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README.md
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- sentiment extraction
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- question-answering
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---
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π 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"], β=>β, 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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π Meet the first multi-task prompt-tunable GLiNER model π
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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"], β=>β, 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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