knowledgator/gliner-multitask-large-v0.5
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Knowledgator Engineering
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Text classification, relations extraction, NER, computational biology
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Add the `modernbert` tag
#4 opened 23 days ago
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tomaarsen
Add the `modernbert` tag
1
#5 opened 23 days ago
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tomaarsen
Ihor
updated
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model
28 days ago
Ihor
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a
dataset
about 1 month ago
Request for Sharing Training Scripts and Datasets for Academic Purposes
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#2 opened about 1 month ago
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Hrithik2212
Spaces in tokens
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#1 opened about 1 month ago
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johnowhitaker
Ihor
updated
2
models
about 1 month ago
Add base model metadata
#1 opened about 1 month ago
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davanstrien
Ihor
updated
a
collection
about 1 month ago
BioMike
updated
a
model
about 2 months ago
Post
1011
🚀 Welcome the New and Improved GLiNER-Multitask! 🚀
Since the release of our beta version, GLiNER-Multitask has received many positive responses. It's been embraced in many consulting, research, and production environments. Thank you everyone for your feedback, it helped us rethink the strengths and weaknesses of the first model and we are excited to present the next iteration of this multi-task information extraction model.
💡 What’s New?
Here are the key improvements in this latest version:
🔹 Expanded Task Support: Now includes text classification and other new capabilities.
🔹 Enhanced Relation Extraction: Significantly improved accuracy and robustness.
🔹 Improved Prompt Understanding: Optimized for open-information extraction tasks.
🔹 Better Named Entity Recognition (NER): More accurate and reliable results.
🔧 How We Made It Better:
These advancements were made possible by:
🔹 Leveraging a better and more diverse dataset.
🔹 Using a larger backbone model for increased capacity.
🔹 Implementing advanced model merging techniques.
🔹 Employing self-learning strategies for continuous improvement.
🔹 Better training strategies and hyperparameters tuning.
📄 Read the Paper: https://arxiv.org/abs/2406.12925
⚙️ Try the Model: knowledgator/gliner-multitask-v1.0
💻 Test the Demo: knowledgator/GLiNER_HandyLab
📌 Explore the Repo: https://github.com/urchade/GLiNER
Since the release of our beta version, GLiNER-Multitask has received many positive responses. It's been embraced in many consulting, research, and production environments. Thank you everyone for your feedback, it helped us rethink the strengths and weaknesses of the first model and we are excited to present the next iteration of this multi-task information extraction model.
💡 What’s New?
Here are the key improvements in this latest version:
🔹 Expanded Task Support: Now includes text classification and other new capabilities.
🔹 Enhanced Relation Extraction: Significantly improved accuracy and robustness.
🔹 Improved Prompt Understanding: Optimized for open-information extraction tasks.
🔹 Better Named Entity Recognition (NER): More accurate and reliable results.
🔧 How We Made It Better:
These advancements were made possible by:
🔹 Leveraging a better and more diverse dataset.
🔹 Using a larger backbone model for increased capacity.
🔹 Implementing advanced model merging techniques.
🔹 Employing self-learning strategies for continuous improvement.
🔹 Better training strategies and hyperparameters tuning.
📄 Read the Paper: https://arxiv.org/abs/2406.12925
⚙️ Try the Model: knowledgator/gliner-multitask-v1.0
💻 Test the Demo: knowledgator/GLiNER_HandyLab
📌 Explore the Repo: https://github.com/urchade/GLiNER
Ihor
updated
2
models
about 2 months ago
Post
397
🚀 Let’s transform LLMs into encoders 🚀
Auto-regressive LMs have ruled, but encoder-based architectures like GLiNER are proving to be just as powerful for information extraction while offering better efficiency and interpretability. 🔍✨
Past encoder backbones were limited by small pre-training datasets and old techniques, but with innovations like LLM2Vec, we've transformed decoders into high-performing encoders! 🔄💡
What’s New?
🔹Converted Llama & Qwen decoders to advanced encoders
🔹Improved GLiNER architecture to be able to work with rotary positional encoding
🔹New GLiNER (zero-shot NER) & GLiClass (zero-shot classification) models
🔥 Check it out:
New models: knowledgator/llm2encoder-66d1c76e3c8270397efc5b5e
GLiNER package: https://github.com/urchade/GLiNER
GLiClass package: https://github.com/Knowledgator/GLiClass
💻 Read our blog for more insights, and stay tuned for what’s next!
https://medium.com/@knowledgrator/llm2encoders-e7d90b9f5966
Auto-regressive LMs have ruled, but encoder-based architectures like GLiNER are proving to be just as powerful for information extraction while offering better efficiency and interpretability. 🔍✨
Past encoder backbones were limited by small pre-training datasets and old techniques, but with innovations like LLM2Vec, we've transformed decoders into high-performing encoders! 🔄💡
What’s New?
🔹Converted Llama & Qwen decoders to advanced encoders
🔹Improved GLiNER architecture to be able to work with rotary positional encoding
🔹New GLiNER (zero-shot NER) & GLiClass (zero-shot classification) models
🔥 Check it out:
New models: knowledgator/llm2encoder-66d1c76e3c8270397efc5b5e
GLiNER package: https://github.com/urchade/GLiNER
GLiClass package: https://github.com/Knowledgator/GLiClass
💻 Read our blog for more insights, and stay tuned for what’s next!
https://medium.com/@knowledgrator/llm2encoders-e7d90b9f5966
Post
738
🚀 Meet the new GLiNER architecture 🚀
GLiNER revolutionized zero-shot NER by demonstrating that lightweight encoders can achieve excellent results. We're excited to continue R&D with this spirit 🔥. Our new bi-encoder and poly-encoder architectures were developed to address the main limitations of the original GLiNER architecture and bring the following new possibilities:
🔹 An unlimited number of entities can be recognized at once.
🔹Faster inference when entity embeddings are preprocessed.
🔹Better generalization to unseen entities.
While the bi-encoder architecture can lack inter-label understanding, we developed a poly-encoder architecture with post-fusion. It achieves the same or even better results on many benchmarking datasets compared to the original GLiNER, while still offering the listed advantages of bi-encoders.
Now, it’s possible to run GLiNER with hundreds of entities much faster and more reliably.
📌 Try the new models here:
knowledgator/gliner-bi-encoders-66c492ce224a51c54232657b
GLiNER revolutionized zero-shot NER by demonstrating that lightweight encoders can achieve excellent results. We're excited to continue R&D with this spirit 🔥. Our new bi-encoder and poly-encoder architectures were developed to address the main limitations of the original GLiNER architecture and bring the following new possibilities:
🔹 An unlimited number of entities can be recognized at once.
🔹Faster inference when entity embeddings are preprocessed.
🔹Better generalization to unseen entities.
While the bi-encoder architecture can lack inter-label understanding, we developed a poly-encoder architecture with post-fusion. It achieves the same or even better results on many benchmarking datasets compared to the original GLiNER, while still offering the listed advantages of bi-encoders.
Now, it’s possible to run GLiNER with hundreds of entities much faster and more reliably.
📌 Try the new models here:
knowledgator/gliner-bi-encoders-66c492ce224a51c54232657b
-
- 4 replies
Post
893
🚀 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
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
Ihor
authored
a
paper
7 months ago
Post
597
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
🔍 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"])