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concise-concepts-0.6.3
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تبلیغات ما
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشترتوضیحات
This repository contains an easy and intuitive approach to few-shot NER using most similar expansion over spaCy embeddings. Now with entity confidence scores!
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | concise-concepts-0.6.3 |
| نام | concise-concepts |
| نسخه کتابخانه | 0.6.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | David Berenstein |
| ایمیل نویسنده | david.m.berenstein@gmail.com |
| آدرس صفحه اصلی | https://github.com/pandora-intelligence/concise-concepts |
| آدرس اینترنتی | https://pypi.org/project/concise-concepts/ |
| مجوز | MIT |
# Concise Concepts
When wanting to apply NER to concise concepts, it is really easy to come up with examples, but pretty difficult to train an entire pipeline. Concise Concepts uses few-shot NER based on word embedding similarity to get you going
with easy! Now with entity scoring!
[](https://github.com/Pandora-Intelligence/concise-concepts/actions/workflows/python-package.yml)
[](https://github.com/pandora-intelligence/concise-concepts/releases)
[](https://pypi.org/project/concise-concepts/)
[](https://pypi.org/project/concise-concepts/)
[](https://github.com/ambv/black)
## Usage
This library defines matching patterns based on the most similar words found in each group, which are used to fill a [spaCy EntityRuler](https://spacy.io/api/entityruler). To better understand the rule definition, I recommend playing around with the [spaCy Rule-based Matcher Explorer](https://demos.explosion.ai/matcher).
### Tutorials
- [TechVizTheDataScienceGuy](https://www.youtube.com/c/TechVizTheDataScienceGuy) created a [nice tutorial](https://prakhar-mishra.medium.com/few-shot-named-entity-recognition-in-natural-language-processing-92d31f0d1143) on how to use it.
- [I](https://www.linkedin.com/in/david-berenstein-1bab11105/) created a [tutorial](https://www.rubrix.ml/blog/concise-concepts-rubrix/) in collaboration with Rubrix.
The section [Matching Pattern Rules](#matching-pattern-rules) expands on the construction, analysis and customization of these matching patterns.
# Install
```
pip install concise-concepts
```
# Quickstart
```python
import spacy
from spacy import displacy
import concise_concepts
data = {
"fruit": ["apple", "pear", "orange"],
"vegetable": ["broccoli", "spinach", "tomato"],
"meat": ["beef", "pork", "fish", "lamb"],
}
text = """
Heat the oil in a large pan and add the Onion, celery and carrots.
Then, cook over a medium–low heat for 10 minutes, or until softened.
Add the courgette, garlic, red peppers and oregano and cook for 2–3 minutes.
Later, add some oranges and chickens. """
nlp = spacy.load("en_core_web_lg", disable=["ner"])
nlp.add_pipe(
"concise_concepts",
config={
"data": data,
"ent_score": True, # Entity Scoring section
"verbose": True,
"exclude_pos": ["VERB", "AUX"],
"exclude_dep": ["DOBJ", "PCOMP"],
"include_compound_words": False,
"json_path": "./fruitful_patterns.json",
},
)
doc = nlp(text)
options = {
"colors": {"fruit": "darkorange", "vegetable": "limegreen", "meat": "salmon"},
"ents": ["fruit", "vegetable", "meat"],
}
ents = doc.ents
for ent in ents:
new_label = f"{ent.label_} ({float(ent._.ent_score):.0%})"
options["colors"][new_label] = options["colors"].get(ent.label_.lower(), None)
options["ents"].append(new_label)
ent.label_ = new_label
doc.ents = ents
displacy.render(doc, style="ent", options=options)
```
# Features
## Matching Pattern Rules
A general introduction about the usage of matching patterns in the [usage section](#usage).
### Customizing Matching Pattern Rules
Even though the baseline parameters provide a decent result, the construction of these matching rules can be customized via the config passed to the spaCy pipeline.
- `exclude_pos`: A list of POS tags to be excluded from the rule-based match.
- `exclude_dep`: A list of dependencies to be excluded from the rule-based match.
- `include_compound_words`: If True, it will include compound words in the entity. For example, if the entity is "New York", it will also include "New York City" as an entity.
- `case_sensitive`: Whether to match the case of the words in the text.
### Analyze Matching Pattern Rules
To motivate actually looking at the data and support interpretability, the matching patterns that have been generated are stored as `./main_patterns.json`. This behavior can be changed by using the `json_path` variable via the config passed to the spaCy pipeline.
## Most Similar Word Expansion
Use a specific number of words to expand over.
```python
data = {
"fruit": ["apple", "pear", "orange"],
"vegetable": ["broccoli", "spinach", "tomato"],
"meat": ["beef", "pork", "fish", "lamb"]
}
topn = [50, 50, 150]
assert len(topn) == len
nlp.add_pipe("concise_concepts", config={"data": data, "topn": topn})
```
## Entity Scoring
Use embdding based word similarity to score entities
```python
import spacy
import concise_concepts
data = {
"ORG": ["Google", "Apple", "Amazon"],
"GPE": ["Netherlands", "France", "China"],
}
text = """Sony was founded in Japan."""
nlp = spacy.load("en_core_web_lg")
nlp.add_pipe("concise_concepts", config={"data": data, "ent_score": True})
doc = nlp(text)
print([(ent.text, ent.label_, ent._.ent_score) for ent in doc.ents])
# output
#
# [('Sony', 'ORG', 0.63740385), ('Japan', 'GPE', 0.5896993)]
```
## Custom Embedding Models
Use `gensim.Word2vec` `gensim.FastText` or `gensim.KeyedVectors` model from the [pre-trained gensim](https://radimrehurek.com/gensim/downloader.html) library or a custom model path.
```python
data = {
"fruit": ["apple", "pear", "orange"],
"vegetable": ["broccoli", "spinach", "tomato"],
"meat": ["beef", "pork", "fish", "lamb"]
}
# model from https://radimrehurek.com/gensim/downloader.html or path to local file
model_path = "glove-wiki-gigaword-300"
nlp.add_pipe("concise_concepts", config={"data": data, "model_path": model_path})
````
نیازمندی
| مقدار | نام |
|---|---|
| >=4,<5 | gensim |
| >=1.7,<2.0 | scipy |
| >=3,<4 | spacy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8,<3.12 | Python |
نحوه نصب
نصب پکیج whl concise-concepts-0.6.3:
pip install concise-concepts-0.6.3.whl
نصب پکیج tar.gz concise-concepts-0.6.3:
pip install concise-concepts-0.6.3.tar.gz