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.
## Language model fine-tuning
Based on the script `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
slower, but over-fitting would take 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
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
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.
