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650 lines
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Markdown
650 lines
23 KiB
Markdown
# Examples
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In this section a few examples are put together. All of these examples work for several models, making use of the very
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similar API between the different models.
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**Important**
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To run the latest versions of the examples, you have to install from source and install some specific requirements for the examples.
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Execute the following steps in a new virtual environment:
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```bash
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git clone https://github.com/huggingface/transformers
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cd transformers
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pip install .
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pip install -r ./examples/requirements.txt
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```
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| Section | Description |
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|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------
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| [TensorFlow 2.0 models on GLUE](#TensorFlow-2.0-Bert-models-on-GLUE) | Examples running BERT TensorFlow 2.0 model on the GLUE tasks. |
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| [Running on TPUs](#running-on-tpus) | Examples on running fine-tuning tasks on Google TPUs to accelerate workloads. |
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| [Language Model training](#language-model-training) | Fine-tuning (or training from scratch) the library models for language modeling on a text dataset. Causal language modeling for GPT/GPT-2, masked language modeling for BERT/RoBERTa. |
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| [Language Generation](#language-generation) | Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet. |
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| [GLUE](#glue) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision. |
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| [SQuAD](#squad) | Using BERT/RoBERTa/XLNet/XLM for question answering, examples with distributed training. |
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| [Multiple Choice](#multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. |
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| [Named Entity Recognition](https://github.com/huggingface/transformers/tree/master/examples/ner) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training. |
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| [XNLI](#xnli) | Examples running BERT/XLM on the XNLI benchmark. |
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| [Adversarial evaluation of model performances](#adversarial-evaluation-of-model-performances) | Testing a model with adversarial evaluation of natural language inference on the Heuristic Analysis for NLI Systems (HANS) dataset (McCoy et al., 2019.) |
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## TensorFlow 2.0 Bert models on GLUE
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Based on the script [`run_tf_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_tf_glue.py).
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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/).
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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.
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Options are toggled using `USE_XLA` or `USE_AMP` variables in the script.
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These options and the below benchmark are provided by @tlkh.
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Quick benchmarks from the script (no other modifications):
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| GPU | Mode | Time (2nd epoch) | Val Acc (3 runs) |
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| --------- | -------- | ----------------------- | ----------------------|
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| Titan V | FP32 | 41s | 0.8438/0.8281/0.8333 |
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| Titan V | AMP | 26s | 0.8281/0.8568/0.8411 |
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| V100 | FP32 | 35s | 0.8646/0.8359/0.8464 |
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| V100 | AMP | 22s | 0.8646/0.8385/0.8411 |
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| 1080 Ti | FP32 | 55s | - |
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Mixed precision (AMP) reduces the training time considerably for the same hardware and hyper-parameters (same batch size was used).
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## Running on TPUs
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You can accelerate your workloads on Google's TPUs. For information on how to setup your TPU environment refer to this
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[README](https://github.com/pytorch/xla/blob/master/README.md).
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The following are some examples of running the `*_tpu.py` finetuning scripts on TPUs. All steps for data preparation are
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identical to your normal GPU + Huggingface setup.
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### GLUE
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Before running anyone of these GLUE tasks you should download the
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[GLUE data](https://gluebenchmark.com/tasks) by running
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[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
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and unpack it to some directory `$GLUE_DIR`.
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For running your GLUE task on MNLI dataset you can run something like the following:
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```
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export XRT_TPU_CONFIG="tpu_worker;0;$TPU_IP_ADDRESS:8470"
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export GLUE_DIR=/path/to/glue
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export TASK_NAME=MNLI
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python run_glue_tpu.py \
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--model_type bert \
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--model_name_or_path bert-base-cased \
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--task_name $TASK_NAME \
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--do_train \
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--do_eval \
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--data_dir $GLUE_DIR/$TASK_NAME \
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--max_seq_length 128 \
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--train_batch_size 32 \
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--learning_rate 3e-5 \
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--num_train_epochs 3.0 \
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--output_dir /tmp/$TASK_NAME \
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--overwrite_output_dir \
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--logging_steps 50 \
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--save_steps 200 \
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--num_cores=8 \
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--only_log_master
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```
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## Language model training
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Based on the script [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py).
