletions
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54
.github/workflows/macos.yml vendored
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@ -1,54 +0,0 @@
on:
workflow_dispatch: # Manually running a workflow
pull_request:
branches:
- master
name: macOS build
jobs:
test-macOS:
runs-on: ${{ matrix.config.os }}
name: ${{ matrix.config.os }} (${{ matrix.config.py }})
strategy:
fail-fast: false
matrix:
config:
- { os: macOS-latest, py: "3.9" }
env:
SDKROOT: /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk
steps:
- name: CHECKOUT CODE
uses: actions/checkout@v2
- name: Cache pip
uses: actions/cache@v2
if: startsWith(runner.os, 'macOS')
with:
path: ~/Library/Caches/pip
key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
restore-keys: |
${{ runner.os }}-pip-
- name: Cache huggingface models
uses: actions/cache@v2
with:
path: ~/.cache/huggingface
key: ${{ runner.os }}-huggingface-
- name: Cache text2vec models
uses: actions/cache@v2
with:
path: ~/.text2vec
key: ${{ runner.os }}-text2vec-
- name: SETUP PYTHON
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.config.py }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install Cython
pip install torch
pip install -r requirements.txt
pip install .
- name: Install from pypi
run: |
pip uninstall -y similarities
pip install -U similarities # install from pypi
python -c "import similarities; print(similarities.__version__)"

14
.github/workflows/ubuntu.yml vendored
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@ -7,8 +7,8 @@ jobs:
strategy:
fail-fast: false
matrix:
#python-version: [ 3.7, 3.8, 3.9 ]
python-version: [ 3.9 ]
python-version: [ 3.7, 3.8, 3.9 ]
# python-version: [ 3.9 ]
steps:
- uses: actions/checkout@v2
- name: Cache pip
@ -38,6 +38,11 @@ jobs:
python -m pip install --upgrade pip
pip install Cython
pip install torch
- name: Install from pypi
run: |
pip install -U similarities
python -c "import similarities; print(similarities.__version__)"
pip uninstall -y similarities
- name: Install dependencies
run: |
pip install -r requirements.txt
@ -45,8 +50,3 @@ jobs:
- name: PKG-TEST
run: |
python -m unittest discover tests/
- name: Install from pypi
run: |
pip uninstall -y similarities
pip install -U similarities
python -c "import similarities; print(similarities.__version__)"

53
.github/workflows/windows.yml vendored
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@ -1,53 +0,0 @@
on:
workflow_dispatch: # Manually running a workflow
pull_request:
branches:
- master
name: Windows build
jobs:
test-windows:
runs-on: windows-latest
steps:
- uses: actions/checkout@v2
- name: Cache pip
uses: actions/cache@v2
if: startsWith(runner.os, 'Windows')
with:
path: ~\AppData\Local\pip\Cache
key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
restore-keys: |
${{ runner.os }}-pip-
- name: Cache huggingface models
uses: actions/cache@v2
with:
path: ~/.cache/huggingface
key: ${{ runner.os }}-huggingface-
- name: Cache text2vec models
uses: actions/cache@v2
with:
path: ~/.text2vec
key: ${{ runner.os }}-text2vec-
- name: Install miniconda
uses: conda-incubator/setup-miniconda@v2
with:
auto-activate-base: true
python-version: 3.9
- name: Install torch
run: |
python -m pip install --upgrade pip
pip install torch
- name: Test torch installation
run: |
python -c "import sys; print(sys.version)"
python -c "import torch; print(torch.__version__)"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install Cython
pip install -r requirements.txt
pip install .
