Finalize lm examples (#8188)
* Finish the cleanup of the language-modeling examples * Update main README * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Apply suggestions from code review Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com> * Propagate changes Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
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Sylvain Gugger
GitHub
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@ -5,7 +5,9 @@ Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.2+.
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Here is the list of all our examples:
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- **grouped by task** (all official examples work for multiple models)
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- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might just lack some features),
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- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might
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just lack some features),
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- whether or not they leverage the [🤗 Datasets](https://github.com/huggingface/datasets) library.
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- links to **Colab notebooks** to walk through the scripts and run them easily,
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- links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup.
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@ -31,19 +33,19 @@ git checkout tags/v3.4.0
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## The Big Table of Tasks
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| Task | Example datasets | Trainer support | TFTrainer support | Colab
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|---|---|:---:|:---:|:---:|
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| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling) | Raw text | ✅ | - | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
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| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification) | GLUE, XNLI | ✅ | ✅ | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb)
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| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER | ✅ | ✅ | -
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| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice) | SWAG, RACE, ARC | ✅ | ✅ | [](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
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| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering) | SQuAD | ✅ | ✅ | -
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| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation) | - | n/a | n/a | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
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| [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation) | All | - | - | -
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| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq) | CNN/Daily Mail | ✅ | - | -
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| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq) | WMT | ✅ | - | -
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| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology) | - | - | - | -
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| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial) | HANS | ✅ | - | -
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| Task | Example datasets | Trainer support | TFTrainer support | 🤗 Datasets | Colab
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|---|---|:---:|:---:|:---:|:---:|
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| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling) | Raw text | ✅ | - | ✅ | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
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| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification) | GLUE, XNLI | ✅ | ✅ | ✅ | [](https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb)
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| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER | ✅ | ✅ | - | -
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| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice) | SWAG, RACE, ARC | ✅ | ✅ | - | [](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
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| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering) | SQuAD | ✅ | ✅ | - | -
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| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation) | - | n/a | n/a | - | [](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
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| [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation) | All | - | - | - | -
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| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq) | CNN/Daily Mail | ✅ | - | - | -
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| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq) | WMT | ✅ | - | - | -
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| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology) | - | - | - | - | -
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| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial) | HANS | ✅ | - | - | -
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<br>
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@ -1,16 +1,19 @@
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## Language model training
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Based on the script [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py).
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Fine-tuning (or training from scratch) the library models for language modeling on a text dataset for GPT, GPT-2,
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ALBERT, BERT, DistilBERT, RoBERTa, XLNet... GPT and GPT-2 are trained or fine-tuned using a causal language modeling
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(CLM) loss while ALBERT, BERT, DistilBERT and RoBERTa are trained or fine-tuned using a masked language modeling (MLM)
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loss. XLNet uses permutation language modeling (PLM), you can find more information about the differences between those
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objectives in our [model summary](https://huggingface.co/transformers/model_summary.html).
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Fine-tuning (or training from scratch) the library models for language modeling on a text dataset for GPT, GPT-2, BERT, DistilBERT and RoBERTa. GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT, DistilBERT and RoBERTa
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are fine-tuned using a masked language modeling (MLM) loss.
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These scripts leverage the 🤗 Datasets library and the Trainer API. You can easily customize them to your needs if you
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need extra processing on your datasets.
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Before running the following example, you should get a file that contains text on which the language model will be
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trained or 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/).
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**Note:** The old script `run_language_modeling.py` is still available
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[here](https://github.com/huggingface/transformers/blob/master/examples/contrib/legacy/language-modeling/run_language_modeling.py).
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We will refer to two different files: `$TRAIN_FILE`, which contains text for training, and `$TEST_FILE`, which contains
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text that will be used for evaluation.
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The following examples, will run on a datasets hosted on our [hub](https://huggingface.co/datasets) or with your own
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text files for training and validation. We give examples of both below.
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### GPT-2/GPT and causal language modeling
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@ -18,66 +21,99 @@ The following example fine-tunes GPT-2 on WikiText-2. We're using the raw WikiTe
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the tokenization). The loss here is that of causal language modeling.
