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
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 Italian development dataset, for which the following (hyper)parameters were utilized:
Batch Size = 32
Max Epochs = 2
Learning Rate = 5e-5
Warmup Steps = 300
Weight Decay = 0
The model ranked first in the CheckThat! Lab and obtained a macro F1 of 0.76 and a SUBJ F1 of 0.65.
- Downloads last month
- 13