Instructions to use ctoraman/deprem-mdeberta-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ctoraman/deprem-mdeberta-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ctoraman/deprem-mdeberta-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ctoraman/deprem-mdeberta-binary") model = AutoModelForSequenceClassification.from_pretrained("ctoraman/deprem-mdeberta-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - tr | |
| tags: | |
| - mdeberta | |
| license: cc-by-nc-sa-4.0 | |
| # deprem-mdeberta-binary | |
| Fine-tuned mDeBERTa model for binary tweet classification detecting if tweet calls for help after the earthquake disaster. The model was trained using the tweets posted in the first 12 hours of the 2023 Turkey-Syria Earthquake. | |
| The dataset and other details can be found at: | |
| https://github.com/avaapm/deprem | |
| ### BibTeX entry and citation info | |
| ```bibtex | |
| @misc{toraman2023earthquake, | |
| doi = {10.48550/ARXIV.2302.13403}, | |
| url = {https://arxiv.org/abs/2302.13403}, | |
| author = {Toraman, Cagri and Kucukkaya, Izzet Emre and Ozcelik, Oguzhan and Sahin, Umitcan}, | |
| keywords = {Social and Information Networks (cs.SI), Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences}, | |
| title = {Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster}, | |
| publisher = {arXiv}, | |
| year = {2023}, | |
| copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International} | |
| } | |
| ``` | |