Text Classification
Transformers
PyTorch
Safetensors
deberta-v2
subjectivity
newspapers
CLEF2023
text-embeddings-inference
Instructions to use GroNLP/mdebertav3-subjectivity-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GroNLP/mdebertav3-subjectivity-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GroNLP/mdebertav3-subjectivity-multilingual")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GroNLP/mdebertav3-subjectivity-multilingual") model = AutoModelForSequenceClassification.from_pretrained("GroNLP/mdebertav3-subjectivity-multilingual") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 554465a1bd6e2f9a480e8a05a72932b018ba45bd29466734c2b93d60ac795f6b
- Size of remote file:
- 16.3 MB
- SHA256:
- 45a824c25ee5199ac5e147a212fa0a1dfdae87b0329ce1477390a68b15e1b223
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