81 lines
3.1 KiB
Markdown
81 lines
3.1 KiB
Markdown
<!---
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Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# Language modelling examples
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This folder contains some scripts showing examples of *language model pre-training* with the 🤗 Transformers library.
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For straightforward use-cases you may be able to use these scripts without modification, although we have also
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included comments in the code to indicate areas that you may need to adapt to your own projects. The two scripts
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have almost identical arguments, but they differ in the type of LM they train - a causal language model (like GPT) or a
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masked language model (like BERT). Masked language models generally train more quickly and perform better when
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fine-tuned on new tasks with a task-specific output head, like text classification. However, their ability to generate
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text is weaker than causal language models.
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## Pre-training versus fine-tuning
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These scripts can be used to both *pre-train* a language model completely from scratch, as well as to *fine-tune*
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a language model on text from your domain of interest. To start with an existing pre-trained language model you
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can use the `--model_name_or_path` argument, or to train from scratch you can use the `--model_type` argument
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to indicate the class of model architecture to initialize.
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### Multi-GPU and TPU usage
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By default, these scripts use a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs
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can also be used by passing the name of the TPU resource with the `--tpu` argument.
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## run_mlm.py
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This script trains a masked language model.
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### Example command
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```bash
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python run_mlm.py \
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--model_name_or_path distilbert/distilbert-base-cased \
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--output_dir output \
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--dataset_name wikitext \
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--dataset_config_name wikitext-103-raw-v1
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```
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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.
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```bash
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python run_mlm.py \
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--model_name_or_path distilbert/distilbert-base-cased \
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--output_dir output \
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--train_file train_file_path
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```
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## run_clm.py
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This script trains a causal language model.
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### Example command
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```bash
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python run_clm.py \
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--model_name_or_path distilbert/distilgpt2 \
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--output_dir output \
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--dataset_name wikitext \
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--dataset_config_name wikitext-103-raw-v1
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```
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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.
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```bash
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python run_clm.py \
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--model_name_or_path distilbert/distilgpt2 \
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--output_dir output \
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--train_file train_file_path
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```
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