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@@ -0,0 +1 @@ +/spacy-stanza-1.0.3.tar.gz diff --git a/python-spacy-stanza.spec b/python-spacy-stanza.spec new file mode 100644 index 0000000..6fc0840 --- /dev/null +++ b/python-spacy-stanza.spec @@ -0,0 +1,680 @@ +%global _empty_manifest_terminate_build 0 +Name: python-spacy-stanza +Version: 1.0.3 +Release: 1 +Summary: Use the latest Stanza (StanfordNLP) research models directly in spaCy +License: MIT +URL: https://explosion.ai +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/95/54/b30f48236ab4701a52e4304a69e01b561f8ac5778f3defa14dc720644461/spacy-stanza-1.0.3.tar.gz +BuildArch: noarch + +Requires: python3-spacy +Requires: python3-stanza + +%description +<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a> + +# spaCy + Stanza (formerly StanfordNLP) + +This package wraps the [Stanza](https://github.com/stanfordnlp/stanza) +(formerly StanfordNLP) library, so you can use Stanford's models in a +[spaCy](https://spacy.io) pipeline. The Stanford models achieved top accuracy +in the CoNLL 2017 and 2018 shared task, which involves tokenization, +part-of-speech tagging, morphological analysis, lemmatization and labeled +dependency parsing in 68 languages. As of v1.0, Stanza also supports named +entity recognition for selected languages. + +> ⚠️ Previous version of this package were available as +> [`spacy-stanfordnlp`](https://pypi.python.org/pypi/spacy-stanfordnlp). + +[](https://dev.azure.com/explosion-ai/public/_build?definitionId=17) +[](https://pypi.python.org/pypi/spacy-stanza) +[](https://github.com/explosion/spacy-stanza) +[](https://github.com/ambv/black) + +Using this wrapper, you'll be able to use the following annotations, computed by +your pretrained `stanza` model: + +- Statistical tokenization (reflected in the `Doc` and its tokens) +- Lemmatization (`token.lemma` and `token.lemma_`) +- Part-of-speech tagging (`token.tag`, `token.tag_`, `token.pos`, `token.pos_`) +- Morphological analysis (`token.morph`) +- Dependency parsing (`token.dep`, `token.dep_`, `token.head`) +- Named entity recognition (`doc.ents`, `token.ent_type`, `token.ent_type_`, `token.ent_iob`, `token.ent_iob_`) +- Sentence segmentation (`doc.sents`) + +## ️️️⌛️ Installation + +As of v1.0.0 `spacy-stanza` is only compatible with **spaCy v3.x**. To install +the most recent version: + +```bash +pip install spacy-stanza +``` + +For spaCy v2, install v0.2.x and refer to the [v0.2.x usage +documentation](https://github.com/explosion/spacy-stanza/tree/v0.2.x#-usage--examples): + +```bash +pip install "spacy-stanza<0.3.0" +``` + +Make sure to also +[download](https://stanfordnlp.github.io/stanza/download_models.html) one of +the [pre-trained Stanza +models](https://stanfordnlp.github.io/stanza/models.html). + +## 📖 Usage & Examples + +> ⚠️ **Important note:** This package has been refactored to take advantage of +> [spaCy v3.0](https://spacy.io). Previous versions that were built for [spaCy +> v2.x](https://v2.spacy.io) worked considerably differently. Please see +> previous tagged versions of this README for documentation on prior versions. + +Use `spacy_stanza.load_pipeline()` to create an `nlp` object that you can use to +process a text with a Stanza pipeline and create a spaCy [`Doc` +object](https://spacy.io/api/doc). By default, both the spaCy pipeline and the +Stanza pipeline will be initialized with the same `lang`, e.g. "en": + +```python +import stanza +import spacy_stanza + +# Download the stanza model if necessary +stanza.download("en") + +# Initialize the pipeline +nlp = spacy_stanza.load_pipeline("en") + +doc = nlp("Barack Obama was born in Hawaii. He was elected president in 2008.") +for token in doc: + print(token.text, token.lemma_, token.pos_, token.dep_, token.ent_type_) +print(doc.ents) +``` + +If language data for the given language is available in spaCy, the