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```bash
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export TRAIN_FILE=/path/to/dataset/wiki.train.raw
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export TEST_FILE=/path/to/dataset/wiki.test.raw
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python run_language_modeling.py \
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--output_dir=output \
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--model_type=gpt2 \
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--model_name_or_path=gpt2 \
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python run_clm.py \
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--model_name_or_path gpt2 \
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--dataset_name wikitext \
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--dataset_config_name wikitext-2-raw-v1 \
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--do_train \
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--train_data_file=$TRAIN_FILE \
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--do_eval \
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--eval_data_file=$TEST_FILE
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--output_dir /tmp/test-clm
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```
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This takes about half an hour to train on a single K80 GPU and about one minute for the evaluation to run. It reaches
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a score of ~20 perplexity once fine-tuned on the dataset.
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To run on your own training and validation files, use the following command:
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```bash
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python run_clm.py \
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--model_name_or_path gpt2 \
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--train_file path_to_train_file \
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--validation_file path_to_validation_file \
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--do_train \
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--do_eval \
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--output_dir /tmp/test-clm
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```
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### RoBERTa/BERT/DistilBERT and masked language modeling
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The following example fine-tunes RoBERTa on WikiText-2. Here too, we're using the raw WikiText-2. The loss is different
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as BERT/RoBERTa have a bidirectional mechanism; we're therefore using the same loss that was used during their
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pre-training: masked language modeling.
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In accordance to the RoBERTa paper, we use dynamic masking rather than static masking. The model may, therefore, converge
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slightly slower (over-fitting takes more epochs).
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We use the `--mlm` flag so that the script may change its loss function.
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If using whole-word masking, use both the`--mlm` and `--wwm` flags.
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In accordance to the RoBERTa paper, we use dynamic masking rather than static masking. The model may, therefore,
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converge slightly slower (over-fitting takes more epochs).
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```bash
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export TRAIN_FILE=/path/to/dataset/wiki.train.raw
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export TEST_FILE=/path/to/dataset/wiki.test.raw
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python run_language_modeling.py \
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--output_dir=output \
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--model_type=roberta \
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--model_name_or_path=roberta-base \
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python run_mlm.py \
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--model_name_or_path roberta-base \
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--dataset_name wikitext \
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--dataset_config_name wikitext-2-raw-v1 \
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--do_train \
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--train_data_file=$TRAIN_FILE \
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--do_eval \
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--eval_data_file=$TEST_FILE \
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--mlm \
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--whole_word_mask
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--output_dir /tmp/test-mlm
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```
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For Chinese models, it's same with English model with only `--mlm`. If using whole-word masking, we need to generate a reference files, because it's char level.
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To run on your own training and validation files, use the following command:
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**Q :** Why ref file ?
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```bash
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python run_clm.py \
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--model_name_or_path roberta-base \
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--train_file path_to_train_file \
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--validation_file path_to_validation_file \
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--do_train \
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--do_eval \
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--output_dir /tmp/test-clm
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```
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**A :** Suppose we have a Chinese sentence like : `我喜欢你` The original Chinese-BERT will tokenize it as `['我','喜','欢','你']` in char level.
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Actually, `喜欢` is a whole word. For whole word mask proxy, We need res like `['我','喜','##欢','你']`.
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So we need a ref file to tell model which pos of BERT original token should be added `##`.
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### Whole word masking
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The BERT authors released a new version of BERT using Whole Word Masking in May 2019. Instead of masking randomly
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selected tokens (which may be aprt of words), they mask randomly selected words (masking all the tokens corresponding
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to that word). This technique has been refined for Chinese in [this paper](https://arxiv.org/abs/1906.08101).
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To fine-tune a model using whole word masking, use the following script:
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python run_mlm_wwm.py \
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--model_name_or_path roberta-base \
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--dataset_name wikitext \
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--dataset_config_name wikitext-2-raw-v1 \
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--do_train \
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--do_eval \
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--output_dir /tmp/test-mlm-wwm
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```
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For Chinese models, we need to generate a reference files (which requires the ltp library), because it's tokenized at
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the character level.
