Text Classification
Transformers
Joblib
Safetensors
English
bert
disaster-response
sdg11
huggingface
text-embeddings-inference
Instructions to use elam2909/bert-disaster-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elam2909/bert-disaster-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="elam2909/bert-disaster-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("elam2909/bert-disaster-classifier") model = AutoModelForSequenceClassification.from_pretrained("elam2909/bert-disaster-classifier") - Notebooks
- Google Colab
- Kaggle
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- **Base model:** `bert-base-uncased`
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- **Fine-tuned on:** Disaster Tweet Classification Dataset (binary classification: disaster vs. not disaster)
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- **Framework:** Hugging Face Transformers + PyTorch
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## Training Dataset
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- **Base model:** `bert-base-uncased`
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- **Fine-tuned on:** Disaster Tweet Classification Dataset (binary classification: disaster vs. not disaster)
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- **Framework:** Hugging Face Transformers + PyTorch
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- **Post-BERT Classifier:** Random Forest trained on BERT [CLS] embeddings
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## Training Dataset
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