respective +language class can be used as the base for the `nlp` object – for example, +`English()`. This lets you use spaCy's lexical attributes like `is_stop` or +`like_num`. The `nlp` object follows the same API as any other spaCy `Language` +class – so you can visualize the `Doc` objects with displaCy, add custom +components to the pipeline, use the rule-based matcher and do pretty much +anything else you'd normally do in spaCy. + +```python +# Access spaCy's lexical attributes +print([token.is_stop for token in doc]) +print([token.like_num for token in doc]) + +# Visualize dependencies +from spacy import displacy +displacy.serve(doc) # or displacy.render if you're in a Jupyter notebook + +# Process texts with nlp.pipe +for doc in nlp.pipe(["Lots of texts", "Even more texts", "..."]): + print(doc.text) + +# Combine with your own custom pipeline components +from spacy import Language +@Language.component("custom_component") +def custom_component(doc): + # Do something to the doc here + print(f"Custom component called: {doc.text}") + return doc + +nlp.add_pipe("custom_component") +doc = nlp("Some text") + +# Serialize attributes to a numpy array +np_array = doc.to_array(['ORTH', 'LEMMA', 'POS']) +``` + +### Stanza Pipeline options + +Additional options for the Stanza +[`Pipeline`](https://stanfordnlp.github.io/stanza/pipeline.html#pipeline) can be +provided as keyword arguments following the `Pipeline` API: + +- Provide the Stanza language as `lang`. For Stanza languages without spaCy + support, use "xx" for the spaCy language setting: + + ```python + # Initialize a pipeline for Coptic + nlp = spacy_stanza.load_pipeline("xx", lang="cop") + ``` + +- Provide Stanza pipeline settings following the `Pipeline` API: + + ```python + # Initialize a German pipeline with the `hdt` package + nlp = spacy_stanza.load_pipeline("de", package="hdt") + ``` + +- Tokenize with spaCy rather than the statistical tokenizer (only for English): + + ```python + nlp = spacy_stanza.load_pipeline("en", processors= {"tokenize": "spacy"}) + ``` + +- Provide any additional processor settings as additional keyword arguments: + + ```python + # Provide pretokenized texts (whitespace tokenization) + nlp = spacy_stanza.load_pipeline("de", tokenize_pretokenized=True) + ``` + +The spaCy config specifies all `Pipeline` options in the `[nlp.tokenizer]` +block. For example, the config for the last example above, a German pipeline +with pretokenized texts: + +```ini +[nlp.tokenizer] +@tokenizers = "spacy_stanza.PipelineAsTokenizer.v1" +lang = "de" +dir = null +package = "default" +logging_level = null +verbose = null +use_gpu = true + +[nlp.tokenizer.kwargs] +tokenize_pretokenized = true + +[nlp.tokenizer.processors] +``` + +### Serialization + +The full Stanza pipeline configuration is stored in the spaCy pipeline +[config](https://spacy.io/usage/training#config), so you can save and load the +pipeline just like any other `nlp` pipeline: + +```python +# Save to a local directory +nlp.to_disk("./stanza-spacy-model") + +# Reload the pipeline +nlp = spacy.load("./stanza-spacy-model") +``` + +Note that this **does not save any Stanza model data by default**. The Stanza +models are very large, so for now, this package expects you to download the +models separately with `stanza.download()` and have them available either in +the default model directory or in the path specified under +`[nlp.tokenizer.dir]` in the config. + +### Adding additional spaCy pipeline components + +By default, the spaCy pipeline in the `nlp` object returned by +`spacy_stanza.load_pipeline()` will be empty, because all `stanza` attributes +are computed and set within the custom tokenizer, +[`StanzaTokenizer`](spacy_stanza/tokenizer.py). But since it's a regular `nlp` +object, you can add your own components to the pipeline. For example, you could +add [your own custom text