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")# pip install -U transformers accelerate # 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
Download tokenizer.json from GroNLP/mdebertav3-subjectivity-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/GroNLP/mdebertav3-subjectivity-multilingual/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://GroNLP/mdebertav3-subjectivity-multilingual@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/GroNLP/mdebertav3-subjectivity-multilingual/resolve/refs%2Fpr%2F1/tokenizer.json
16.3 MB
- Xet hash:
- 554465a1bd6e2f9a480e8a05a72932b018ba45bd29466734c2b93d60ac795f6b
- Size of remote file:
- 16.3 MB
- SHA256:
- 45a824c25ee5199ac5e147a212fa0a1dfdae87b0329ce1477390a68b15e1b223
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