- name: Install from pypi
run: |
pip uninstall -y similarities
pip install -U similarities
python -c "import similarities; print(similarities.__version__)"

151
README.md
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@ -64,9 +64,9 @@ python3 setup.py install
```python
from similarities import Similarity
m = Similarity("shibing624/text2vec-base-chinese")
m = Similarity()
r = m.similarity('如何更换花呗绑定银行卡', '花呗更改绑定银行卡')
print(f"similarity score: {r:.4f}") # similarity score: 0.8551
print(f"similarity score: {float(r)}") # similarity score: 0.855146050453186
```
> 余弦值`score`范围是[-1, 1]，值越大越相似。
@ -94,11 +94,12 @@ corpus = [
'中央情报局局长访问以色列叙利亚会谈',
'人在巴基斯坦基地的炸弹袭击中丧生',
]
model = Similarity("shibing624/text2vec-base-chinese")
model = Similarity(model_name_or_path="shibing624/text2vec-base-chinese")
print(model)
similarity_score = model.similarity(sentences[0], sentences[1])
print(f"{sentences[0]} vs {sentences[1]}, score: {float(similarity_score):.4f}")
print('-' * 50 + '\n')
# 2.Compute similarity between two list
similarity_scores = model.similarity(sentences, corpus)
print(similarity_scores.numpy())
@ -106,12 +107,16 @@ for i in range(len(sentences)):
for j in range(len(corpus)):
print(f"{sentences[i]} vs {corpus[j]}, score: {similarity_scores.numpy()[i][j]:.4f}")
print('-' * 50 + '\n')
# 3.Semantic Search
model.add_corpus(corpus)
q = '如何更换花呗绑定银行卡'
print("query:", q)
for i in model.most_similar(q, topn=5):
print('\t', i)
res = model.most_similar(queries=sentences, topn=3)
print(res)
for q_id, c in res.items():
print('query:', sentences[q_id])
print("search top 3:")
for corpus_id, s in c.items():
print(f'\t{model.corpus[corpus_id]}: {s:.4f}')
```
output:
@ -134,11 +139,10 @@ output:
花呗更改绑定银行卡 vs 人在巴基斯坦基地的炸弹袭击中丧生, score: 0.1279
query: 如何更换花呗绑定银行卡
(0, '花呗更改绑定银行卡', 0.8551459908485413)
(1, '我什么时候开通了花呗', 0.721195638179779)
(4, '中央情报局局长访问以色列叙利亚会谈', 0.2517135739326477)
(3, '暴风雨掩埋了东北部；新泽西16英寸的降雪', 0.21666759252548218)
(2, '俄罗斯警告乌克兰反对欧盟协议', 0.1450251191854477)
search top 3:
花呗更改绑定银行卡: 0.8551
我什么时候开通了花呗: 0.7212
中央情报局局长访问以色列叙利亚会谈: 0.2517
```
> 余弦`score`的值范围[-1, 1]，值越大，表示该query与corpus的文本越相似。
@ -167,12 +171,30 @@ from similarities.literalsim import SimHashSimilarity, TfidfSimilarity, BM25Simi
text1 = "如何更换花呗绑定银行卡"
text2 = "花呗更改绑定银行卡"
corpus = [
'花呗更改绑定银行卡',
'我什么时候开通了花呗',
'俄罗斯警告乌克兰反对欧盟协议',
'暴风雨掩埋了东北部；新泽西16英寸的降雪',
'中央情报局局长访问以色列叙利亚会谈',
'人在巴基斯坦基地的炸弹袭击中丧生',
]
queries = [
'我的花呗开通了？',
'乌克兰被俄罗斯警告'
]
m = TfidfSimilarity()
print(text1, text2, ' sim score: ', m.similarity(text1, text2))
zh_list = ['刘若英是个演员', '他唱歌很好听', 'women喜欢这首歌', '我不是演员吗']
m.add_corpus(zh_list)
print(m.most_similar('刘若英是演员'))
m.add_corpus(corpus)
res = m.most_similar(queries, topn=3)
print('sim search: ', res)
for q_id, c in res.items():
print('query:', queries[q_id])
print("search top 3:")
for corpus_id, s in c.items():
print(f'\t{m.corpus[corpus_id]}: {s:.4f}')
```
output:
@ -180,7 +202,13 @@ output:
```shell
如何更换花呗绑定银行卡 花呗更改绑定银行卡 sim score: 0.8203384355246909
[(0, '刘若英是个演员', 0.9847577834309504), (3, '我不是演员吗', 0.7056381915655814), (1, '他唱歌很好听', 0.5), (2, 'women喜欢这首歌', 0.5)]
sim search: {0: {2: 0.9999999403953552, 1: 0.43930041790008545, 0: 0.0}, 1: {0: 0.7380483150482178, 1: 0.0, 2: 0.0}}
query: 我的花呗开通了？
search top 3:
我什么时候开通了花呗: 1.0000
花呗更改绑定银行卡: 0.4393
俄罗斯警告乌克兰反对欧盟协议: 0.0000
...
