Text Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
subjectivity
newspapers
CLEF2023
text-embeddings-inference
Instructions to use GroNLP/mdebertav3-subjectivity-english with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GroNLP/mdebertav3-subjectivity-english with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GroNLP/mdebertav3-subjectivity-english")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GroNLP/mdebertav3-subjectivity-english") model = AutoModelForSequenceClassification.from_pretrained("GroNLP/mdebertav3-subjectivity-english") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5bedadda417df957cf21e9a30aa6e7461ec27e2864410df312f84537f6a61620
- Size of remote file:
- 1.11 GB
- SHA256:
- b91528b4f5d34216879dc87d30f6b2f330d98f146cb86758148fa2c47bf8f27c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.