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**Q :** Why a reference file?
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**A :** Suppose we have a Chinese sentence like: `我喜欢你` The original Chinese-BERT will tokenize it as
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`['我','喜','欢','你']` (character level). But `喜欢` is a whole word. For whole word masking proxy, we need a result
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like `['我','喜','##欢','你']`, so we need a reference file to tell the model which position of the BERT original token
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should be added `##`.
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**Q :** Why LTP ?
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**A :** Cause the best known Chinese WWM BERT is [Chinese-BERT-wwm](https://github.com/ymcui/Chinese-BERT-wwm) by HIT. It works well on so many Chines Task like CLUE (Chinese GLUE).
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They use LTP, so if we want to fine-tune their model, we need LTP.
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**A :** Cause the best known Chinese WWM BERT is [Chinese-BERT-wwm](https://github.com/ymcui/Chinese-BERT-wwm) by HIT.
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It works well on so many Chines Task like CLUE (Chinese GLUE). They use LTP, so if we want to fine-tune their model,
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we need LTP.
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Now LTP only only works well on `transformers==3.2.0`. So we don't add it to requirements.txt.
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You need to check to `3.2.0` for `run_chinese_ref.py`. And the code could be found in `examples/contrib`.
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You need to create a separate enviromnent with this version of Transformers to run the `run_chinese_ref.py` script that
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will create the reference files. The script is in `examples/contrib`. Once in the proper enviromnent, run the
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following:
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```bash
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@ -87,31 +123,25 @@ export BERT_RESOURCE=/path/to/bert/tokenizer
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export SAVE_PATH=/path/to/data/ref.txt
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python examples/contrib/run_chinese_ref.py \
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--file_name=$TRAIN_FILE \
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--ltp=$LTP_RESOURCE \
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--bert=$BERT_RESOURCE \
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--save_path=$SAVE_PATH
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--file_name=path_to_train_or_eval_file \
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--ltp=path_to_ltp_tokenizer \
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--bert=path_to_bert_tokenizer \
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--save_path=path_to_reference_file
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```
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Now Chinese Ref is only supported by `LineByLineWithRefDataset` Class, so we need add `line_by_line` flag:
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Then you can run the script like this:
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```bash
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export TRAIN_FILE=/path/to/dataset/wiki.train.raw
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export TEST_FILE=/path/to/dataset/wiki.test.raw
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export REF_FILE=/path/to/ref.txt
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python run_language_modeling.py \
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--output_dir=output \
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--model_type=roberta \
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--model_name_or_path=roberta-base \
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python run_mlm_wwm.py \
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--model_name_or_path roberta-base \
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--train_file path_to_train_file \
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--validation_file path_to_validation_file \
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--train_ref_file path_to_train_chinese_ref_file \
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--validation_ref_file path_to_validation_chinese_ref_file \
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--do_train \
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--train_data_file=$TRAIN_FILE \
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--chinese_ref_file=$REF_FILE \
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--do_eval \
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--eval_data_file=$TEST_FILE \
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--mlm \
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--line_by_line \
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--whole_word_mask
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--output_dir /tmp/test-mlm-wwm
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```
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### XLNet and permutation language modeling
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@ -126,15 +156,26 @@ context length for permutation language modeling.
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The `--max_span_length` flag may also be used to limit the length of a span of masked tokens used
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for permutation language modeling.
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```bash
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export TRAIN_FILE=/path/to/dataset/wiki.train.raw
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export TEST_FILE=/path/to/dataset/wiki.test.raw
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Here is how to fine-tun XLNet on wikitext-2:
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python run_language_modeling.py \
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--output_dir=output \
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```bash
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python run_plm.py \
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--model_name_or_path=xlnet-base-cased \
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--dataset_name wikitext \
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--dataset_config_name wikitext-2-raw-v1 \
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--do_train \
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--train_data_file=$TRAIN_FILE \
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--do_eval \
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--eval_data_file=$TEST_FILE \
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--output_dir /tmp/test-plm
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```
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To fine-tune it on your own training and validation file, run:
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```bash
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python run_plm.py \
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--model_name_or_path=xlnet-base-cased \
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--train_file path_to_train_file \
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--validation_file path_to_validation_file \
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--do_train \
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--do_eval \
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--output_dir /tmp/test-plm
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```
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@ -175,10 +175,10 @@ def main():
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# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
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# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
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# (the dataset will be downloaded automatically from the datasets Hub
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# (the dataset will be downloaded automatically from the datasets Hub).
