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* Validation split added: custom data files Validation split added in case of no validation file and loading custom data * Updated documentation with custom file usage Updated documentation with custom file usage * Update README.md * Update README.md * Update README.md * Made some suggested stylistic changes * Used logger instead of print. Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Made similar changes to add validation split In case of a missing validation file, a validation split will be used now. * max_train_samples to be used for training only max_train_samples got misplaced, now corrected so that it is applied on training data only, not whole data. * styled * changed ordering * Improved language of documentation Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Improved language of documentation Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Fixed styling issue * Update run_mlm.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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Language modelling examples
This folder contains some scripts showing examples of language model pre-training with the 🤗 Transformers library. For straightforward use-cases you may be able to use these scripts without modification, although we have also included comments in the code to indicate areas that you may need to adapt to your own projects. The two scripts have almost identical arguments, but they differ in the type of LM they train - a causal language model (like GPT) or a masked language model (like BERT). Masked language models generally train more quickly and perform better when fine-tuned on new tasks with a task-specific output head, like text classification. However, their ability to generate text is weaker than causal language models.
Pre-training versus fine-tuning
These scripts can be used to both pre-train a language model completely from scratch, as well as to fine-tune
a language model on text from your domain of interest. To start with an existing pre-trained language model you
can use the --model_name_or_path argument, or to train from scratch you can use the --model_type argument
to indicate the class of model architecture to initialize.
Multi-GPU and TPU usage
By default, these scripts use a MirroredStrategy and will use multiple GPUs effectively if they are available. TPUs
can also be used by passing the name of the TPU resource with the --tpu argument.
run_mlm.py
This script trains a masked language model.
Example command
python run_mlm.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--dataset_name wikitext \
--dataset_config_name wikitext-103-raw-v1
When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation.
python run_mlm.py \
--model_name_or_path distilbert-base-cased \
--output_dir output \
--train_file train_file_path
run_clm.py
This script trains a causal language model.
Example command
python run_clm.py \
--model_name_or_path distilgpt2 \
--output_dir output \
--dataset_name wikitext \
--dataset_config_name wikitext-103-raw-v1
When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation.
python run_clm.py \
--model_name_or_path distilgpt2 \
--output_dir output \
--train_file train_file_path