|
--- |
|
tags: |
|
- generated_from_keras_callback |
|
model-index: |
|
- name: US_politicians_covid_skepticism |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should |
|
probably proofread and complete it, then remove this comment. --> |
|
|
|
|
|
|
|
This model is a fine-tuned version of [vinai/bertweet-covid19-base-uncased](https://huggingface.co/vinai/bertweet-covid19-base-uncased) on a dataset of 10k tweets about COVID-19 policies from US legislators in the House and Senate. |
|
|
|
The model is intended to identify skepticism of COVID-19 policies (i.e. masks, social distancing, lockdowns, vaccines etc.). |
|
|
|
|
|
|
|
It's a pretty simple task but I used a grid search to optimize hyperparameters. The model uses the following hyperparamters: |
|
|
|
**Optimized Hyperparameters** |
|
- The best learning rate is: 9.928559980965476e-06 |
|
- The best weight decay is: 0.003083325125091835 |
|
- The best epoch is : 5 |
|
- The best train split is : 0.2864649363822965 |
|
|
|
**Training** |
|
- Train Loss: 0.1007 |
|
- Train Sparse Categorical Accuracy: 0.9591 |
|
- Validation Loss: 0.0913 |
|
- Validation Sparse Categorical Accuracy: 0.9627 |
|
- Optimizer: Adam |
|
- Starting Learn rate: 5e-07 |
|
|
|
|
|
|
