Ihor (Stepanov)

Posts 4

Post

782

🚀 Meet Our New Line of Efficient and Accurate Zero-Shot Classifiers! 🚀

The new architecture brings better inter-label understanding and can solve complex classification tasks at a single forward pass.

Key Applications:
✅ Multi-class classification (up to 100 classes in a single run)
✅ Topic classification
✅ Sentiment analysis
✅ Event classification
✅ Prompt-based constrained classification
✅ Natural Language Inference
✅ Multi- and single-label classification

knowledgator/gliclass-6661838823756265f2ac3848
knowledgator/GLiClass_SandBox
knowledgator/gliclass-base-v1.0-lw

Post

564

We’re thrilled to share our latest technical paper on the multi-task GLiNER model. Our research dives into the following exciting and forward-thinking topics:

🔍 Zero-shot NER & Information Extraction: We demonstrate that with diverse and ample data, paired with the right architecture, encoders can achieve impressive results across various extraction tasks;

🛠️ Synthetic Data Generation: Leveraging open labelling by LLMs like Llama, we generated high-quality training data. Our student model even outperformed the teacher model, highlighting the potential of this approach.

🤖 Self-Learning: Our model showed consistent improvements in performance without labelled data, achieving up to a 12% increase in F1 score for initially challenging topics. This ability to learn and improve autonomously is a very perspective direction of future research!

GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks (2406.12925)
knowledgator/gliner-multitask-large-v0.5
knowledgator/GLiNER_HandyLab

#!pip install gliner -U

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. 
"""

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

entities = model.predict_entities(text, labels)

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