bgnlp 0.2.0

Package for Bulgarian Natural Language Processing (NLP)

bgnlp: Model-first approach to Bulgarian NLP

pip install bgnlp

Package functionalities

Part-of-speech (PoS) tagging

from bgnlp import pos


print(pos("Това е библиотека за обработка на естествен език."))

[{
    "word": "Това",
    "tag": "PDOsn",
    "bg_desc": "местоимение",
    "en_desc": "pronoun"
}, {
    "word": "е",
    "tag": "VLINr3s",
    "bg_desc": "глагол",
    "en_desc": "verb"
}, {
    "word": "библиотека",
    "tag": "NCFsof",
    "bg_desc": "съществително име",
    "en_desc": "noun"
}, {
    "word": "за",
    "tag": "R",
    "bg_desc": "предлог",
    "en_desc": "preposition"
}, {
    "word": "обработка",
    "tag": "NCFsof",
    "bg_desc": "съществително име",
    "en_desc": "noun"
}, {
    "word": "на",
    "tag": "R",
    "bg_desc": "предлог",
    "en_desc": "preposition"
}, {
    "word": "естествен",
    "tag": "Asmo",
    "bg_desc": "прилагателно име",
    "en_desc": "adjective"
}, {
    "word": "език",
    "tag": "NCMsom",
    "bg_desc": "съществително име",
    "en_desc": "noun"
}, {
    "word": ".",
    "tag": "U",
    "bg_desc": "препинателен знак",
    "en_desc": "punctuation"
}]

Lemmatization

from bgnlp import lemmatize


text = "Добре дошли!"
print(lemmatize(text))

[{'word': 'Добре', 'lemma': 'Добре'}, {'word': 'дошли', 'lemma': 'дойда'}, {'word': '!', 'lemma': '!'}]

# Generating a string of lemmas.
print(lemmatize(text, as_string=True))

Добре дойда!

Named Entity Recognition (NER) tagging

Currently, the available NER tags are:

• PER - Person
• ORG - Organization
• LOC - Location

from bgnlp import ner


text = "Барух Спиноза е роден в Амстердам"

print(f"Input: {text}")
print("Result:", ner(text))

Input: Барух Спиноза е роден в Амстердам
Result: [{'word': 'Барух Спиноза', 'entity_group': 'PER'}, {'word': 'Амстердам', 'entity_group': 'LOC'}]

Using a Config object

A tagger Config is used to define the underlying model.

You can change the device on which it makes inference:

# Make inference using the GPU (by default it is "cpu"):
config = NerTaggerConfig(device="cuda")
ner = NerTagger(config=config)
# ...

You can also change the path to the model weights. For NerTagger you can directly pass the HuggingFace's Model Hub path. All other taggers use weights uploaded to Google Drive.

# Define the path to the model weights. It can be a single .pt file or a path to HuggingFace's Model Hub (only for NerTagger).
config = NerTaggerConfig(model_path="path/to/model")
ner = NerTagger(config=config)
# ...

Please, note that the model should be of the same architecture as the one used by the certain Tagger.