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@@ -23,6 +23,8 @@ It can be used for `topic classification`, `sentiment analysis` and as a reranke
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  The model was trained on synthetic data and can be used in commercial applications.
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  ### How to use:
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  First of all, you need to install GLiClass library:
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  ```bash
@@ -34,8 +36,8 @@ Than you need to initialize a model and a pipeline:
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  from gliclass import GLiClassModel, ZeroShotClassificationPipeline
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  from transformers import AutoTokenizer
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- model = GLiClassModel.from_pretrained("knowledgator/gliclass-small-v1.0")
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- tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-small-v1.0")
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  pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')
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  The model was trained on synthetic data and can be used in commercial applications.
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+ This model wasn't additionally fine-tuned on any dataset except initial (MoritzLaurer/synthetic_zeroshot_mixtral_v0.1).
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+
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  ### How to use:
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  First of all, you need to install GLiClass library:
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  ```bash
 
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  from gliclass import GLiClassModel, ZeroShotClassificationPipeline
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  from transformers import AutoTokenizer
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+ model = GLiClassModel.from_pretrained("knowledgator/gliclass-small-v1.0-init")
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+ tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-small-v1.0-init")
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  pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')
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