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#
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# For CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this
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# behavior (see below)
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# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
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# 'text' is found. You can easily tweak this behavior (see below).
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#
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# In distributed training, the load_dataset function guarantee that only one local process can concurrently
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# download the dataset.
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@ -0,0 +1,325 @@
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# coding=utf-8
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# Copyright 2020 The HuggingFace Team All rights reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a
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text file or a dataset.
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Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
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https://huggingface.co/models?filter=masked-lm
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"""
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# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
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import json
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import logging
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import math
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import os
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import sys
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from dataclasses import dataclass, field
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from typing import Optional
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|
||||
from datasets import Dataset, load_dataset
|
||||
|
||||
import transformers
|
||||
from transformers import (
|
||||
CONFIG_MAPPING,
|
||||
MODEL_FOR_MASKED_LM_MAPPING,
|
||||
AutoConfig,
|
||||
AutoModelForMaskedLM,
|
||||
AutoTokenizer,
|
||||
DataCollatorForWholeWordMask,
|
||||
HfArgumentParser,
|
||||
Trainer,
|
||||
TrainingArguments,
|
||||
set_seed,
|
||||
)
|
||||
from transformers.trainer_utils import is_main_process
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
|
||||
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelArguments:
|
||||
"""
|
||||
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
|
||||
"""
|
||||
|
||||
model_name_or_path: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "The model checkpoint for weights initialization."
|
||||
"Don't set if you want to train a model from scratch."
|
||||
},
|
||||
)
|
||||
model_type: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
|
||||
)
|
||||
config_name: Optional[str] = field(
|
||||
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
||||
)
|
||||
tokenizer_name: Optional[str] = field(
|
||||
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
|
||||
)
|
||||
cache_dir: Optional[str] = field(
|
||||
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
|
||||
)
|
||||
use_fast_tokenizer: bool = field(
|
||||
default=True,
|
||||
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class DataTrainingArguments:
|
||||
"""
|
||||
Arguments pertaining to what data we are going to input our model for training and eval.
|
||||
"""
|
||||
|
||||
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
|
||||
validation_file: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
|
||||
)
|
||||
train_ref_file: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
|
||||
)
|
||||
validation_ref_file: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
|
||||
)
|
||||
overwrite_cache: bool = field(
|
||||
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
||||
)
|
||||
max_seq_length: Optional[int] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "The maximum total input sequence length after tokenization. Sequences longer "
|
||||
"than this will be truncated. Default to the max input length of the model."
|
||||
},
|
||||
)
|
||||
preprocessing_num_workers: Optional[int] = field(
|
||||
default=None,
|
||||
metadata={"help": "The number of processes to use for the preprocessing."},
|
||||
)
|
||||
mlm_probability: float = field(
|
||||
default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
|
||||
)
|
||||
|
||||
def __post_init__(self):
|
||||
if self.train_file is not None:
|
||||
extension = self.train_file.split(".")[-1]
|
||||
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
|
||||
if self.validation_file is not None:
|
||||
extension = self.validation_file.split(".")[-1]
|
||||
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
|
||||
|
||||
|
||||
def add_chinese_references(dataset, ref_file):
|
||||
with open(ref_file, "r", encoding="utf-8") as f:
|
||||
refs = [json.loads(line) for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
|
||||
assert len(dataset) == len(refs)
|
||||
|
||||
dataset_dict = {c: dataset[c] for c in dataset.column_names}
|
||||
dataset_dict["chinese_ref"] = refs
|
||||
return Dataset.from_dict(dataset_dict)
|
||||
|
||||
|
||||
def main():