```
### 5. 图像相似度计算和匹配搜索
@ -192,31 +220,100 @@ example: [examples/image_demo.py](./examples/image_demo.py)
```python
import sys
import glob
from PIL import Image
sys.path.append('..')
from similarities.imagesim import ImageHashSimilarity, SiftSimilarity, ClipSimilarity
image_fp1 = 'data/image1.png'
image_fp2 = 'data/image12-like-image1.png'
m = ClipSimilarity()
print(m)
print(m.similarity(image_fp1, image_fp2))
# add corpus
m.add_corpus(glob.glob('data/*.jpg') + glob.glob('data/*.png'))
r = m.most_similar(image_fp1)
print(r)
def sim_and_search(m):
print(m)
# similarity
sim_scores = m.similarity(imgs1, imgs2)
print('sim scores: ', sim_scores)
for (idx, i), j in zip(enumerate(image_fps1), image_fps2):
s = sim_scores[idx] if isinstance(sim_scores, list) else sim_scores[idx][idx]
print(f"{i} vs {j}, score: {s:.4f}")
# search
m.add_corpus(corpus_imgs)
queries = imgs1
res = m.most_similar(queries, topn=3)
print('sim search: ', res)
for q_id, c in res.items():
print('query:', image_fps1[q_id])
print("search top 3:")
for corpus_id, s in c.items():
print(f'\t{m.corpus[corpus_id].filename}: {s:.4f}')
print('-' * 50 + '\n')
image_fps1 = ['data/image1.png', 'data/image3.png']
image_fps2 = ['data/image12-like-image1.png', 'data/image10.png']
imgs1 = [Image.open(i) for i in image_fps1]
imgs2 = [Image.open(i) for i in image_fps2]
corpus_fps = glob.glob('data/*.jpg') + glob.glob('data/*.png')
corpus_imgs = [Image.open(i) for i in corpus_fps]
# 2. image and image similarity score
sim_and_search(ClipSimilarity()) # the best result
sim_and_search(ImageHashSimilarity(hash_function='phash'))
sim_and_search(SiftSimilarity())
```
output:
```shell
0.9579
Similarity: ClipSimilarity, matching_model: CLIPModel
sim scores: tensor([[0.9580, 0.8654],
[0.6558, 0.6145]])
[(6, 'data/image1.png', 1.0), (0, 'data/image12-like-image1.png', 0.9579654335975647), (4, 'data/image8-like-image1.png', 0.9326782822608948), ... ]
data/image1.png vs data/image12-like-image1.png, score: 0.9580
data/image3.png vs data/image10.png, score: 0.6145
sim search: {0: {6: 0.9999999403953552, 0: 0.9579654932022095, 4: 0.9326782822608948}, 1: {8: 0.9999997615814209, 4: 0.6729235649108887, 0: 0.6558331847190857}}
query: data/image1.png
search top 3:
data/image1.png: 1.0000
data/image12-like-image1.png: 0.9580
data/image8-like-image1.png: 0.9327
```
![image_sim](docs/image_sim.png)
### 6. 图文互搜
CLIP 模型不仅支持以图搜图，还支持图文互搜：
```python
import sys
import glob
from PIL import Image
sys.path.append('..')
from similarities.imagesim import ImageHashSimilarity, SiftSimilarity, ClipSimilarity
m = ClipSimilarity()
print(m)
# similarity score between text and image
image_fps = ['data/image3.png', # yellow flower image
'data/image1.png'] # tiger image
texts = ['a yellow flower', 'a tiger']
imgs = [Image.open(i) for i in image_fps]
sim_scores = m.similarity(imgs, texts)
print('sim scores: ', sim_scores)
for (idx, i), j in zip(enumerate(image_fps), texts):
s = sim_scores[idx][idx]
print(f"{i} vs {j}, score: {s:.4f}")
```
output:
```shell
sim scores: tensor([[0.3220, 0.2409],
[0.1677, 0.2959]])
data/image3.png vs a yellow flower, score: 0.3220
data/image1.png vs a tiger, score: 0.2959
```
# Contact
- Issue(建议)

1
examples/base_demo.py
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@ -35,7 +35,6 @@ for i in range(len(sentences)):
print('-' * 50 + '\n')
# 3.Semantic Search
model.add_corpus(corpus)
model.add_corpus(corpus)
res = model.most_similar(queries=sentences, topn=3)
print(res)
for q_id, c in res.items():

13
examples/one_line_demo.py Normal file
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@ -0,0 +1,13 @@
# -*- coding: utf-8 -*-
"""
@author:XuMing(xuming624@qq.com)
@description:
"""
import sys
sys.path.append('..')
from similarities import Similarity
m = Similarity()
r = m.similarity('如何更换花呗绑定银行卡', '花呗更改绑定银行卡')
print(f"similarity score: {float(r)}")