KhanEason/bert-disaster-tweets-nlp-hw
text-classification model by KhanEason
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Model card & documentation
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Actually full-fine-tuned BERT-base-uncased with 109,483,778 trainable parameters. Labels: 0 Non-disaster, 1 Disaster. This is text classification, not independent event fact-checking. Data: Kaggle nlp-getting-started, mirrored at gdwangh/kaggle-nlp-getting-start revision 6b37397565bdbd6ede10e362e6a1be4c62083bb3. The labeled train.csv was deduplicated before stratified 80/10/10 splitting with seed 42. Train/validation/local test counts: 5986/748/749. Official competition test.csv is unlabeled and excluded from evaluation. Training: 3 epochs, batch 16, LR 2e-5, AdamW, weight decay .01, linear warmup .1, max length 128, BF16, one A100 80GB. Best epoch 3 by validation Disaster F1. Local test accuracy 82.64%,…
Read the full model card ↗ · Preview checked 2026-10-29T08:21:46.284Z
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Model overview
KhanEason/bert-disaster-tweets-nlp-hw is a text-classification model repository published by KhanEason on Hugging Face. A library was not reported.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- text-classification
- Library
- Not reported
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 109,483,778
- Architecture
- BertForSequenceClassification
- License
- Not reported — check the model card
- Access
- Not gated by Hugging Face
- Created
- 2026-10-29T06:15:18.000Z
- Last modified
- 2026-10-29T06:18:59.000Z
Compatibility and lineage
Reported languages: en
Reported base models: google-bert/bert-base-uncased
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-29T06:48:44.800Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.