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Fine-tuning (or training from scratch) the library models for language modeling on a text dataset for GPT, GPT-2, BERT and RoBERTa (DistilBERT
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to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa
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are fine-tuned using a masked language modeling (MLM) loss.
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Before running the following example, you should get a file that contains text on which the language model will be
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trained or fine-tuned. A good example of such text is the [WikiText-2 dataset](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/).
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We will refer to two different files: `$TRAIN_FILE`, which contains text for training, and `$TEST_FILE`, which contains
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text that will be used for evaluation.
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### GPT-2/GPT and causal language modeling
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The following example fine-tunes GPT-2 on WikiText-2. We're using the raw WikiText-2 (no tokens were replaced before
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the tokenization). The loss here is that of causal language modeling.
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```bash
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export TRAIN_FILE=/path/to/dataset/wiki.train.raw
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export TEST_FILE=/path/to/dataset/wiki.test.raw
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python run_language_modeling.py \
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--output_dir=output \
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--model_type=gpt2 \
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--model_name_or_path=gpt2 \
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--do_train \
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--train_data_file=$TRAIN_FILE \
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--do_eval \
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--eval_data_file=$TEST_FILE
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```
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This takes about half an hour to train on a single K80 GPU and about one minute for the evaluation to run. It reaches
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a score of ~20 perplexity once fine-tuned on the dataset.
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### RoBERTa/BERT and masked language modeling
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The following example fine-tunes RoBERTa on WikiText-2. Here too, we're using the raw WikiText-2. The loss is different
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as BERT/RoBERTa have a bidirectional mechanism; we're therefore using the same loss that was used during their
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pre-training: masked language modeling.
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In accordance to the RoBERTa paper, we use dynamic masking rather than static masking. The model may, therefore, converge
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slightly slower (over-fitting takes more epochs).
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We use the `--mlm` flag so that the script may change its loss function.
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```bash
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export TRAIN_FILE=/path/to/dataset/wiki.train.raw
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export TEST_FILE=/path/to/dataset/wiki.test.raw
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python run_language_modeling.py \
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--output_dir=output \
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--model_type=roberta \
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--model_name_or_path=roberta-base \
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--do_train \
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--train_data_file=$TRAIN_FILE \
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--do_eval \
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--eval_data_file=$TEST_FILE \
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--mlm
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```
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## Language generation
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Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py).
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Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL, XLNet, CTRL.
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A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you
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can try out the different models available in the library.
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Example usage:
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```bash
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python run_generation.py \
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--model_type=gpt2 \
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--model_name_or_path=gpt2
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```
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## GLUE
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Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_glue.py).
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Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding
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Evaluation](https://gluebenchmark.com/). This script can fine-tune the following models: BERT, XLM, XLNet and RoBERTa.
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GLUE is made up of a total of 9 different tasks. We get the following results on the dev set of the benchmark with an
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uncased BERT base model (the checkpoint `bert-base-uncased`). All experiments ran single V100 GPUs with a total train
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batch sizes between 16 and 64. Some of these tasks have a small dataset and training can lead to high variance in the results
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between different runs. We report the median on 5 runs (with different seeds) for each of the metrics.
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| Task | Metric | Result |
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|-------|------------------------------|-------------|
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| CoLA | Matthew's corr | 49.23 |
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| SST-2 | Accuracy | 91.97 |
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| MRPC | F1/Accuracy | 89.47/85.29 |
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| STS-B | Person/Spearman corr. | 83.95/83.70 |
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| QQP | Accuracy/F1 | 88.40/84.31 |
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| MNLI | Matched acc./Mismatched acc. | 80.61/81.08 |
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| QNLI | Accuracy | 87.46 |
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| RTE | Accuracy | 61.73 |
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| WNLI | Accuracy | 45.07 |
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Some of these results are significantly different from the ones reported on the test set
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of GLUE benchmark on the website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the webite.
