Text Classification
Transformers
PyTorch
Safetensors
Italian
deberta-v2
subjectivity
newspapers
CLEF2023
text-embeddings-inference
Instructions to use GroNLP/mdebertav3-subjectivity-italian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GroNLP/mdebertav3-subjectivity-italian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GroNLP/mdebertav3-subjectivity-italian")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GroNLP/mdebertav3-subjectivity-italian") model = AutoModelForSequenceClassification.from_pretrained("GroNLP/mdebertav3-subjectivity-italian") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0220717f48703065e8d780e5eb5d30ceb5fa590f2a160e06c9cec96d297f5b3
- Size of remote file:
- 1.11 GB
- SHA256:
- c615a874a1986de1790d4bb01b1a78870429262c8ed6cb7008a0fcaaeed4b790
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