Text Classification
Transformers
Safetensors
multilingual
deberta-v2
subjectivity-detection
news-articles
deberta-v3
Generated from Trainer
Instructions to use AIWizards/mdeberta-v3-base-subjectivity-multilingual-no-arabic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIWizards/mdeberta-v3-base-subjectivity-multilingual-no-arabic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AIWizards/mdeberta-v3-base-subjectivity-multilingual-no-arabic")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AIWizards/mdeberta-v3-base-subjectivity-multilingual-no-arabic") model = AutoModelForSequenceClassification.from_pretrained("AIWizards/mdeberta-v3-base-subjectivity-multilingual-no-arabic") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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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-multilingual-no-arabic
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results: []
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# mdeberta-v3-base-subjectivity-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
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It achieves the following results on the evaluation set:
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- Loss: 0.7196
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- Macro F1: 0.8071
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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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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-multilingual-no-arabic
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results: []
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# mdeberta-v3-base-subjectivity-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: 0.7196
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- Macro F1: 0.8071
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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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