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Before running any one of these GLUE tasks you should download the
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[GLUE data](https://gluebenchmark.com/tasks) by running
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[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
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and unpack it to some directory `$GLUE_DIR`.
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```bash
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export GLUE_DIR=/path/to/glue
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export TASK_NAME=MRPC
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python run_glue.py \
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--model_type bert \
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--model_name_or_path bert-base-cased \
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--task_name $TASK_NAME \
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--do_train \
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--do_eval \
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--data_dir $GLUE_DIR/$TASK_NAME \
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--max_seq_length 128 \
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--per_gpu_train_batch_size 32 \
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--learning_rate 2e-5 \
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--num_train_epochs 3.0 \
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--output_dir /tmp/$TASK_NAME/
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```
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where task name can be one of CoLA, SST-2, MRPC, STS-B, QQP, MNLI, QNLI, RTE, WNLI.
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The dev set results will be present within the text file `eval_results.txt` in the specified output_dir.
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In case of MNLI, since there are two separate dev sets (matched and mismatched), there will be a separate
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output folder called `/tmp/MNLI-MM/` in addition to `/tmp/MNLI/`.
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The code has not been tested with half-precision training with apex on any GLUE task apart from MRPC, MNLI,
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CoLA, SST-2. The following section provides details on how to run half-precision training with MRPC. With that being
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said, there shouldn’t be any issues in running half-precision training with the remaining GLUE tasks as well,
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since the data processor for each task inherits from the base class DataProcessor.
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### MRPC
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#### Fine-tuning example
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The following examples fine-tune BERT on the Microsoft Research Paraphrase Corpus (MRPC) corpus and runs in less
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than 10 minutes on a single K-80 and in 27 seconds (!) on single tesla V100 16GB with apex installed.
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Before running any one of these GLUE tasks you should download the
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[GLUE data](https://gluebenchmark.com/tasks) by running
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[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
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and unpack it to some directory `$GLUE_DIR`.
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```bash
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export GLUE_DIR=/path/to/glue
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python run_glue.py \
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--model_name_or_path bert-base-cased \
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--task_name MRPC \
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--do_train \
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--do_eval \
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--data_dir $GLUE_DIR/MRPC/ \
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--max_seq_length 128 \
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--per_gpu_train_batch_size 32 \
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--learning_rate 2e-5 \
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--num_train_epochs 3.0 \
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--output_dir /tmp/mrpc_output/
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```
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Our test ran on a few seeds with [the original implementation hyper-
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parameters](https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks) gave evaluation
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results between 84% and 88%.
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#### Using Apex and mixed-precision
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Using Apex and 16 bit precision, the fine-tuning on MRPC only takes 27 seconds. First install
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[apex](https://github.com/NVIDIA/apex), then run the following example:
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```bash
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export GLUE_DIR=/path/to/glue
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python run_glue.py \
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--model_name_or_path bert-base-cased \
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--task_name MRPC \
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--do_train \
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--do_eval \
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--data_dir $GLUE_DIR/MRPC/ \
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--max_seq_length 128 \
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--per_gpu_train_batch_size 32 \
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--learning_rate 2e-5 \
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--num_train_epochs 3.0 \
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--output_dir /tmp/mrpc_output/ \
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--fp16
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```
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#### Distributed training
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Here is an example using distributed training on 8 V100 GPUs. The model used is the BERT whole-word-masking and it
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reaches F1 > 92 on MRPC.
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```bash
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export GLUE_DIR=/path/to/glue
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python -m torch.distributed.launch \
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--nproc_per_node 8 run_glue.py \
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--model_name_or_path bert-base-cased \
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--task_name MRPC \
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--do_train \
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--do_eval \
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--data_dir $GLUE_DIR/MRPC/ \
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--max_seq_length 128 \
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--per_gpu_train_batch_size 8 \
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--learning_rate 2e-5 \
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--num_train_epochs 3.0 \
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--output_dir /tmp/mrpc_output/
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```
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Training with these hyper-parameters gave us the following results:
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```bash
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acc = 0.8823529411764706
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acc_and_f1 = 0.901702786377709
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eval_loss = 0.3418912578906332
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f1 = 0.9210526315789473
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global_step = 174
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loss = 0.07231863956341798
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```
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### MNLI
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The following example uses the BERT-large, uncased, whole-word-masking model and fine-tunes it on the MNLI task.
