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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# baseline

This model is a fine-tuned version of [textattack/bert-base-uncased-imdb](https://huggingface.co/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

```bash
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