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
What are the labels indicating?
#2
by DanielBischoff - opened
Hi! This looks great — thank you for uploading the model. Could you please clarify what the labels represent?
Hi! The labels refer to whether a sentence from a news article is written in a subjective or objective style.
Refs:
Hi, and thank you for the quick response!
Just to confirm—does this mean that 'label0' corresponds to objective and 'label1' to subjective?
I got this result from the Hugging Face pipeline.
Best regards,
Daniel
Hi Daniel,
correct:
- 0 = objective
- 1 = subjective
Hi tommasoc,
thank you. That helps a lot.
gsarti changed discussion status to closed