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aiocorenlp-1.0.2
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Asyncio support for Stanford CoreNLP
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | aiocorenlp-1.0.2 |
| نام | aiocorenlp |
| نسخه کتابخانه | 1.0.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | moomoohk |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/moomoohk/aiocorenlp |
| آدرس اینترنتی | https://pypi.org/project/aiocorenlp/ |
| مجوز | MIT |
# aiocorenlp
High-fidelity `asyncio` capable Stanford [CoreNLP](https://github.com/stanfordnlp/CoreNLP/) library.
Heavily based on [ner](https://github.com/dat/pyner) and [nltk](https://github.com/nltk/nltk).
## Rationale and differences from `nltk`
For every tag operation (in other words, every call to `StanfordTagger.tag*`), `nltk` runs a Stanford JAR (`stanford-ner.jar`/`stanford-postagger.jar`) in a newly spawned Java subprocess.
In order to pass the input text to these JARs, `nltk` first writes it to a `tempfile` and includes its path in the Java command line using the `-textFile` flag.
This method works well in sequential applications, however once scaled up by concurrency and stress problems begin to arise:
1. Python's `tempfile.mkstemp` doesn't work very well on Windows to begin with and starts to break down under stress.
* Calls to `tempfile.mkstemp` start to fail which in turn results in Stanford code failing (no input file to read).
* Temporary files get leaked resulting in negative impact on disk usage.
2. Repeated calls to `subprocess` mean:
* Multiple Java processes run in parallel causing negative impact on CPU and memory usage.
* OS-level subprocess and Java startup code has to be run every time causing additional negative impact on CPU usage.
All this causes unnecessary slowdown and bad reliability to user-written code.
Patching `nltk`'s code to use `tempfile.TemporaryDirectory` instead of `tempfile.mkstemp` seemed to resolve issue 1 but issue 2 would require more work.
This library runs the Stanford code in a server mode and sends input text over TCP, meaning:
1. Filesystem operations and temporary files/directories are avoided entirely.
2. There's no need to run a Java subprocess more than once.
3. The only synchronization bottleneck is offloaded to Java's `SocketServer` class which is used in the Stanford code.
4. CPU, memory and disk usage is greatly reduced.
### Differences from `ner`
* `asyncio` support.
* [Method name mangling](https://docs.python.org/3/tutorial/classes.html#private-variables) is inexplicably enabled in the [`ner.client.NER` class](https://https://github.com/dat/pyner/blob/master/ner/client.py), making subclassing not practical.
* The ner library appears to be abandoned.
### Differences from [`stanza`](https://github.com/stanfordnlp/stanza)
* `asyncio` support.
* Stanza aims to provide a wider range of uses.
## Basic Usage
```pycon
>>> from aiocorenlp import ner_tag
>>> await ner_tag("I complained to Microsoft about Bill Gates.")
[('O', 'I'), ('O', 'complained'), ('O', 'to'), ('ORGANIZATION', 'Microsoft'), ('O', 'about'), ('PERSON', 'Bill'), ('PERSON', 'Gates.')]
```
This usage doesn't require interfacing with the server and socket directly and is suitable for low frequency/one-time tagging.
## Advanced Usage
To fully take advantage of this library's benefits the `AsyncNerServer` and `AsyncPosServer` classes should be used:
```python
from aiocorenlp.async_ner_server import AsyncNerServer
from aiocorenlp.async_corenlp_socket import AsyncCorenlpSocket
server = AsyncNerServer()
port = server.start()
print(f"Server started on port {port}")
socket: AsyncCorenlpSocket = server.get_socket()
while True:
text = input("> ")
if text == "exit":
break
print(await socket.tag(text))
server.stop()
```
Context manager is supported as well:
```python
from aiocorenlp.async_ner_server import AsyncNerServer
server: AsyncNerServer
async with AsyncNerServer() as server:
socket = server.get_socket()
while True:
text = input("> ")
if text == "exit":
break
print(await socket.tag(text))
```
## Configuration
As seen above, all classes and functions this library exposes may be used without arguments (default values).
Optionally, the following arguments may be passed to `AsyncNerServer` (and by extension `ner_tag`/`pos_tag`):
* `port`: Server bind port. Leave `None` for random port.
* `model_path`: Path to language model. Leave `None` to let `nltk` find the model (supports `STANFORD_MODELS` environment variable).
* `jar_path`: Path to `stanford-*.jar`. Leave `None` to let `nltk` find the jar (supports `STANFORD_POSTAGGER` environment variable, for NER as well).
* `output_format`: Output format. See `OutputFormat` enum for values. Default is `slashTags`.
* `encoding`: Output encoding.
* `java_options`: Additional JVM options.
It is not possible to configure the server bind interface. This is a limitation imposed by the Stanford code.
نیازمندی
| مقدار | نام |
|---|---|
| - | nltk |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.10 | Python |
نحوه نصب
نصب پکیج whl aiocorenlp-1.0.2:
pip install aiocorenlp-1.0.2.whl
نصب پکیج tar.gz aiocorenlp-1.0.2:
pip install aiocorenlp-1.0.2.tar.gz