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# Text classification examples

## GLUE tasks

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

Fine-tuning the library TensorFlow 2.0 Bert model for sequence classification on the MRPC task of the GLUE benchmark: [General Language Understanding Evaluation](https://gluebenchmark.com/).

This script has an option for mixed precision (Automatic Mixed Precision / AMP) to run models on Tensor Cores (NVIDIA Volta/Turing GPUs) and future hardware and an option for XLA, which uses the XLA compiler to reduce model runtime.
Options are toggled using `USE_XLA` or `USE_AMP` variables in the script.
These options and the below benchmark are provided by @tlkh.

Quick benchmarks from the script (no other modifications):

| GPU    | Mode | Time (2nd epoch) | Val Acc (3 runs) |
| --------- | -------- | ----------------------- | ----------------------|
| Titan V | FP32 | 41s | 0.8438/0.8281/0.8333 |
| Titan V | AMP | 26s | 0.8281/0.8568/0.8411 |
| V100    | FP32 | 35s | 0.8646/0.8359/0.8464 |
| V100    | AMP | 22s | 0.8646/0.8385/0.8411 |
| 1080 Ti | FP32 | 55s | - |

Mixed precision (AMP) reduces the training time considerably for the same hardware and hyper-parameters (same batch size was used).


## Run generic text classification script in TensorFlow

The script [run_tf_text_classification.py](https://github.com/huggingface/transformers/blob/master/examples/tensorflow/text-classification/run_tf_text_classification.py) allows users to run a text classification on their own CSV files. For now there are few restrictions, the CSV files must have a header corresponding to the column names and not more than three columns: one column for the id, one column for the text and another column for a second piece of text in case of an entailment classification for example.

To use the script, one as to run the following command line:
```bash
python run_tf_text_classification.py \
  --train_file train.csv \ ### training dataset file location (mandatory if running with --do_train option)
  --dev_file dev.csv \ ### development dataset file location (mandatory if running with --do_eval option)
  --test_file test.csv \ ### test dataset file location (mandatory if running with --do_predict option)
  --label_column_id 0 \ ### which column corresponds to the labels
  --model_name_or_path bert-base-multilingual-uncased \
  --output_dir model \
  --num_train_epochs 4 \
  --per_device_train_batch_size 16 \
  --per_device_eval_batch_size 32 \
  --do_train \
  --do_eval \
  --do_predict \
  --logging_steps 10 \
  --evaluation_strategy steps \
  --save_steps 10 \
  --overwrite_output_dir \
  --max_seq_length 128
```