|
||||
# See all possible arguments in src/transformers/training_args.py
|
||||
# or by passing the --help flag to this script.
|
||||
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
||||
|
||||
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
|
||||
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
||||
# If we pass only one argument to the script and it's the path to a json file,
|
||||
# let's parse it to get our arguments.
|
||||
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
||||
else:
|
||||
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
||||
|
||||
if (
|
||||
os.path.exists(training_args.output_dir)
|
||||
and os.listdir(training_args.output_dir)
|
||||
and training_args.do_train
|
||||
and not training_args.overwrite_output_dir
|
||||
):
|
||||
raise ValueError(
|
||||
f"Output directory ({training_args.output_dir}) already exists and is not empty."
|
||||
"Use --overwrite_output_dir to overcome."
|
||||
)
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(
|
||||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||
datefmt="%m/%d/%Y %H:%M:%S",
|
||||
level=logging.INFO if is_main_process(training_args.local_rank) else logging.WARN,
|
||||
)
|
||||
|
||||
# Log on each process the small summary:
|
||||
logger.warning(
|
||||
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
|
||||
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
|
||||
)
|
||||
# Set the verbosity to info of the Transformers logger (on main process only):
|
||||
if is_main_process(training_args.local_rank):
|
||||
transformers.utils.logging.set_verbosity_info()
|
||||
logger.info("Training/evaluation parameters %s", training_args)
|
||||
|
||||
# Set seed before initializing model.
|
||||
set_seed(training_args.seed)
|
||||
|
||||
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
|
||||
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
|
||||
# (the dataset will be downloaded automatically from the datasets Hub).
|
||||
#
|
||||
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
|
||||
# 'text' is found. You can easily tweak this behavior (see below).
|
||||
#
|
||||
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
|
||||
# download the dataset.
|
||||
data_files = {}
|
||||
if data_args.train_file is not None:
|
||||
data_files["train"] = data_args.train_file
|
||||
if data_args.validation_file is not None:
|
||||
data_files["validation"] = data_args.train_file
|
||||
extension = data_args.train_file.split(".")[-1]
|
||||
if extension == "txt":
|
||||
extension = "text"
|
||||
datasets = load_dataset(extension, data_files=data_files)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
# Load pretrained model and tokenizer
|
||||
#
|
||||
# Distributed training:
|
||||
# The .from_pretrained methods guarantee that only one local process can concurrently
|
||||
# download model & vocab.
|
||||
if model_args.config_name:
|
||||
config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
|
||||
elif model_args.model_name_or_path:
|
||||
config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
|
||||
else:
|
||||
config = CONFIG_MAPPING[model_args.model_type]()
|
||||
logger.warning("You are instantiating a new config instance from scratch.")
|
||||
|
||||
if model_args.tokenizer_name:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
||||
)
|
||||
elif model_args.model_name_or_path:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
|
||||
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
|
||||
)
|
||||
|
||||
if model_args.model_name_or_path:
|
||||
model = AutoModelForMaskedLM.from_pretrained(
|
||||
model_args.model_name_or_path,
|
||||
from_tf=bool(".ckpt" in model_args.model_name_or_path),
|
||||
config=config,
|
||||
cache_dir=model_args.cache_dir,
|
||||
)
|
||||
else:
|
||||
logger.info("Training new model from scratch")
|
||||
model = AutoModelForMaskedLM.from_config(config)
|
||||
|
||||
model.resize_token_embeddings(len(tokenizer))
