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🚀 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