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```bash
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export GLUE_DIR=/path/to/glue
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python -m torch.distributed.launch \
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--nproc_per_node 8 run_glue.py \
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--model_name_or_path bert-base-cased \
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--task_name mnli \
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--do_train \
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--do_eval \
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--data_dir $GLUE_DIR/MNLI/ \
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--max_seq_length 128 \
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--per_gpu_train_batch_size 8 \
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--learning_rate 2e-5 \
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--num_train_epochs 3.0 \
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--output_dir output_dir \
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```
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The results are the following:
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```bash
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***** Eval results *****
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acc = 0.8679706601466992
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eval_loss = 0.4911287787382479
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global_step = 18408
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loss = 0.04755385363816904
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***** Eval results *****
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acc = 0.8747965825874695
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eval_loss = 0.45516540421714036
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global_step = 18408
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loss = 0.04755385363816904
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```
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## Multiple Choice
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Based on the script [`run_multiple_choice.py`]().
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#### Fine-tuning on SWAG
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Download [swag](https://github.com/rowanz/swagaf/tree/master/data) data
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```bash
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#training on 4 tesla V100(16GB) GPUS
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export SWAG_DIR=/path/to/swag_data_dir
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python ./examples/multiple-choice/run_multiple_choice.py \
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--task_name swag \
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--model_name_or_path roberta-base \
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--do_train \
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--do_eval \
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--data_dir $SWAG_DIR \
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--learning_rate 5e-5 \
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--num_train_epochs 3 \
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--max_seq_length 80 \
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--output_dir models_bert/swag_base \
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--per_gpu_eval_batch_size=16 \
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--per_gpu_train_batch_size=16 \
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--gradient_accumulation_steps 2 \
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--overwrite_output
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```
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Training with the defined hyper-parameters yields the following results:
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```
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***** Eval results *****
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eval_acc = 0.8338998300509847
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eval_loss = 0.44457291918821606
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```
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## SQuAD
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Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py).
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#### Fine-tuning BERT on SQuAD1.0
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This example code fine-tunes BERT on the SQuAD1.0 dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large)
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on a single tesla V100 16GB. The data for SQuAD can be downloaded with the following links and should be saved in a
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$SQUAD_DIR directory.
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* [train-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json)
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* [dev-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json)
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* [evaluate-v1.1.py](https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py)
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And for SQuAD2.0, you need to download:
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- [train-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json)
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- [dev-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json)
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- [evaluate-v2.0.py](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/)
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```bash
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export SQUAD_DIR=/path/to/SQUAD
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python run_squad.py \
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--model_type bert \
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--model_name_or_path bert-base-uncased \
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--do_train \
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--do_eval \
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--train_file $SQUAD_DIR/train-v1.1.json \
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--predict_file $SQUAD_DIR/dev-v1.1.json \
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--per_gpu_train_batch_size 12 \
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--learning_rate 3e-5 \
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--num_train_epochs 2.0 \
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--max_seq_length 384 \
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--doc_stride 128 \
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--output_dir /tmp/debug_squad/
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```
|
||
|
||
Training with the previously defined hyper-parameters yields the following results:
|
||
|
||
```bash
|
||
f1 = 88.52
|
||
exact_match = 81.22
|
||
```
|
||
|
||
#### Distributed training
|
||
|
||
|
||
Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:
|
||
|
||
```bash
|
||
python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
|
||
--model_type bert \
|
||
--model_name_or_path bert-large-uncased-whole-word-masking \
|
||
--do_train \
|
||
--do_eval \
|
||
--train_file $SQUAD_DIR/train-v1.1.json \
|
||
--predict_file $SQUAD_DIR/dev-v1.1.json \
|
||
--learning_rate 3e-5 \
|
||
--num_train_epochs 2 \
|
||
--max_seq_length 384 \
|
||
--doc_stride 128 \
|
||
--output_dir ./examples/models/wwm_uncased_finetuned_squad/ \
|
||
--per_gpu_eval_batch_size=3 \
|
||
--per_gpu_train_batch_size=3 \
|
||
```
|
||
|
||
Training with the previously defined hyper-parameters yields the following results:
|
||
|
||
```bash
|
||
f1 = 93.15
|
||
exact_match = 86.91
|
||
```
|
||
|
||
This fine-tuned model is available as a checkpoint under the reference
|
||
`bert-large-uncased-whole-word-masking-finetuned-squad`.
