Text Classification
Transformers
PyTorch
Safetensors
German
deberta-v2
subjectivity
newspapers
CLEF2023
text-embeddings-inference
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("GroNLP/mdebertav3-subjectivity-german")
model = AutoModelForSequenceClassification.from_pretrained("GroNLP/mdebertav3-subjectivity-german", device_map="auto")Quick Links
Fine-tuned mDeBERTa V3 model for subjectivity detection in newspaper sentences. This model was developed as part of the CLEF 2023 CheckThat! Lab Task 2: Subjectivity in News Articles.
The goal in this task is to detect whether a sentence is objective (OBJ) or subjective (SUBJ). A sentence is subjective if its content is based on or influenced by personal feelings, tastes, or opinions. Otherwise, the sentence is objective. (Antici et al., 2023).
The model was fine-tuned using a multilingual training and German development dataset, for which the following (hyper)parameters were utilized:
Batch Size = 16
Max Epochs = 5
Learning Rate = 4e-5
Warmup Steps = 100
Weight Decay = 0.2
The model ranked first in the CheckThat! Lab and obtained a macro F1 of 0.82 and a SUBJ F1 of 0.77.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GroNLP/mdebertav3-subjectivity-german")