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diff --git a/python-simba.spec b/python-simba.spec new file mode 100644 index 0000000..ec1ce68 --- /dev/null +++ b/python-simba.spec @@ -0,0 +1,300 @@ +%global _empty_manifest_terminate_build 0 +Name: python-simba +Version: 0.1.1 +Release: 1 +Summary: Semantic similarity measures from Babylon Health +License: Proprietary +URL: https://github.com/babylonhealth/simba +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b6/11/d28277e11d32bb9452eb23e9f1f1b811874142b0f38ddc8eb02862a643ce/simba-0.1.1.tar.gz +BuildArch: noarch + + +%description +# simba :lion: + +Similarity measures from Babylon Health. + +## Installation + +```bash +$ pip install simba +``` + +You can also checkout this repository and install from the root folder: +```bash +$ pip install . +``` + +Many of the similarity measures in simba rely on pre-trained embeddings. +If you don't have your own encoding logic already, you can register your +embedding files to use them easily with simba, as long as they're in the +standard text format for word vectors (as described [here](https://fasttext.cc/docs/en/english-vectors.html)). +For example, if you want to use fastText vectors that you've saved to `/path/to/fasttext`, +you can just run +```bash +$ simba embs register --name fasttext --path /path/to/fasttext +``` +and simba will recognise them under the name `fasttext`. + +You can do something similar for frequencies files (like [these](https://github.com/PrincetonML/SIF/blob/master/auxiliary_data/enwiki_vocab_min200.txt)): +```bash +$ simba freqs register --name wiki --path /path/to/wiki/counts +``` + +## Usage +```python +from simba.similarities import dynamax_jaccard +from simba.core import embed + +sentences = ('The king has returned', 'Change is good') + +# Assuming you've registered fasttext embeddings as described above +x, y = embed([s.split() for s in sentences], embedding='fasttext') +sim = dynamax_jaccard(x, y) +``` +There are more examples, including comparing different similarity metrics on a dataset +of pairs, in the `examples` directory. + +## Similarity Measures + +This library contains implementations of the following methods in `simba.similarities`. +Please consider citing the corresponding papers in your work if you find them useful. + +| Method | Description | Paper | +| - | - | - | +| `avg_cosine` | Average vector compared with cosine similarity | - | +| `batch_avg_pca` | Average vector with principal component removal | [1] | +| `fbow_jaccard_factory` | Factory method for general fuzzy bag-of-words given a universe matrix | [2] | +| `max_jaccard` | Max-pooled vectors compared with Jaccard coefficient | [2] | +| `dynamax_{jaccard, otsuka, dice}` | DynaMax using Jaccard, Otsuka-Ochiai, and Dice coefficients | [2] | +| `gaussian_correction_{tic, aic}` | Takeuchi and Akaike Information Criteria (TIC and AIC) for Gaussian likelihood | [3] | +| `spherical_gaussian_correction_{tic, aic}` | TIC and AIC for spherical Gaussian likelihood | [3] | +| `von_mises_correction_{tic, aic}` | TIC and AIC for von Mises Fisher likelihood | [3] | +| `avg_{pearson, spearman, kendall}` | Average vector compared with Pearson, Spearman, and Kendall correlation | [4] | +| `max_spearman` | Max-pooled vectors compared with Spearman correlation | [5] | +| `cka_factory` | Factory method for general Centered Kernel Alignment (CKA) | [5] | +| `cka_{linear, gaussian}`| CKA with linear and Gaussian kernels | [5] | +| `dcorr` | CKA with distance kernel (distance correlation) | [5] | + +Papers: +1. [Arora et al., ICLR 2017. *A Simple but Tough-to-Beat Baseline for Sentence Embeddings*](https://openreview.net/forum?id=SyK00v5xx) +2. [Zhelezniak et al., ICLR 2019. *Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors*](https://openreview.net/forum?id=SkxXg2C5FX) +3. [Vargas et al., ICML 2019. *Model Comparison for Semantic Grouping*](http://proceedings.mlr.press/v97/vargas19a.html) +4. [Zhelezniak et al., NAACL-HLT 2019. *Correlation Coefficients and Semantic Textual Similarity*](https://www.aclweb.org/anthology/N19-1100/) +5. [Zhelezniak et al., EMNLP-IJCNLP 