|
||||
|
||||
# Preprocessing the datasets.
|
||||
# First we tokenize all the texts.
|
||||
if training_args.do_train:
|
||||
column_names = datasets["train"].column_names
|
||||
else:
|
||||
column_names = datasets["validation"].column_names
|
||||
text_column_name = "text" if "text" in column_names else column_names[0]
|
||||
|
||||
def tokenize_function(examples):
|
||||
# Remove empty lines
|
||||
examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
|
||||
return tokenizer(examples["text"], truncation=True, max_length=data_args.max_seq_length)
|
||||
|
||||
tokenized_datasets = datasets.map(
|
||||
tokenize_function,
|
||||
batched=True,
|
||||
num_proc=data_args.preprocessing_num_workers,
|
||||
remove_columns=[text_column_name],
|
||||
load_from_cache_file=not data_args.overwrite_cache,
|
||||
)
|
||||
|
||||
# Add the chinese references if provided
|
||||
if data_args.train_ref_file is not None:
|
||||
tokenized_datasets["train"] = add_chinese_references(tokenized_datasets["train"], data_args.train_ref_file)
|
||||
if data_args.valid_ref_file is not None:
|
||||
tokenized_datasets["validation"] = add_chinese_references(
|
||||
tokenized_datasets["validation"], data_args.validation_ref_file
|
||||
)
|
||||
|
||||
# Data collator
|
||||
# This one will take care of randomly masking the tokens.
|
||||
data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
|
||||
|
||||
# Initialize our Trainer
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=tokenized_datasets["train"] if training_args.do_train else None,
|
||||
eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None,
|
||||
tokenizer=tokenizer,
|
||||
data_collator=data_collator,
|
||||
)
|
||||
|
||||
# Training
|
||||
if training_args.do_train:
|
||||
trainer.train(
|
||||
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
|
||||
)
|
||||
trainer.save_model() # Saves the tokenizer too for easy upload
|
||||
|
||||
# Evaluation
|
||||
results = {}
|
||||
if training_args.do_eval:
|
||||
logger.info("*** Evaluate ***")
|
||||
|
||||
eval_output = trainer.evaluate()
|
||||
|
||||
perplexity = math.exp(eval_output["eval_loss"])
|
||||
results["perplexity"] = perplexity
|
||||
|
||||
output_eval_file = os.path.join(training_args.output_dir, "eval_results_mlm_wwm.txt")
|
||||
if trainer.is_world_process_zero():
|
||||
with open(output_eval_file, "w") as writer:
|
||||
logger.info("***** Eval results *****")
|
||||
for key, value in results.items():
|
||||
logger.info(f" {key} = {value}")
|
||||
writer.write(f"{key} = {value}\n")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def _mp_fn(index):
|
||||
# For xla_spawn (TPUs)
|
||||
main()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -96,7 +96,7 @@ class DataTrainingArguments:
|
|||
default=None,
|
||||
metadata={
|
||||
"help": "The maximum total input sequence length after tokenization. Sequences longer "
|
||||
"than this will be truncated."
|
||||
"than this will be truncated. Default to the max input length of the model."
|
||||
},
|
||||
)
|
||||
preprocessing_num_workers: Optional[int] = field(
|
||||
|
|
@ -172,10 +172,10 @@ def main():
|
|||
|
||||
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
|
||||
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
|
||||
# (the dataset will be downloaded automatically from the datasets Hub
|
||||
# (the dataset will be downloaded automatically from the datasets Hub).
|
||||
#
|
||||
# For CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this
|
||||
# behavior (see below)
|
||||
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
|
||||
# 'text' is found. You can easily tweak this behavior (see below).
|
||||
#
|
||||
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
|
||||
# download the dataset.
|
||||
|
|
|
|||
|
|
@ -177,7 +177,7 @@ def main():
|
|||
set_seed(training_args.seed)
|
||||
|
||||
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
|
||||
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub
|
||||
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
|
||||
#
|
||||
# For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
|
||||
# sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
|
||||
|
|
|
|||
|
|
@ -190,10 +190,10 @@ def main():
|
|||
|
||||
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
|
||||
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
|
||||
# (the dataset will be downloaded automatically from the datasets Hub
|
||||
# (the dataset will be downloaded automatically from the datasets Hub).
|
||||
#
|
||||
# For CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this
|
||||
# behavior (see below)
|
||||
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
|
||||
# 'text' is found. You can easily tweak this behavior (see below).
|
||||
#
|
||||
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
|
||||
# download the dataset.
|
||||
|
|
|
|||
Loading…
Reference in New Issue