|
||
|
||
#### Fine-tuning XLNet on SQuAD
|
||
|
||
This example code fine-tunes XLNet on both SQuAD1.0 and SQuAD2.0 dataset. See above to download the data for SQuAD .
|
||
|
||
##### Command for SQuAD1.0:
|
||
|
||
```bash
|
||
export SQUAD_DIR=/path/to/SQUAD
|
||
|
||
python run_squad.py \
|
||
--model_type xlnet \
|
||
--model_name_or_path xlnet-large-cased \
|
||
--do_train \
|
||
--do_eval \
|
||
--train_file $SQUAD_DIR/train-v1.1.json \
|
||
--predict_file $SQUAD_DIR/dev-v1.1.json \
|
||
--learning_rate 3e-5 \
|
||
--num_train_epochs 2 \
|
||
--max_seq_length 384 \
|
||
--doc_stride 128 \
|
||
--output_dir ./wwm_cased_finetuned_squad/ \
|
||
--per_gpu_eval_batch_size=4 \
|
||
--per_gpu_train_batch_size=4 \
|
||
--save_steps 5000
|
||
```
|
||
|
||
##### Command for SQuAD2.0:
|
||
|
||
```bash
|
||
export SQUAD_DIR=/path/to/SQUAD
|
||
|
||
python run_squad.py \
|
||
--model_type xlnet \
|
||
--model_name_or_path xlnet-large-cased \
|
||
--do_train \
|
||
--do_eval \
|
||
--version_2_with_negative \
|
||
--train_file $SQUAD_DIR/train-v2.0.json \
|
||
--predict_file $SQUAD_DIR/dev-v2.0.json \
|
||
--learning_rate 3e-5 \
|
||
--num_train_epochs 4 \
|
||
--max_seq_length 384 \
|
||
--doc_stride 128 \
|
||
--output_dir ./wwm_cased_finetuned_squad/ \
|
||
--per_gpu_eval_batch_size=2 \
|
||
--per_gpu_train_batch_size=2 \
|
||
--save_steps 5000
|
||
```
|
||
|
||
Larger batch size may improve the performance while costing more memory.
|
||
|
||
##### Results for SQuAD1.0 with the previously defined hyper-parameters:
|
||
|
||
```python
|
||
{
|
||
"exact": 85.45884578997162,
|
||
"f1": 92.5974600601065,
|
||
"total": 10570,
|
||
"HasAns_exact": 85.45884578997162,
|
||
"HasAns_f1": 92.59746006010651,
|
||
"HasAns_total": 10570
|
||
}
|
||
```
|
||
|
||
##### Results for SQuAD2.0 with the previously defined hyper-parameters:
|
||
|
||
```python
|
||
{
|
||
"exact": 80.4177545691906,
|
||
"f1": 84.07154997729623,
|
||
"total": 11873,
|
||
"HasAns_exact": 76.73751686909581,
|
||
"HasAns_f1": 84.05558584352873,
|
||
"HasAns_total": 5928,
|
||
"NoAns_exact": 84.0874684608915,
|
||
"NoAns_f1": 84.0874684608915,
|
||
"NoAns_total": 5945
|
||
}
|
||
```
|
||
|
||
|
||
|
||
|
||
## XNLI
|
||
|
||
Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_xnli.py).
