17 9 121

Stepanov

Ihor

https://knowledgator.com/

ihor_step

Ingvarstep

AI & ML interests

Text classification, computational biology, relations extraction, path reasoning

Organizations

Posts 3

Post

505

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"])

Post

780

We are super happy to contribute to the GLiNER ecosystem by optimizing training code and releasing a multi-task, prompt-tunable model.

The model can be used for the following tasks:
* Named entity recognition (NER);
* Open information extraction;
* Question answering;
* Relation extraction;
* Summarization;

Model: knowledgator/gliner-multitask-large-v0.5
Demo: knowledgator/GLiNER_HandyLab
Repo: 👨‍💻 https://github.com/urchade/GLiNER

**How to use**
First of all, install gliner package.

pip install gliner

Then try the following code:

from gliner import GLiNER

model = GLiNER.from_pretrained("knowledgator/gliner_small-v2.1")

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"])

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Papers 1

arxiv:2406.12925

models 1

Ihor/DILI-scibert

Text Classification • Updated Dec 24, 2023 • 10 • 1

datasets

None public yet