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") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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license: cc-by-nc-sa-4.0
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---
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---
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language:
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- tr
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tags:
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- mdeberta
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license: cc-by-nc-sa-4.0
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---
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# deprem-mdeberta-binary
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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.
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The dataset and other details can be found at:
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https://github.com/avaapm/deprem
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### BibTeX entry and citation info
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```bibtex
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@InProceedings{toraman2023earthquake,
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author = {Toraman, Cagri and Kucukkaya, Izzet Emre and Ozcelik, Oguzhan and Sahin, Umitcan},
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title = {Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster},
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year = {2023},
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eprint = {xxxx.xxxxx},
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archivePrefix = {arXiv},
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primaryClass = {cs.SI}
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}
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```
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