# Examples In this section a few examples are put together. All of these examples work for several models, making use of the very similar API between the different models. | Section | Description | |----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------| | [Language Model fine-tuning](#language-model-fine-tuning) | Fine-tuning the library models for language modeling on a text dataset. Causal language modeling for GPT/GPT-2, masked language modeling for BERT/RoBERTa. | | [Language Generation](#language-generation) | Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet. | | [GLUE](#glue) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision. | | [SQuAD](#squad) | Using BERT for question answering, examples with distributed training. | | [Multiple Choice](#multiple choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. ## Language model fine-tuning Based on the script [`run_lm_finetuning.py`](https://github.com/huggingface/transformers/blob/master/examples/run_lm_finetuning.py). Fine-tuning the library models for language modeling on a text dataset for GPT, GPT-2, BERT and RoBERTa (DistilBERT to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned using a masked language modeling (MLM) loss. Before running the following example, you should get a file that contains text on which the language model will be 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/). We will refer to two different files: `$TRAIN_FILE`, which contains text for training, and `$TEST_FILE`, which contains text that will be used for evaluation. ### GPT-2/GPT and causal language modeling The following example fine-tunes GPT-2 on WikiText-2. We're using the raw WikiText-2 (no tokens were replaced before the tokenization). The loss here is that of causal language modeling. ```bash export TRAIN_FILE=/path/to/dataset/wiki.train.raw export TEST_FILE=/path/to/dataset/wiki.test.raw python run_lm_finetuning.py \ --output_dir=output \ --model_type=gpt2 \ --model_name_or_path=gpt2 \ --do_train \ --train_data_file=$TRAIN_FILE \ --do_eval \ --eval_data_file=$TEST_FILE ``` This takes about half an hour to train on a single K80 GPU and about one minute for the evaluation to run. It reaches a score of ~20 perplexity once fine-tuned on the dataset. ### RoBERTa/BERT and masked language modeling The following example fine-tunes RoBERTa on WikiText-2. Here too, we're using the raw WikiText-2. The loss is different as BERT/RoBERTa have a bidirectional mechanism; we're therefore using the same loss that was used during their pre-training: masked language modeling. In accordance to the RoBERTa paper, we use dynamic masking rather than static masking. The model may, therefore, converge slightly slower (over-fitting takes more epochs). We use the `--mlm` flag so that the script may change its loss function. ```bash export TRAIN_FILE=/path/to/dataset/wiki.train.raw export TEST_FILE=/path/to/dataset/wiki.test.raw python run_lm_finetuning.py \ --output_dir=output \ --model_type=roberta \ --model_name_or_path=roberta-base \ --do_train \ --train_data_file=$TRAIN_FILE \ --do_eval \ --eval_data_file=$TEST_FILE \ --mlm ``` ## Language generation Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py). Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet. A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you can try out the different models available in the library. Example usage: ```bash python run_generation.py \ --model_type=gpt2 \ --model_name_or_path=gpt2 ``` ## GLUE Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/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 the following models: BERT, XLM, XLNet and RoBERTa. 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 uncased BERT base model (the checkpoint `bert-base-uncased`). All experiments ran on 8 V100 GPUs with a total train batch size of 24. Some of these tasks have a small dataset and training can lead to high variance in the results between different runs. We report the median on 5 runs (with different seeds) for each of the metrics. | Task | Metric | Result | |-------|------------------------------|-------------| | CoLA | Matthew's corr | 48.87 | | SST-2 | Accuracy | 91.74 | | MRPC | F1/Accuracy | 90.70/86.27 | | STS-B | Person/Spearman corr. | 91.39/91.04 | | QQP | Accuracy/F1 | 90.79/87.66 | | MNLI | Matched acc./Mismatched acc. | 83.70/84.83 | | QNLI | Accuracy | 89.31 | | RTE | Accuracy | 71.43 | | WNLI | Accuracy | 43.66 | 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 webite. Before running anyone of these GLUE tasks you should download the [GLUE data](https://gluebenchmark.com/tasks) by running [this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e) and unpack it to some directory `$GLUE_DIR`. ```bash export GLUE_DIR=/path/to/glue export TASK_NAME=MRPC python run_glue.py \ --model_type bert \ --model_name_or_path bert-base-cased \ --task_name $TASK_NAME \ --do_train \ --do_eval \ --do_lower_case \ --data_dir $GLUE_DIR/$TASK_NAME \ --max_seq_length 128 \ --per_gpu_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3.0 \ --output_dir /tmp/$TASK_NAME/ ``` where task name can be one of CoLA, SST-2, MRPC, STS-B, QQP, MNLI, QNLI, RTE, WNLI. The dev set results will be present within the text file `eval_results.txt` in the specified output_dir. In case of MNLI, since there are two separate dev sets (matched and mismatched), there will be a separate output folder called `/tmp/MNLI-MM/` in addition to `/tmp/MNLI/`. The code has not been tested with half-precision training with apex on any GLUE task apart from MRPC, MNLI, CoLA, SST-2. The following section provides details on how to run half-precision training with MRPC. With that being said, there shouldn’t be any issues in running half-precision