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README.md
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: mdeberta-v3-base-subjectivity-sentiment-multilingual-no-arabic
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mdeberta-v3-base-subjectivity-sentiment-multilingual-no-arabic
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This model is a fine-tuned version of [](https://huggingface.co/) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.8900
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- Macro F1: 0.7969
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- Transformers 4.47.0
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- Pytorch 2.5.1+cu121
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- Datasets 3.3.1
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- Tokenizers 0.21.0
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- generated_from_trainer
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metrics:
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- accuracy
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- f1
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model-index:
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- name: mdeberta-v3-base-subjectivity-sentiment-multilingual-no-arabic
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results: []
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license: mit
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base_model:
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- microsoft/mdeberta-v3-base
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pipeline_tag: text-classification
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mdeberta-v3-base-subjectivity-sentiment-multilingual-no-arabic
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the [CheckThat! Lab Task 1 Subjectivity Detection at CLEF 2025](arxiv.org/abs/2507.11764).
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It achieves the following results on the evaluation set:
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- Loss: 0.8900
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- Macro F1: 0.7969
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- Transformers 4.47.0
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- Pytorch 2.5.1+cu121
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- Datasets 3.3.1
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- Tokenizers 0.21.0
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