2019. *Correlations between Word Vector Sets*](https://arxiv.org/abs/1910.02902) + +## Contact +* [April Shen](https://github.com/apriltuesday) +* [Sasho Savkov](https://github.com/savkov) +* [Vitalii Zhelezniak](https://github.com/ironvital) + +%package -n python3-simba +Summary: Semantic similarity measures from Babylon Health +Provides: python-simba +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-simba +# simba :lion: + +Similarity measures from Babylon Health. + +## Installation + +```bash +$ pip install simba +``` + +You can also checkout this repository and install from the root folder: +```bash +$ pip install . +``` + +Many of the similarity measures in simba rely on pre-trained embeddings. +If you don't have your own encoding logic already, you can register your +embedding files to use them easily with simba, as long as they're in the +standard text format for word vectors (as described [here](https://fasttext.cc/docs/en/english-vectors.html)). +For example, if you want to use fastText vectors that you've saved to `/path/to/fasttext`, +you can just run +```bash +$ simba embs register --name fasttext --path /path/to/fasttext +``` +and simba will recognise them under the name `fasttext`. + +You can do something similar for frequencies files (like [these](https://github.com/PrincetonML/SIF/blob/master/auxiliary_data/enwiki_vocab_min200.txt)): +```bash +$ simba freqs register --name wiki --path /path/to/wiki/counts +``` + +## Usage +```python +from simba.similarities import dynamax_jaccard +from simba.core import embed + +sentences = ('The king has returned', 'Change is good') + +# Assuming you've registered fasttext embeddings as described above +x, y = embed([s.split() for s in sentences], embedding='fasttext') +sim = dynamax_jaccard(x, y) +``` +There are more examples, including comparing different similarity metrics on a dataset +of pairs, in the `examples` directory. + +## Similarity Measures + +This library contains implementations of the following methods in `simba.similarities`. +Please consider citing the corresponding papers in your work if you find them useful. + +| Method | Description | Paper | +| - | - | - | +| `avg_cosine` | Average vector compared with cosine similarity | - | +| `batch_avg_pca` | Average vector with principal component removal | [1] | +| `fbow_jaccard_factory` | Factory method for general fuzzy bag-of-words given a universe matrix | [2] | +| `max_jaccard` | Max-pooled vectors compared with Jaccard coefficient | [2] | +| `dynamax_{jaccard, otsuka, dice}` | DynaMax using Jaccard, Otsuka-Ochiai, and Dice coefficients | [2] | +| `gaussian_correction_{tic, aic}` | Takeuchi and Akaike Information Criteria (TIC and AIC) for Gaussian likelihood | [3] | +| `spherical_gaussian_correction_{tic, aic}` | TIC and AIC for spherical Gaussian likelihood | [3] | +| `von_mises_correction_{tic, aic}` | TIC and AIC for von Mises Fisher likelihood | [3] | +| `avg_{pearson, spearman, kendall}` | Average vector compared with Pearson, Spearman, and Kendall correlation | [4] | +| `max_spearman` | Max-pooled vectors compared with Spearman correlation | [5] | +| `cka_factory` | Factory method for general Centered Kernel Alignment (CKA) | [5] | +| `cka_{linear, gaussian}`| CKA with linear and Gaussian kernels | [5] | +| `dcorr` | CKA with distance kernel (distance correlation) | [5] | + +Papers: +1. [Arora et al., ICLR 2017. *A Simple but Tough-to-Beat Baseline for Sentence Embeddings*](https://openreview.net/forum?id=SyK00v5xx) +2. [Zhelezniak et al., ICLR 2019. *Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors*](https://openreview.net/forum?id=SkxXg2C5FX) +3. [Vargas et al., ICML 2019. *Model Comparison for Semantic Grouping*](http://proceedings.mlr.press/v97/vargas19a.html) +4. [Zhelezniak et al., NAACL-HLT 2019. *Correlation Coefficients and Semantic Textual Similarity*](https://www.aclweb.org/anthology/N19-1100/) +5. [Zhelezniak et al., EMNLP-IJCNLP 2019. *Correlations between Word Vector Sets*](https://arxiv.org/abs/1910.02902) + +## Contact +* [April Shen](https://github.com/apriltuesday) +* [Sasho Savkov](https://github.com/savkov) +* [Vitalii