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# Text classification examples

## Training Adapters in PyTorch

Based on scripts `run_glue.py` and `run_glue_alt.py` (using model classes with flexible heads).

By specifying a few additional, adapter-specific flags, you can easily switch from fine-tuning a full model to training Adapter modules on GLUE:

```bash
export TASK_NAME=mrpc

python run_glue_alt.py \
  --model_name_or_path bert-base-uncased \
  --task_name $TASK_NAME \
  --do_train \
  --do_eval \
  --max_seq_length 128 \
  --per_device_train_batch_size 32 \
  --learning_rate 1e-4 \
  --num_train_epochs 10.0 \
  --output_dir /tmp/$TASK_NAME \
  --overwrite_output_dir \
  --train_adapter \
  --adapter_config pfeiffer
```

---

## PyTorch version

Based on the script [`run_glue.py`](https://github.com/Adapter-Hub/adapter-transformers/blob/master/examples/text-classification/run_glue.py).

Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding
Evaluation](https://gluebenchmark.com/). This script can fine-tune any of the models on the [hub](https://huggingface.co/models)
and can also be used for a dataset hosted on our [hub](https://huggingface.co/datasets) or your own data in a csv or a JSON file 
(the script might need some tweaks in that case, refer to the comments inside for help).

GLUE is made up of a total of 9 different tasks. Here is how to run the script on one of them:

```bash
export TASK_NAME=mrpc

python run_glue.py \
  --model_name_or_path bert-base-cased \
  --task_name $TASK_NAME \
  --do_train \
  --do_eval \
  --max_seq_length 128 \
  --per_device_train_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --output_dir /tmp/$TASK_NAME/
```

where task name can be one of cola, sst2, mrpc, stsb, qqp, mnli, qnli, rte, wnli.

We get the following results on the dev set of the benchmark with the previous commands (with an exception for MRPC and
WNLI which are tiny and where we used 5 epochs instead of 3). Trainings are seeded so you should obtain the same
results with PyTorch 1.6.0 (and close results with different versions), training times are given for information (a
single Titan RTX was used):

| Task  | Metric                       | Result      | Training time |
|-------|------------------------------|-------------|---------------|
| CoLA  | Matthews corr                | 56.53       | 3:17          |
| SST-2 | Accuracy                     | 92.32       | 26:06         |
| MRPC  | F1/Accuracy                  | 88.85/84.07 | 2:21          |
| STS-B | Pearson/Spearman corr.       | 88.64/88.48 | 2:13          |
| QQP   | Accuracy/F1                  | 90.71/87.49 | 2:22:26       |
| MNLI  | Matched acc./Mismatched acc. | 83.91/84.10 | 2:35:23       |
| QNLI  | Accuracy                     | 90.66       | 40:57         |
| RTE   | Accuracy                     | 65.70       | 57            |
| WNLI  | Accuracy                     | 56.34       | 24            |

Some of these results are significantly different from the ones reported on the test set of GLUE benchmark on the
website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the website.

The following example fine-tunes BERT on the `imdb` dataset hosted on our [hub](https://huggingface.co/datasets):

```bash
python run_glue.py \
  --model_name_or_path bert-base-cased \
  --dataset_name imdb  \
  --do_train \
  --do_predict \
  --max_seq_length 128 \
  --per_device_train_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --output_dir /tmp/imdb/
```


### Mixed precision training

If you have a GPU with mixed precision capabilities (architecture Pascal or more recent), you can use mixed precision
training with PyTorch 1.6.0 or latest, or by installing the [Apex](https://github.com/NVIDIA/apex) library for previous
versions. Just add the flag `--fp16` to your command launching one of the scripts mentioned above!

Using mixed precision training usually results in 2x-speedup for training with the same final results:

| Task  | Metric                       | Result      | Training time | Result (FP16) | Training time (FP16) |
|-------|------------------------------|-------------|---------------|---------------|----------------------|
| CoLA  | Matthews corr                | 56.53       | 3:17          | 56.78         | 1:41                 |
| SST-2 | Accuracy                     | 92.32       | 26:06         | 91.74         | 13:11                |
| MRPC  | F1/Accuracy                  | 88.85/84.07 | 2:21          | 88.12/83.58   | 1:10                 |
| STS-B | Pearson/Spearman corr.       | 88.64/88.48 | 2:13          | 88.71/88.55   | 1:08                 |
| QQP   | Accuracy/F1                  | 90.71/87.49 | 2:22:26       | 90.67/87.43   | 1:11:54              |
| MNLI  | Matched acc./Mismatched acc. | 83.91/84.10 | 2:35:23       | 84.04/84.06   | 1:17:06              |
| QNLI  | Accuracy                     | 90.66       | 40:57         | 90.96         | 20:16                |
| RTE   | Accuracy                     | 65.70       | 57            | 65.34         | 29                   |
| WNLI  | Accuracy                     | 56.34       | 24            | 56.34         | 12                   |


## PyTorch version, no Trainer

Based on the script [`run_glue_no_trainer.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue_no_trainer.py).

Like `run_glue.py`, this script allows you to fine-tune any of the models on the [hub](https://huggingface.co/models) on a
text classification task, either a GLUE task or your own data in a csv or a JSON file. The main difference is that this
script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like.

It offers less options than the script with `Trainer` (for instance you can easily change the options for the optimizer
or the dataloaders directly in the script) but still run in a distributed setup, on TPU and supports mixed precision by
the mean of the [🤗 `Accelerate`](https://github.com/huggingface/accelerate) library. You can use the script normally
after installing it:

```bash
pip install accelerate
```

then

```bash
export TASK_NAME=mrpc

python run_glue_no_trainer.py \
  --model_name_or_path bert-base-cased \
  --task_name $TASK_NAME \
  --max_length 128 \
  --per_device_train_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --output_dir /tmp/$TASK_NAME/
```

You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run

```bash
accelerate config
```

and reply to the questions asked. Then

```bash
accelerate test
```

that will check everything is ready for training. Finally, you can launch training with

```bash
export TASK_NAME=mrpc

accelerate launch run_glue_no_trainer.py \
  --model_name_or_path bert-base-cased \
  --task_name $TASK_NAME \
  --max_length 128 \
  --per_device_train_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --output_dir /tmp/$TASK_NAME/
```

This command is the same and will work for:

- a CPU-only setup
- a setup with one GPU
- a distributed training with several GPUs (single or multi node)
- a training on TPUs

Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.