classification +component](https://spacy.io/usage/training) with `nlp.add_pipe("textcat", +source=source_nlp)`, or augment the named entities with your own rule-based +patterns using the [`EntityRuler` +component](https://spacy.io/usage/rule-based-matching#entityruler). + + +%package -n python3-spacy-stanza +Summary: Use the latest Stanza (StanfordNLP) research models directly in spaCy +Provides: python-spacy-stanza +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-spacy-stanza +<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a> + +# spaCy + Stanza (formerly StanfordNLP) + +This package wraps the [Stanza](https://github.com/stanfordnlp/stanza) +(formerly StanfordNLP) library, so you can use Stanford's models in a +[spaCy](https://spacy.io) pipeline. The Stanford models achieved top accuracy +in the CoNLL 2017 and 2018 shared task, which involves tokenization, +part-of-speech tagging, morphological analysis, lemmatization and labeled +dependency parsing in 68 languages. As of v1.0, Stanza also supports named +entity recognition for selected languages. + +> ⚠️ Previous version of this package were available as +> [`spacy-stanfordnlp`](https://pypi.python.org/pypi/spacy-stanfordnlp). + +[](https://dev.azure.com/explosion-ai/public/_build?definitionId=17) +[](https://pypi.python.org/pypi/spacy-stanza) +[](https://github.com/explosion/spacy-stanza) +[](https://github.com/ambv/black) + +Using this wrapper, you'll be able to use the following annotations, computed by +your pretrained `stanza` model: + +- Statistical tokenization (reflected in the `Doc` and its tokens) +- Lemmatization (`token.lemma` and `token.lemma_`) +- Part-of-speech tagging (`token.tag`, `token.tag_`, `token.pos`, `token.pos_`) +- Morphological analysis (`token.morph`) +- Dependency parsing (`token.dep`, `token.dep_`, `token.head`) +- Named entity recognition (`doc.ents`, `token.ent_type`, `token.ent_type_`, `token.ent_iob`, `token.ent_iob_`) +- Sentence segmentation (`doc.sents`) + +## ️️️⌛️ Installation + +As of v1.0.0 `spacy-stanza` is only compatible with **spaCy v3.x**. To install +the most recent version: + +```bash +pip install spacy-stanza +``` + +For spaCy v2, install v0.2.x and refer to the [v0.2.x usage +documentation](https://github.com/explosion/spacy-stanza/tree/v0.2.x#-usage--examples): + +```bash +pip install "spacy-stanza<0.3.0" +``` + +Make sure to also +[download](https://stanfordnlp.github.io/stanza/download_models.html) one of +the [pre-trained Stanza +models](https://stanfordnlp.github.io/stanza/models.html). + +## 📖 Usage & Examples + +> ⚠️ **Important note:** This package has been refactored to take advantage of +> [spaCy v3.0](https://spacy.io). Previous versions that were built for [spaCy +> v2.x](https://v2.spacy.io) worked considerably differently. Please see +> previous tagged versions of this README for documentation on prior versions. + +Use `spacy_stanza.load_pipeline()` to create an `nlp` object that you can use to +process a text with a Stanza pipeline and create a spaCy [`Doc` +object](https://spacy.io/api/doc). By default, both the spaCy pipeline and the +Stanza pipeline will be initialized with the same `lang`, e.g. "en": + +```python +import stanza +import spacy_stanza + +# Download the stanza model if necessary +stanza.download("en") + +# Initialize the pipeline +nlp = spacy_stanza.load_pipeline("en") + +doc = nlp("Barack Obama was born in Hawaii. He was elected president in 2008.") +for token in doc: + print(token.text, token.lemma_, token.pos_, token.dep_, token.ent_type_) +print(doc.ents) +``` + +If language data for the given language is available in spaCy, the respective +language class can be used as the base for the `nlp` object – for example, +`English()`. This lets you use spaCy's lexical attributes like `is_stop` or +`like_num`. The `nlp` object follows the same API as any other