Instructions to use g-assismoraes/deberta-semeval25task10-aya2en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use g-assismoraes/deberta-semeval25task10-aya2en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="g-assismoraes/deberta-semeval25task10-aya2en")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("g-assismoraes/deberta-semeval25task10-aya2en") model = AutoModelForSequenceClassification.from_pretrained("g-assismoraes/deberta-semeval25task10-aya2en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
deberta-semeval25-fulltrain-translateen
This model is a fine-tuned version of microsoft/deberta-v3-base on the "Semeval-2025 Task 10, Subtask 2" dataset.
Model description
This model was trained for multi-label classification in the "SemEval 2025 Task 10, Subtask 2" taxonomy, with article texts translated into English by the Aya Expanse 8B model.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for g-assismoraes/deberta-semeval25task10-aya2en
Base model
microsoft/deberta-v3-base