training with the remaining GLUE tasks as well, since the data processor for each task inherits from the base class DataProcessor. ### MRPC #### Fine-tuning example The following examples fine-tune BERT on the Microsoft Research Paraphrase Corpus (MRPC) corpus and runs in less than 10 minutes on a single K-80 and in 27 seconds (!) on single tesla V100 16GB with apex installed. Before running anyone of these GLUE tasks you should download the [GLUE data](https://gluebenchmark.com/tasks) by running [this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e) and unpack it to some directory `$GLUE_DIR`. ```bash export GLUE_DIR=/path/to/glue python run_glue.py \ --model_type bert \ --model_name_or_path bert-base-cased \ --task_name MRPC \ --do_train \ --do_eval \ --do_lower_case \ --data_dir $GLUE_DIR/MRPC/ \ --max_seq_length 128 \ --per_gpu_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3.0 \ --output_dir /tmp/mrpc_output/ ``` Our test ran on a few seeds with [the original implementation hyper- parameters](https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks) gave evaluation results between 84% and 88%. #### Using Apex and mixed-precision Using Apex and 16 bit precision, the fine-tuning on MRPC only takes 27 seconds. First install [apex](https://github.com/NVIDIA/apex), then run the following example: ```bash export GLUE_DIR=/path/to/glue python run_glue.py \ --model_type bert \ --model_name_or_path bert-base-cased \ --task_name MRPC \ --do_train \ --do_eval \ --do_lower_case \ --data_dir $GLUE_DIR/MRPC/ \ --max_seq_length 128 \ --per_gpu_train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3.0 \ --output_dir /tmp/mrpc_output/ \ --fp16 ``` #### Distributed training Here is an example using distributed training on 8 V100 GPUs. The model used is the BERT whole-word-masking and it reaches F1 > 92 on MRPC. ```bash export GLUE_DIR=/path/to/glue python -m torch.distributed.launch \ --nproc_per_node 8 run_glue.py \ --model_type bert \ --model_name_or_path bert-base-cased \ --task_name MRPC \ --do_train \ --do_eval \ --do_lower_case \ --data_dir $GLUE_DIR/MRPC/ \ --max_seq_length 128 \ --per_gpu_train_batch_size 8 \ --learning_rate 2e-5 \ --num_train_epochs 3.0 \ --output_dir /tmp/mrpc_output/ ``` Training with these hyper-parameters gave us the following results: ```bash acc = 0.8823529411764706 acc_and_f1 = 0.901702786377709 eval_loss = 0.3418912578906332 f1 = 0.9210526315789473 global_step = 174 loss = 0.07231863956341798 ``` ### MNLI The following example uses the BERT-large, uncased, whole-word-masking model and fine-tunes it on the MNLI task. ```bash export GLUE_DIR=/path/to/glue python -m torch.distributed.launch \ --nproc_per_node 8 run_glue.py \ --model_type bert \ --model_name_or_path bert-base-cased \ --task_name mnli \ --do_train \ --do_eval \ --do_lower_case \ --data_dir $GLUE_DIR/MNLI/ \ --max_seq_length 128 \ --per_gpu_train_batch_size 8 \ --learning_rate 2e-5 \ --num_train_epochs 3.0 \ --output_dir output_dir \ ``` The results are the following: ```bash ***** Eval results ***** acc = 0.8679706601466992 eval_loss = 0.4911287787382479 global_step = 18408 loss = 0.04755385363816904 ***** Eval results ***** acc = 0.8747965825874695 eval_loss = 0.45516540421714036 global_step = 18408 loss = 0.04755385363816904 ``` ##Multiple Choice Based on the script [`run_multiple_choice.py`](). #### Fine-tuning on SWAG Download [swag](https://github.com/rowanz/swagaf/tree/master/data) data ``` #training on 4 tesla V100(16GB) GPUS export SWAG_DIR=/path/to/swag_data_dir python ./examples/single_model_scripts/run_multiple_choice.py \ --model_type roberta \ --task_name swag \ --model_name_or_path roberta-base \ --do_train \ --do_eval \ --do_lower_case \ --data_dir $SWAG_DIR \ --learning_rate 5e-5 \ --num_train_epochs 3 \ --max_seq_length 80 \ --output_dir models_bert/swag_base \ --per_gpu_eval_batch_size=16 \ --per_gpu_train_batch_size=16 \ --gradient_accumulation_steps 2 \ --overwrite_output ``` Training with the defined hyper-parameters yields the following results: ``` ***** Eval results ***** eval_acc = 0.8338998300509847 eval_loss = 0.44457291918821606 ``` ## SQuAD Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py). #### Fine-tuning on SQuAD This example code fine-tunes BERT on the SQuAD dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large) on a single tesla V100 16GB. The data for SQuAD can be downloaded with the following links and should be saved in a $SQUAD_DIR directory. * [train-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json) * [dev-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json) * [evaluate-v1.1.py](https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py) ```bash export SQUAD_DIR=/path/to/SQUAD python run_squad.py \ --model_type bert \ --model_name_or_path bert-base-cased \ --do_train \ --do_eval \ --do_lower_case \ --train_file $SQUAD_DIR/train-v1.1.json \ --predict_file $SQUAD_DIR/dev-v1.1.json \ --per_gpu_train_batch_size 12 \ --learning_rate 3e-5 \ --num_train_epochs 2.0 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /tmp/debug_squad/ ``` 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 SQuAD: ```bash python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \ --model_type bert \ --model_name_or_path bert-base-cased \ --do_train \ --do_eval \ --do_lower_case \ --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 ../models/wwm_uncased_finetuned_squad/ \ --per_gpu_train_batch_size 24 \ --gradient_accumulation_steps 12 ``` Training with the previously defined hyper-parameters yields the following results: ```bash f1 = 93.15 exact_match = 86.91 ``` This fine-tuneds model is available as a checkpoint under the reference `bert-large-uncased-whole-word-masking-finetuned-squad`.