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Text classification examples

PyTorch version

Based on the script run_glue.py.

Fine-tuning the library models for sequence classification on the GLUE benchmark: General Language Understanding Evaluation. This script can fine-tune any of the models on the hub and can also be used for 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:

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 isntead 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 Matthew's corr 56.53 3:17
SST-2 Accuracy 92.32 26:06
MRPC F1/Accuracy 88.85/84.07 2:21
STS-B Person/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 on the website.

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 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 Matthew's 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 Person/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.

Like run_glue.py, this script allows you to fine-tune any of the models on the hub 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 library. You can use the script normally after installing it:

pip install accelerate

then

export TASK_NAME=mrpc

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

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

accelerate config

and reply to the questions asked. Then

accelerate test

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

export TASK_NAME=mrpc

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

TensorFlow 2.0 version

Based on the script 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.

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 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:

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

XNLI

Based on the script run_xnli.py.

XNLI is a crowd-sourced dataset based on MultiNLI. It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).

Fine-tuning on XNLI

This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in 106 mins on a single tesla V100 16GB.

python run_xnli.py \
  --model_name_or_path bert-base-multilingual-cased \
  --language de \
  --train_language en \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 32 \
  --learning_rate 5e-5 \
  --num_train_epochs 2.0 \
  --max_seq_length 128 \
  --output_dir /tmp/debug_xnli/ \
  --save_steps -1

Training with the previously defined hyper-parameters yields the following results on the test set:

acc = 0.7093812375249501