Multitask Models
| Name | Encoder | Size (GB) | Zero-Shot F1 Score▼ |
|---|---|---|---|
| gliner-multitask-v1.0 | deberta-v2-xlarge | 3.64 | 0.6325 |
| gliner-multitask-large-v0.5 | deberta-v3-large | 1.76 | 0.6276 |
| gliner-llama-multitask-1B-v1.0 | Llama-encoder-1.0B | 4.24 | 0.6153 |
Classification
from gliner import GLiNER
from gliner.multitask import GLiNERClassifier
model_id = 'knowledgator/gliner-multitask-v1.0'
model = GLiNER.from_pretrained(model_id)
classifier = GLiNERClassifier(model=model)
text = "SpaceX successfully launched a new rocket into orbit."
labels = ['science', 'technology', 'business', 'sports']
predictions = classifier(text, classes=labels, multi_label=False)
print(predictions)
Expected Output
[[{'label': 'technology', 'score': 0.3839840292930603}]]
Question-Answering
from gliner import GLiNER
from gliner.multitask import GLiNERQuestionAnswerer
model_id = 'knowledgator/gliner-multitask-v1.0'
model = GLiNER.from_pretrained(model_id)
answerer = GLiNERQuestionAnswerer(model=model)
text = "SpaceX successfully launched a new rocket into orbit."
question = 'Which company launched a new rocket?'
predictions = answerer(text, questions=question)
print(predictions)
Expected Output
[[{'answer': 'SpaceX', 'score': 0.998126208782196}]]
Relation Extraction
from gliner import GLiNER
from gliner.multitask import GLiNERRelationExtractor
model_id = 'knowledgator/gliner-multitask-v1.0'
model = GLiNER.from_pretrained(model_id)
relation_extractor = GLiNERRelationExtractor(model=model)
text = "Elon Musk founded SpaceX in 2002 to reduce space transportation costs."
relations = ['founded', 'owns', 'works for']
entities = ['person', 'company', 'year']
predictions = relation_extractor(text, entities=entities, relations=relations)
print(predictions)
Expected Output
[[{'source': 'Elon Musk', 'relation': 'founded', 'target': 'SpaceX', 'score': 0.9583475589752197}]]
Open Information Extraction
from gliner import GLiNER
from gliner.multitask import GLiNEROpenExtractor
model_id = 'knowledgator/gliner-multitask-v1.0'
model = GLiNER.from_pretrained(model_id)
extractor = GLiNEROpenExtractor(model=model, prompt="Extract all companies related to space technologies")
text = "Elon Musk founded SpaceX in 2002 to reduce space transportation costs. Also Elon is founder of Tesla, NeuroLink and many other companies."
labels = ['company']
predictions = extractor(text, labels=labels)
print(predictions)
Expected Output
[[{'start': 72, 'end': 78, 'text': 'SpaceX', 'label': 'company', 'score': 0.9622299075126648}, {'start': 149, 'end': 154, 'text': 'Tesla', 'label': 'company', 'score': 0.9357912540435791}, {'start': 156, 'end': 165, 'text': 'NeuroLink', 'label': 'company', 'score': 0.9122058749198914}]]
Summarization
from gliner import GLiNER
from gliner.multitask import GLiNERSummarizer
model_id = 'knowledgator/gliner-multitask-v1.0'
model = GLiNER.from_pretrained(model_id)
summarizer = GLiNERSummarizer(model=model)
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."
summary = summarizer(text, threshold=0.1)
print(summary)
Expected Output
['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.']