|
||
|
||
[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
|
||
|
||
#### Fine-tuning on XNLI
|
||
|
||
This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in 106 mins
|
||
on a single tesla V100 16GB. The data for XNLI can be downloaded with the following links and should be both saved (and un-zipped) in a
|
||
`$XNLI_DIR` directory.
|
||
|
||
* [XNLI 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip)
|
||
* [XNLI-MT 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-MT-1.0.zip)
|
||
|
||
```bash
|
||
export XNLI_DIR=/path/to/XNLI
|
||
|
||
python run_xnli.py \
|
||
--model_type bert \
|
||
--model_name_or_path bert-base-multilingual-cased \
|
||
--language de \
|
||
--train_language en \
|
||
--do_train \
|
||
--do_eval \
|
||
--data_dir $XNLI_DIR \
|
||
--per_gpu_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:
|
||
|
||
```bash
|
||
acc = 0.7093812375249501
|
||
```
|
||
|
||
## MM-IMDb
|
||
|
||
Based on the script [`run_mmimdb.py`](https://github.com/huggingface/transformers/blob/master/examples/contrib/mm-imdb/run_mmimdb.py).
|
||
|
||
[MM-IMDb](http://lisi1.unal.edu.co/mmimdb/) is a Multimodal dataset with around 26,000 movies including images, plots and other metadata.
|
||
|
||
### Training on MM-IMDb
|
||
|
||
```
|
||
python run_mmimdb.py \
|
||
--data_dir /path/to/mmimdb/dataset/ \
|
||
--model_type bert \
|
||
--model_name_or_path bert-base-uncased \
|
||
--output_dir /path/to/save/dir/ \
|
||
--do_train \
|
||
--do_eval \
|
||
--max_seq_len 512 \
|
||
--gradient_accumulation_steps 20 \
|
||
--num_image_embeds 3 \
|
||
--num_train_epochs 100 \
|
||
--patience 5
|
||
```
|
||
|
||
## Adversarial evaluation of model performances
|
||
|
||
Here is an example on evaluating a model using adversarial evaluation of natural language inference with the Heuristic Analysis for NLI Systems (HANS) dataset [McCoy et al., 2019](https://arxiv.org/abs/1902.01007). The example was gracefully provided by [Nafise Sadat Moosavi](https://github.com/ns-moosavi).
|
||
|
||
The HANS dataset can be downloaded from [this location](https://github.com/tommccoy1/hans).
|
||
|
||
This is an example of using test_hans.py:
|
||
|
||
```bash
|
||
export HANS_DIR=path-to-hans
|
||
export MODEL_TYPE=type-of-the-model-e.g.-bert-roberta-xlnet-etc
|
||
export MODEL_PATH=path-to-the-model-directory-that-is-trained-on-NLI-e.g.-by-using-run_glue.py
|
||
|
||
python examples/hans/test_hans.py \
|
||
--task_name hans \
|
||
--model_type $MODEL_TYPE \
|
||
--do_eval \
|
||
--data_dir $HANS_DIR \
|
||
--model_name_or_path $MODEL_PATH \
|
||
--max_seq_length 128 \
|
||
--output_dir $MODEL_PATH \
|
||
```
|
||
|
||
This will create the hans_predictions.txt file in MODEL_PATH, which can then be evaluated using hans/evaluate_heur_output.py from the HANS dataset.
|
||
|
||
The results of the BERT-base model that is trained on MNLI using batch size 8 and the random seed 42 on the HANS dataset is as follows:
|
||
|
||
```bash
|
||
Heuristic entailed results:
|
||
lexical_overlap: 0.9702
|
||
subsequence: 0.9942
|
||
constituent: 0.9962
|
||
|
||
Heuristic non-entailed results:
|
||
lexical_overlap: 0.199
|
||
subsequence: 0.0396
|
||
constituent: 0.118
|
||
```
|