YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/model-cards#model-card-metadata)
TextAttack Model Card
bert-base-uncased model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the
nlp library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.7256317689530686, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out TextAttack on Github.
- Downloads last month
This model can be loaded on the Inference API on-demand.