metadata

tags:
  - generated_from_trainer
datasets:
  - imdb
metrics:
  - accuracy
base_model: textattack/bert-base-uncased-imdb
model-index:
  - name: baseline
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: imdb
          type: imdb
          config: plain_text
          split: test
          args: plain_text
        metrics:
          - type: accuracy
            value: 0.92088
            name: Accuracy

baseline

This model is a fine-tuned version of textattack/bert-base-uncased-imdb on the imdb dataset. It achieves the following results on the evaluation set:

Loss: 0.5238
Accuracy: 0.9209

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0
mixed_precision_training: Native AMP

Training script

python run_glue.py \
  --model_name_or_path textattack/bert-base-uncased-imdb \
  --dataset_name imdb  \
  --do_train \
  --do_eval \
  --max_seq_length 384 \
  --pad_to_max_length False \
  --per_device_train_batch_size 32 \
  --per_device_eval_batch_size 32 \
  --fp16 \
  --learning_rate 5e-5 \
  --optim adamw_torch \
  --num_train_epochs 3 \
  --overwrite_output_dir \
  --output_dir /tmp/bert-base-uncased-imdb

Note: run_glue.py is modified to set the "test" split as evaluation dataset.

Framework versions

Transformers 4.27.4
Pytorch 1.13.1
Datasets 2.11.0
Tokenizers 0.13.3