## TextAttack Model Card
This `albert-base-v2` 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 64, a learning 
rate of 3e-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 0.776173285198556, as measured by the 
eval set accuracy, found after 4 epochs.

For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).

{}
transformers pytorch albert text-classification autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us 

## TextAttack Model Card
This 'albert-base-v2' 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 64, a learning 
rate of 3e-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 0.776173285198556, as measured by the 
eval set accuracy, found after 4 epochs.

For more information, check out TextAttack on Github.

## TextAttack Model Card
This 'albert-base-v2' 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 64, a learning 
rate of 3e-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 0.776173285198556, as measured by the 
eval set accuracy, found after 4 epochs.

For more information, check out TextAttack on Github.
TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ## TextAttack Model Card
This 'albert-base-v2' 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 64, a learning 
rate of 3e-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 0.776173285198556, as measured by the 
eval set accuracy, found after 4 epochs.

For more information, check out TextAttack on Github.
passage: TAGS
#transformers #pytorch #albert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us 
## TextAttack Model Card
This 'albert-base-v2' 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 64, a learning 
rate of 3e-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 0.776173285198556, as measured by the 
eval set accuracy, found after 4 epochs.

For more information, check out TextAttack on Github.