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## Language model training

Fine-tuning (or training from scratch) the library models for language modeling on a text dataset for GPT, GPT-2,
ALBERT, BERT, DistilBERT, RoBERTa, XLNet... GPT and GPT-2 are trained or fine-tuned using a causal language modeling
(CLM) loss while ALBERT, BERT, DistilBERT and RoBERTa are trained or fine-tuned using a masked language modeling (MLM)
loss. XLNet uses permutation language modeling (PLM), you can find more information about the differences between those
objectives in our [model summary](https://huggingface.co/transformers/model_summary.html).

There are two sets of scripts provided. The first set leverages the Trainer API. The second set with `no_trainer` in the suffix uses a custom training loop and leverages the 🤗 Accelerate library . Both sets use the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.

**Note:** The old script `run_language_modeling.py` is still available [here](https://github.com/huggingface/transformers/blob/main/examples/legacy/run_language_modeling.py).

The following examples, will run on datasets hosted on our [hub](https://huggingface.co/datasets) or with your own
text files for training and validation. We give examples of both below.

### 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
python run_clm.py \
    --model_name_or_path openai-community/gpt2 \
    --dataset_name wikitext \
    --dataset_config_name wikitext-2-raw-v1 \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --do_train \
    --do_eval \
    --output_dir /tmp/test-clm
```

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.

To run on your own training and validation files, use the following command:

```bash
python run_clm.py \
    --model_name_or_path openai-community/gpt2 \
    --train_file path_to_train_file \
    --validation_file path_to_validation_file \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --do_train \
    --do_eval \
    --output_dir /tmp/test-clm
```

This uses the built in HuggingFace `Trainer` for training. If you want to use a custom training loop, you can utilize or adapt the `run_clm_no_trainer.py` script. Take a look at the script for a list of supported arguments. An example is shown below:

```bash
python run_clm_no_trainer.py \
    --dataset_name wikitext \
    --dataset_config_name wikitext-2-raw-v1 \
    --model_name_or_path openai-community/gpt2 \
    --output_dir /tmp/test-clm
```

### GPT-2/GPT and causal language modeling with fill-in-the middle objective

The following example fine-tunes GPT-2 on WikiText-2 but using the Fill-in-middle training objective. FIM objective was proposed in [Efficient Training of Language Models to Fill in the Middle](https://arxiv.org/abs/2207.14255). They showed that autoregressive language models can learn to infill text after applying a straightforward transformation to the dataset, which simply moves a span of text from the middle of a document to its end.

We're using the raw WikiText-2 (no tokens were replaced before the tokenization). The loss here is that of causal language modeling.

```bash
python run_fim.py \
    --model_name_or_path gpt2 \
    --dataset_name wikitext \
    --dataset_config_name wikitext-2-raw-v1 \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --fim_rate 0.5 \
    --fim_spm_rate 0.2 \
    --do_train \
    --do_eval \
    --output_dir /tmp/test-clm
```

To run on your own training and validation files, use the following command:

```bash
python run_fim.py \
    --model_name_or_path gpt2 \
    --train_file path_to_train_file \
    --validation_file path_to_validation_file \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --fim_rate 0.5 \
    --fim_spm_rate 0.2 \
    --do_train \
    --do_eval \
    --output_dir /tmp/test-clm
```

This uses the built in HuggingFace `Trainer` for training. If you want to use a custom training loop, you can utilize or adapt the `run_fim_no_trainer.py` script. Take a look at the script for a list of supported arguments. An example is shown below:

```bash
python run_fim_no_trainer.py \
    --model_name_or_path gpt2 \
    --dataset_name wikitext \
    --dataset_config_name wikitext-2-raw-v1 \
    --model_name_or_path gpt2 \
    --fim_rate 0.5 \
    --fim_spm_rate 0.2 \
    --output_dir /tmp/test-clm
```

**Note**: Passing in FIM rate as `0.5` means that FIM transformations will be applied to the dataset with a probability of 50%. Whereas passing in FIM SPM rate as `0.2` means that 20% of FIM transformations will use SPM (or Suffix-Prefix-Middle) and the remaining 80% will use PSM (or Prefix-Suffix-Middle) mode of transformation.

### RoBERTa/BERT/DistilBERT 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).

