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            - sentiment extraction
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            - question-answering
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            ---
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            🚀 Meet the first multi-task prompt- | 
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            **GLiNER-Multitask** is a model designed to extract various pieces of information from plain text based on a user-provided custom prompt. This versatile model leverages a bidirectional transformer encoder, similar to BERT, which ensures both high generalization and compute efficiency despite its compact size.
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                print(entity["label"], “=>”, entity["text"])
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            ```
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            ### Construct relations extraction pipeline with [utca](https://github.com/Knowledgator/utca)
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            First of all, we need import neccessary components of the library and initalize predictor - GLiNER model and construct pipeline that  | 
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            ```python
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            from utca.core import RenameAttribute
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            from utca.implementation.predictors import (
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            - sentiment extraction
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            - question-answering
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            ---
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            🚀 Meet the first multi-task prompt-tunable GLiNER model 🚀
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            **GLiNER-Multitask** is a model designed to extract various pieces of information from plain text based on a user-provided custom prompt. This versatile model leverages a bidirectional transformer encoder, similar to BERT, which ensures both high generalization and compute efficiency despite its compact size.
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                print(entity["label"], “=>”, entity["text"])
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            ```
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            ### Construct relations extraction pipeline with [utca](https://github.com/Knowledgator/utca)
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            First of all, we need import neccessary components of the library and initalize predictor - GLiNER model and construct pipeline that combines NER and realtions extraction:
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            ```python
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            from utca.core import RenameAttribute
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            from utca.implementation.predictors import (
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