spaCy `Language` +class – so you can visualize the `Doc` objects with displaCy, add custom +components to the pipeline, use the rule-based matcher and do pretty much +anything else you'd normally do in spaCy. + +```python +# Access spaCy's lexical attributes +print([token.is_stop for token in doc]) +print([token.like_num for token in doc]) + +# Visualize dependencies +from spacy import displacy +displacy.serve(doc) # or displacy.render if you're in a Jupyter notebook + +# Process texts with nlp.pipe +for doc in nlp.pipe(["Lots of texts", "Even more texts", "..."]): + print(doc.text) + +# Combine with your own custom pipeline components +from spacy import Language +@Language.component("custom_component") +def custom_component(doc): + # Do something to the doc here + print(f"Custom component called: {doc.text}") + return doc + +nlp.add_pipe("custom_component") +doc = nlp("Some text") + +# Serialize attributes to a numpy array +np_array = doc.to_array(['ORTH', 'LEMMA', 'POS']) +``` + +### Stanza Pipeline options + +Additional options for the Stanza +[`Pipeline`](https://stanfordnlp.github.io/stanza/pipeline.html#pipeline) can be +provided as keyword arguments following the `Pipeline` API: + +- Provide the Stanza language as `lang`. For Stanza languages without spaCy + support, use "xx" for the spaCy language setting: + + ```python + # Initialize a pipeline for Coptic + nlp = spacy_stanza.load_pipeline("xx", lang="cop") + ``` + +- Provide Stanza pipeline settings following the `Pipeline` API: + + ```python + # Initialize a German pipeline with the `hdt` package + nlp = spacy_stanza.load_pipeline("de", package="hdt") + ``` + +- Tokenize with spaCy rather than the statistical tokenizer (only for English): + + ```python + nlp = spacy_stanza.load_pipeline("en", processors= {"tokenize": "spacy"}) + ``` + +- Provide any additional processor settings as additional keyword arguments: + + ```python + # Provide pretokenized texts (whitespace tokenization) + nlp = spacy_stanza.load_pipeline("de", tokenize_pretokenized=True) + ``` + +The spaCy config specifies all `Pipeline` options in the `[nlp.tokenizer]` +block. For example, the config for the last example above, a German pipeline +with pretokenized texts: + +```ini +[nlp.tokenizer] +@tokenizers = "spacy_stanza.PipelineAsTokenizer.v1" +lang = "de" +dir = null +package = "default" +logging_level = null +verbose = null +use_gpu = true + +[nlp.tokenizer.kwargs] +tokenize_pretokenized = true + +[nlp.tokenizer.processors] +``` + +### Serialization + +The full Stanza pipeline configuration is stored in the spaCy pipeline +[config](https://spacy.io/usage/training#config), so you can save and load the +pipeline just like any other `nlp` pipeline: + +```python +# Save to a local directory +nlp.to_disk("./stanza-spacy-model") + +# Reload the pipeline +nlp = spacy.load("./stanza-spacy-model") +``` + +Note that this **does not save any Stanza model data by default**. The Stanza +models are very large, so for now, this package expects you to download the +models separately with `stanza.download()` and have them available either in +the default model directory or in the path specified under +`[nlp.tokenizer.dir]` in the config. + +### Adding additional spaCy pipeline components + +By default, the spaCy pipeline in the `nlp` object returned by +`spacy_stanza.load_pipeline()` will be empty, because all `stanza` attributes +are computed and set within the custom tokenizer, +[`StanzaTokenizer`](spacy_stanza/tokenizer.py). But since it's a regular `nlp` +object, you can add your own components to the pipeline. For example, you could +add [your own custom text classification +component](https://spacy.io/usage/training) with `nlp.add_pipe("textcat", +source=source_nlp)`, or augment the named entities with your own rule-based +patterns using the [`EntityRuler` +component](https://spacy.io/usage/rule-based-matching#entityruler). + + +%package help +Summary: Development documents and examples for spacy-stanza +Provides: python3-spacy-stanza-doc +%description help +<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a> + +# spaCy + Stanza (formerly StanfordNLP) + +This package wraps the [Stanza](https://github.com/stanfordnlp/stanza) +(formerly StanfordNLP) library, so you can use Stanford's models in a +[spaCy](https://spacy.io) pipeline. The Stanford models achieved top accuracy +in the CoNLL 2017 and 2018 shared task, which involves tokenization, +part-of-speech tagging, morphological analysis, lemmatization and labeled +dependency parsing in 68 languages. As of v1.0, Stanza also supports named +entity recognition for selected languages. + +> ⚠️ Previous version of this package were available as +> [`spacy-stanfordnlp`](https://pypi.python.org/pypi/spacy-stanfordnlp). + +[](https://dev.azure.com/explosion-ai/public/_build?definitionId=17) +[](https://pypi.python.org/pypi/spacy-stanza) +[](https://github.com/explosion/spacy-stanza) +[](https://github.com/ambv/black) + +Using this wrapper, you'll be able to use the following annotations, computed by +your pretrained `stanza` model: + +- Statistical tokenization (reflected in the `Doc` and its tokens) +- Lemmatization (`token.lemma` and `token.lemma_`) +- Part-of-speech tagging (`token.tag`, `token.tag_`, `token.pos`, `token.pos_`) +- Morphological analysis (`token.morph`) +- Dependency parsing (`token.dep`, `token.dep_`, `token.head`) +- Named entity recognition (`doc.ents`, `token.ent_type`, `token.ent_type_`, `token.ent_iob`, `token.ent_iob_`) +- Sentence segmentation (`doc.sents`) + +## ️️️⌛️ Installation + +As of v1.0.0 `spacy-stanza` is only compatible with **spaCy v3.x**. To install +the most recent version: + +```bash +pip install spacy-stanza +``` + +For spaCy v2, install v0.2.x and refer to the [v0.2.x usage +documentation](https://github.com/explosion/spacy-stanza/tree/v0.2.x#-usage--examples): + +```bash +pip install "spacy-stanza<0.3.0" +``` + +Make sure to also +[download](https://stanfordnlp.github.io/stanza/download_models.html) one of +the [pre-trained Stanza +models](https://stanfordnlp.github.io/stanza/models.html). + +## 📖 Usage & Examples + +> ⚠️ **Important note:** This package has been refactored to take advantage of +> [spaCy v3.0](https://spacy.io). Previous versions that were built for [spaCy +> v2.x](https://v2.spacy.io) worked considerably differently. Please see +> previous tagged versions of this README for documentation on prior versions. + +Use `spacy_stanza.load_pipeline()` to create an `nlp` object that you can use to +process a text with a Stanza pipeline and create a spaCy [`Doc` +object](https://spacy.io/api/doc). By default, both the spaCy pipeline and the +Stanza pipeline will be initialized with the same `lang`, e.g. "en": + +```python +import stanza +import spacy_stanza + +# Download the stanza model if necessary +stanza.download("en") + +# Initialize the pipeline +nlp = spacy_stanza.load_pipeline("en") + +doc = nlp("Barack Obama was born in Hawaii. He was elected president in 2008.") +for token in doc: + print(token.text, token.lemma_, token.pos_, token.dep_, token.ent_type_) +print(doc.ents) +``` + +If language data for the given language is available in spaCy, the respective +language class can be used as the base for the `nlp` object – for example, +`English()`. This lets you use spaCy's lexical attributes like `is_stop` or +`like_num`. The `nlp` object follows the same API as any other spaCy `Language` +class – so you can visualize the `Doc` objects with displaCy, add custom +components to the pipeline, use the rule-based matcher and do pretty much +anything else you'd normally do in spaCy. + +```python +# Access spaCy's lexical attributes +print([token.is_stop for token in doc]) +print([token.like_num