```bash
python run_mlm.py \
    --model_name_or_path FacebookAI/roberta-base \
    --dataset_name wikitext \
    --dataset_config_name wikitext-2-raw-v1 \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --do_train \
    --do_eval \
    --output_dir /tmp/test-mlm
```

To run on your own training and validation files, use the following command:

```bash
python run_mlm.py \
    --model_name_or_path FacebookAI/roberta-base \
    --train_file path_to_train_file \
    --validation_file path_to_validation_file \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --do_train \
    --do_eval \
    --output_dir /tmp/test-mlm
```

If your dataset is organized with one sample per line, you can use the `--line_by_line` flag (otherwise the script
concatenates all texts and then splits them in blocks of the same length).

This uses the built in HuggingFace `Trainer` for training. If you want to use a custom training loop, you can utilize or adapt the `run_mlm_no_trainer.py` script. Take a look at the script for a list of supported arguments. An example is shown below:

```bash
python run_mlm_no_trainer.py \
    --dataset_name wikitext \
    --dataset_config_name wikitext-2-raw-v1 \
    --model_name_or_path FacebookAI/roberta-base \
    --output_dir /tmp/test-mlm
```

**Note:** On TPU, you should use the flag `--pad_to_max_length` in conjunction with the `--line_by_line` flag to make
sure all your batches have the same length.

### Whole word masking

This part was moved to https://github.com/huggingface/transformers-research-projects/tree/main/mlm_wwm.

### XLNet and permutation language modeling

XLNet uses a different training objective, which is permutation language modeling. It is an autoregressive method
to learn bidirectional contexts by maximizing the expected likelihood over all permutations of the input
sequence factorization order.

We use the `--plm_probability` flag to define the ratio of length of a span of masked tokens to surrounding
context length for permutation language modeling.

The `--max_span_length` flag may also be used to limit the length of a span of masked tokens used
for permutation language modeling.

Here is how to fine-tune XLNet on wikitext-2:

```bash
python run_plm.py \
    --model_name_or_path=xlnet/xlnet-base-cased \
    --dataset_name wikitext \
    --dataset_config_name wikitext-2-raw-v1 \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --do_train \
    --do_eval \
    --output_dir /tmp/test-plm
```

To fine-tune it on your own training and validation file, run:

```bash
python run_plm.py \
    --model_name_or_path=xlnet/xlnet-base-cased \
    --train_file path_to_train_file \
    --validation_file path_to_validation_file \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --do_train \
    --do_eval \
    --output_dir /tmp/test-plm
```

If your dataset is organized with one sample per line, you can use the `--line_by_line` flag (otherwise the script
concatenates all texts and then splits them in blocks of the same length).

**Note:** On TPU, you should use the flag `--pad_to_max_length` in conjunction with the `--line_by_line` flag to make
sure all your batches have the same length.

## Streaming

To use the streaming dataset mode which can be very useful for large datasets, add `--streaming` to the command line. This is supported by `run_mlm.py`, `run_clm.py` and `run_fim.py`. Make sure to adapt the other scripts to your use case by taking inspiration from them.

## Low Cpu Memory Usage

To use low cpu memory mode which can be very useful for LLM, add `--low_cpu_mem_usage` to the command line. This is currently supported by `run_clm.py`,`run_mlm.py`, `run_plm.py`, `run_fim.py`, `run_mlm_no_trainer.py`, `run_clm_no_trainer.py` and `run_fim_no_trainer.py`.

## Creating a model on the fly

When training a model from scratch, configuration values may be overridden with the help of `--config_overrides`:


```bash
python run_clm.py --model_type gpt2 --tokenizer_name openai-community/gpt2 \ --config_overrides="n_embd=1024,n_head=16,n_layer=48,n_positions=102" \
[...]
```

This feature is only available in `run_clm.py`, `run_plm.py`, `run_mlm.py` and `run_fim.py`.
