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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b29bbd80484c0e8197e838cad6bd66ef4d9ff4afcc63f9b9e2b5102f8080bafe
- Size of remote file:
- 1.11 GB
- SHA256:
- 05ad6087c029428927735d51029caa5a4774963b0dee29a8e399b140ad1be5e2
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