Zhelezniak](https://github.com/ironvital) + +%package help +Summary: Development documents and examples for simba +Provides: python3-simba-doc +%description help +# simba :lion: + +Similarity measures from Babylon Health. + +## Installation + +```bash +$ pip install simba +``` + +You can also checkout this repository and install from the root folder: +```bash +$ pip install . +``` + +Many of the similarity measures in simba rely on pre-trained embeddings. +If you don't have your own encoding logic already, you can register your +embedding files to use them easily with simba, as long as they're in the +standard text format for word vectors (as described [here](https://fasttext.cc/docs/en/english-vectors.html)). +For example, if you want to use fastText vectors that you've saved to `/path/to/fasttext`, +you can just run +```bash +$ simba embs register --name fasttext --path /path/to/fasttext +``` +and simba will recognise them under the name `fasttext`. + +You can do something similar for frequencies files (like [these](https://github.com/PrincetonML/SIF/blob/master/auxiliary_data/enwiki_vocab_min200.txt)): +```bash +$ simba freqs register --name wiki --path /path/to/wiki/counts +``` + +## Usage +```python +from simba.similarities import dynamax_jaccard +from simba.core import embed + +sentences = ('The king has returned', 'Change is good') + +# Assuming you've registered fasttext embeddings as described above +x, y = embed([s.split() for s in sentences], embedding='fasttext') +sim = dynamax_jaccard(x, y) +``` +There are more examples, including comparing different similarity metrics on a dataset +of pairs, in the `examples` directory. + +## Similarity Measures + +This library contains implementations of the following methods in `simba.similarities`. +Please consider citing the corresponding papers in your work if you find them useful. + +| Method | Description | Paper | +| - | - | - | +| `avg_cosine` | Average vector compared with cosine similarity | - | +| `batch_avg_pca` | Average vector with principal component removal | [1] | +| `fbow_jaccard_factory` | Factory method for general fuzzy bag-of-words given a universe matrix | [2] | +| `max_jaccard` | Max-pooled vectors compared with Jaccard coefficient | [2] | +| `dynamax_{jaccard, otsuka, dice}` | DynaMax using Jaccard, Otsuka-Ochiai, and Dice coefficients | [2] | +| `gaussian_correction_{tic, aic}` | Takeuchi and Akaike Information Criteria (TIC and AIC) for Gaussian likelihood | [3] | +| `spherical_gaussian_correction_{tic, aic}` | TIC and AIC for spherical Gaussian likelihood | [3] | +| `von_mises_correction_{tic, aic}` | TIC and AIC for von Mises Fisher likelihood | [3] | +| `avg_{pearson, spearman, kendall}` | Average vector compared with Pearson, Spearman, and Kendall correlation | [4] | +| `max_spearman` | Max-pooled vectors compared with Spearman correlation | [5] | +| `cka_factory` | Factory method for general Centered Kernel Alignment (CKA) | [5] | +| `cka_{linear, gaussian}`| CKA with linear and Gaussian kernels | [5] | +| `dcorr` | CKA with distance kernel (distance correlation) | [5] | + +Papers: +1. [Arora et al., ICLR 2017. *A Simple but Tough-to-Beat Baseline for Sentence Embeddings*](https://openreview.net/forum?id=SyK00v5xx) +2. [Zhelezniak et al., ICLR 2019. *Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors*](https://openreview.net/forum?id=SkxXg2C5FX) +3. [Vargas et al., ICML 2019. *Model Comparison for Semantic Grouping*](http://proceedings.mlr.press/v97/vargas19a.html) +4. [Zhelezniak et al., NAACL-HLT 2019. *Correlation Coefficients and Semantic Textual Similarity*](https://www.aclweb.org/anthology/N19-1100/) +5. [Zhelezniak et al., EMNLP-IJCNLP 2019. *Correlations between Word Vector Sets*](https://arxiv.org/abs/1910.02902) + +## Contact +* [April Shen](https://github.com/apriltuesday) +* [Sasho Savkov](https://github.com/savkov) +* [Vitalii Zhelezniak](https://github.com/ironvital) + +%prep +%autosetup -n simba-0.1.1 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-simba -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.1-1 +- Package Spec generated |