for token in doc]) + +# Visualize dependencies +from spacy import displacy +displacy.serve(doc) # or displacy.render if you're in a Jupyter notebook + +# Process texts with nlp.pipe +for doc in nlp.pipe(["Lots of texts", "Even more texts", "..."]): + print(doc.text) + +# Combine with your own custom pipeline components +from spacy import Language +@Language.component("custom_component") +def custom_component(doc): + # Do something to the doc here + print(f"Custom component called: {doc.text}") + return doc + +nlp.add_pipe("custom_component") +doc = nlp("Some text") + +# Serialize attributes to a numpy array +np_array = doc.to_array(['ORTH', 'LEMMA', 'POS']) +``` + +### Stanza Pipeline options + +Additional options for the Stanza +[`Pipeline`](https://stanfordnlp.github.io/stanza/pipeline.html#pipeline) can be +provided as keyword arguments following the `Pipeline` API: + +- Provide the Stanza language as `lang`. For Stanza languages without spaCy + support, use "xx" for the spaCy language setting: + + ```python + # Initialize a pipeline for Coptic + nlp = spacy_stanza.load_pipeline("xx", lang="cop") + ``` + +- Provide Stanza pipeline settings following the `Pipeline` API: + + ```python + # Initialize a German pipeline with the `hdt` package + nlp = spacy_stanza.load_pipeline("de", package="hdt") + ``` + +- Tokenize with spaCy rather than the statistical tokenizer (only for English): + + ```python + nlp = spacy_stanza.load_pipeline("en", processors= {"tokenize": "spacy"}) + ``` + +- Provide any additional processor settings as additional keyword arguments: + + ```python + # Provide pretokenized texts (whitespace tokenization) + nlp = spacy_stanza.load_pipeline("de", tokenize_pretokenized=True) + ``` + +The spaCy config specifies all `Pipeline` options in the `[nlp.tokenizer]` +block. For example, the config for the last example above, a German pipeline +with pretokenized texts: + +```ini +[nlp.tokenizer] +@tokenizers = "spacy_stanza.PipelineAsTokenizer.v1" +lang = "de" +dir = null +package = "default" +logging_level = null +verbose = null +use_gpu = true + +[nlp.tokenizer.kwargs] +tokenize_pretokenized = true + +[nlp.tokenizer.processors] +``` + +### Serialization + +The full Stanza pipeline configuration is stored in the spaCy pipeline +[config](https://spacy.io/usage/training#config), so you can save and load the +pipeline just like any other `nlp` pipeline: + +```python +# Save to a local directory +nlp.to_disk("./stanza-spacy-model") + +# Reload the pipeline +nlp = spacy.load("./stanza-spacy-model") +``` + +Note that this **does not save any Stanza model data by default**. The Stanza +models are very large, so for now, this package expects you to download the +models separately with `stanza.download()` and have them available either in +the default model directory or in the path specified under +`[nlp.tokenizer.dir]` in the config. + +### Adding additional spaCy pipeline components + +By default, the spaCy pipeline in the `nlp` object returned by +`spacy_stanza.load_pipeline()` will be empty, because all `stanza` attributes +are computed and set within the custom tokenizer, +[`StanzaTokenizer`](spacy_stanza/tokenizer.py). But since it's a regular `nlp` +object, you can add your own components to the pipeline. For example, you could +add [your own custom text classification +component](https://spacy.io/usage/training) with `nlp.add_pipe("textcat", +source=source_nlp)`, or augment the named entities with your own rule-based +patterns using the [`EntityRuler` +component](https://spacy.io/usage/rule-based-matching#entityruler). + + +%prep +%autosetup -n spacy-stanza-1.0.3 + +%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-spacy-stanza -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.3-1 +- Package Spec generated @@ -0,0 +1 @@ +91fecbe74c0a2a83a3dcb3029d15c39a spacy-stanza-1.0.3.tar.gz |