|
--- |
|
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). |
