pip install gliner

from gliner import GLiNER

model = GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5")

text = """
Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014.
"""

labels = ["founder", "computer", "software", "position", "date"]

entities = model.predict_entities(text, labels)

for entity in entities:
    print(entity["text"], "=>", entity["label"])

text = """
Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014.
"""

labels = ["Microsoft <> founder", "Microsoft <> inception date", "Bill Gates <> held position"]

entities = model.predict_entities(text, labels)

for entity in entities:
    print(entity["label"], "=>", entity["text"])

from utca.core import RenameAttribute
from utca.implementation.predictors import (
    GLiNERPredictor,
    GLiNERPredictorConfig
)
from utca.implementation.tasks import (
    GLiNER,
    GLiNERPreprocessor,
    GLiNERRelationExtraction,
    GLiNERRelationExtractionPreprocessor,
)

predictor = GLiNERPredictor( # Predictor manages the model that will be used by tasks
    GLiNERPredictorConfig(
        model_name = "knowledgator/gliner-multitask-large-v0.5", # Model to use
        device = "cuda:0", # Device to use
    )
)

pipe = (
    GLiNER( # GLiNER task produces classified entities that will be at the "output" key.
        predictor=predictor,
        preprocess=GLiNERPreprocessor(threshold=0.7) # Entities threshold
    ) 
    | RenameAttribute("output", "entities") # Rename output entities from GLiNER task to use them as inputs in GLiNERRelationExtraction
    | GLiNERRelationExtraction( # GLiNERRelationExtraction is used for relation extraction.
        predictor=predictor,
        preprocess=(
            GLiNERPreprocessor(threshold=0.5) # Relations threshold
            | GLiNERRelationExtractionPreprocessor()
        )
    )
)

r = pipe.run({
    "text": text, # Text to process
    "labels": ["organisation", "founder", "position", "date"],
    "relations": [{ # Relation parameters
        "relation": "founder", # Relation label. Required parameter.
        "pairs_filter": [("organisation", "founder")], # Optional parameter. It specifies possible members of relations by their entity labels.
        "distance_threshold": 100, # Optional parameter. It specifies the max distance between spans in the text (i.e., the end of the span that is closer to the start of the text and the start of the next one).
    }, {
        "relation": "inception date",
        "pairs_filter": [("organisation", "date")],
    }, {
        "relation": "held position",
        "pairs_filter": [("founder", "position")],
    }]
})

print(r["output"])

prompt = """Find all positive aspects about the product:\n"""
text = """
I recently purchased the Sony WH-1000XM4 Wireless Noise-Canceling Headphones from Amazon and I must say, I'm thoroughly impressed. The package arrived in New York within 2 days, thanks to Amazon Prime's expedited shipping.

The headphones themselves are remarkable. The noise-canceling feature works like a charm in the bustling city environment, and the 30-hour battery life means I don't have to charge them every day. Connecting them to my Samsung Galaxy S21 was a breeze, and the sound quality is second to none.

I also appreciated the customer service from Amazon when I had a question about the warranty. They responded within an hour and provided all the information I needed.

However, the headphones did not come with a hard case, which was listed in the product description. I contacted Amazon, and they offered a 10% discount on my next purchase as an apology.

Overall, I'd give these headphones a 4.5/5 rating and highly recommend them to anyone looking for top-notch quality in both product and service.
"""

input_ = prompt+text

labels = ["match"]

matches = model.predict_entities(input_, labels)

for match in matches:
    print(match["text"], "=>", match["score"])

question = "Who was the CEO of Microsoft?"
text = """
Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014.
"""

labels = ["answer"]

input_ = question+text
answers = model.predict_entities(input_, labels)

for answer in answers:
    print(answer["text"], "=>", answer["score"])

prompt = "Summarize the given text, highlighting the most important information:\n"

text = """
Several studies have reported its pharmacological activities, including anti-inflammatory, antimicrobial, and antitumoral effects.
The effect of E-anethole was studied in the osteosarcoma MG-63 cell line, and the antiproliferative activity was evaluated by an MTT assay.
It showed a GI50 value of 60.25 μM with apoptosis induction through the mitochondrial-mediated pathway. Additionally, it induced cell cycle arrest at the G0/G1 phase, up-regulated the expression of p53, caspase-3, and caspase-9, and down-regulated Bcl-xL expression.
Moreover, the antitumoral activity of anethole was assessed against oral tumor Ca9-22 cells, and the cytotoxic effects were evaluated by MTT and LDH assays.
It demonstrated a LD50 value of 8 μM, and cellular proliferation was 42.7% and 5.2% at anethole concentrations of 3 μM and 30 μM, respectively.
It was reported that it could selectively and in a dose-dependent manner decrease cell proliferation and induce apoptosis, as well as induce autophagy, decrease ROS production, and increase glutathione activity. The cytotoxic effect was mediated through NF-kB, MAP kinases, Wnt, caspase-3 and -9, and PARP1 pathways. Additionally, treatment with anethole inhibited cyclin D1 oncogene expression, increased cyclin-dependent kinase inhibitor p21WAF1, up-regulated p53 expression, and inhibited the EMT markers.
"""

labels = ["summary"]

input_ = prompt+text

threshold = 0.5
summaries = model.predict_entities(input_, labels, threshold=threshold)

for summary in summaries:
    print(summary["text"], "=>", summary["score"])

@misc{stepanov2024gliner,
      title={GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks}, 
      author={Ihor Stepanov and Mykhailo Shtopko},
      year={2024},
      eprint={2406.12925},
      archivePrefix={arXiv},
      primaryClass={id='cs.LG' full_name='Machine Learning' is_active=True alt_name=None in_archive='cs' is_general=False description='Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.'}
}