---
license: mit
metrics:
- accuracy
pipeline_tag: text-classification
datasets:
- Dcolinmorgan/disaster-tw
widget:
- text: "Waves are crashing all around, the wind is growing stronger, lights are flashing."
  example_title: "Storm description without keyword"
- text: "A cool breeze is passing through the meadow and over the brook tonight"
  example_title: "No emergency"
- text: "Blood was scene at the corner of X and Y"
  example_title: "Emergency scene"
- text: "Two men were seen at the corner of X and Y"
  example_title: "Normal scene"
---

## Model Name

distaster inference from tweets trained locally on M1 max via mlx

bert-base-uncased with labels tuned to 2

### Training

Description of the training process.

### Evaluation

epoch and accuracy based.

### Weights & Biases

This model was trained and evaluated using Weights & Biases. You can see the training and evaluation logs [here](https://api.wandb.ai/links/dcolinmorgan/tpeiht5r).