transnormerA lexical normalizer for historical spelling variants using a transformer architecture.
transnormer
If you have a GPU available, you should first install and set up a conda environment.
conda install -y pip
conda create -y --name <environment-name> python=3.9 pip
conda activate <environment-name>
conda install -y cudatoolkit=11.3.1 cudnn=8.3.2 -c conda-forge
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
pip install torch==1.12.1+cu113 torchvision torchaudio -f https://download.pytorch.org/whl/torch_stable.htmlSet up a virtual environment, e.g. like this
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pippip install git+https://github.com/ybracke/transnormer.git# Clone repo from GitHub
git clone git@github.com:ybracke/transnormer.git
cd ./transnormer
# install package in editable mode
pip install -e .
# install development requirements
pip install -r requirements-dev.txtTo train a model you need the following resources:
$ python3 src/transnormer/models/model_train.py.For more details, see below
pred_eval.sh. Then run: bash pred_eval.shFor more details, see sections on Generation and Evaluation.
venv source .venv/bin/activateconda activate <environment-name>
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/1):
export CUDA_VISIBLE_DEVICES=1gpu = "cuda:0" in config fileexport TOKENIZERS_PARALLELISM=false to get rid of parallelism warning messagesSee repository transnormer-data
Specify the training parameters in the config file
Run training script: $ python3 src/transnormer/models/model_train.py. (Don't forget to start your virtual environment first; see Installation.) Training can take multiple hours, so consider using nohup: $ nohup nice python3 src/transnormer/models/train_model.py &
The file training_config.toml serves as a comprehensive configuration guide for customizing and fine-tuning the training process of a language model using the specified parameters.
Please note that the provided configuration settings and parameters are examples. You can customize them to fit your specific training requirements. Refer to the comments within the configuration file for additional information and guidance on modifying these parameters for optimal training outcomes.
The following paragraphs provide detailed explanations of each section and parameter within the configuration file to facilitate effective model training.
The gpu parameter allows you to specify the GPU device for training. You can set it to the desired GPU identifier, such as "cuda:0", ensuring compatibility with the CUDA environment. Remember to set the appropriate CUDA visible devices beforehand using if required (e.g. export CUDA_VISIBLE_DEVICES=1 to use only the GPU with index 1).
The random_seed parameter defines a fixed random seed (42 in the default settings) to ensure reproducibility of the training process. This enables consistent results across different runs.
The [data] section includes paths to training, validation, and test datasets. The paths_train, paths_validation, and paths_test parameters provide paths to respective JSONL files containing data examples. Additionally, n_examples_train, n_examples_validation, and n_examples_test specify the number of examples to be used from each dataset split during training.
Both paths_{split} and n_examples_{split} are lists. The number at n_examples_{split}[i] refers to the number of examples to use from the data specified at paths_{split}[i]. Hence n_examples_{split} must be the same length as paths_{split}. Setting n_examples_{split}[i] to a value higher than the number of examples in paths_{split}[i] ensures that all examples in this split will be used, but no oversampling is applied.
The [tokenizer] section holds settings related to tokenization of input and output sequences. You can specify tokenizer_input and tokenizer_output models. If you omit tokenizer_output, tokenizer_input will be used as the output tokenizer as well. If you omit tokenizer_input, the program will try to use the tokenizer of the checkpoint given under language_model.
You can specify an input_transliterator for data preprocessing. This option is not implemented for the byte-based models and might be removed in the future.
You can adjust min_length_input and max_length_input to filter inputs before traing. You can set max_length_output to define the maximum token lengths of output sequences, though this is not recommended and the property might be removed.
Under [language_models], you can choose the language model(s) to be retrained. It is possible to either use a byte-based encoder-decoder as the base model or two subword-based models (encoder and decoder). Accordingly the config file must either specify a checkpoint_encoder_decoder parameter, which points to the checkpoint of the chosen encoder-decoder model or two parameters, checkpoint_encoder (for historic language) and checkpoint_decoder (for modern language).
This section may change in the future, see this issue.
The [training_hyperparams] section encompasses essential training parameters, such as batch_size (determines the number of examples in each training batch), epochs (indicates the number of training epochs) , and (not actually used). You can control the frequency of logging, evaluation, and model saving using learning_ratelogging_steps, eval_steps, and save_steps respectively. eval_strategy defines how often evaluation occurs, and fp16 toggles half-precision training.
This section may change in the future, see this issue.
The [beam_search_decoding] section contains parameters related to beam search decoding during inference. no_repeat_ngram_size prevents n-grams of a certain size from repeating. (Note that what is a sensible value for this parameter is different depending on the tokenization. For a char/byte-based (aka "tokenizer-free") model, set this to higher value than for subword-based models.) early_stopping enables stopping decoding when early stopping criteria are met. length_penalty controls the trade-off between sequence length and probability. num_beams specifies the number of beams to use in beam search.
This section may change in the future, see this issue.
We may want to fine-tune a model that is already the product of fine-tuning. We call the first fine-tuned model 'checkpoint-X' and the second model 'checkpoint-Y'. To train checkpoint-Y from checkpoint-X simply add the path to checkpoint-X under language_models in training_config.toml.
To clarify, checkpoint-Y was created like this:
original pretrained model (e.g. byt5-small) -> checkpoint-X -> checkpoint-Y
Thus, in order to keep track of the full provenance of checkpoint-Y, we must not only keep checkpoint-Y's training_config.toml but also keep the directory where checkpoint-X and its training_config.toml is located.
The fastest way to create normalizations and get evaluation metrics is to run the bash script:
bash pred_eval.sh
This runs the scripts for generation and evaluation and performs the copy/rename operations described in the following.
The script src/transnormer/models/generate.py generates normalizations given a config file. This produces at JSONL file with generated normalizations.
usage: generate.py [-h] [-c CONFIG] [-o OUT]
Generates normalizations given a configuration file that specifies the model, the data and parameters.
optional arguments:
-h, --help show this help message and exit
-c CONFIG, --config CONFIG
Path to the config file (TOML)
-o OUT, --out OUT Path to the output file (JSONL)Example call:
python3 src/transnormer/models/generate.py -c test_config.toml --out <path>The test config file configures which device, data, tokenizer, model and generation parameters are used when generating normalizations. Refer to test_config.toml for a template and the description of the training config file for a detailed description of the sections. Note that, currently, only a single test data file is allowed as input.
Rename and copy current test_config.toml:
# in the transformer directory call
filename=`md5sum test_config.toml | head -c 8`.toml
cp test_config.toml hidden/test_configs/$filenameRename the predictions file (e.g. hidden/predictions/preds.jsonl) to a unique name like this:
# go to predictions directory
cd hidden/predictions
# rename pred file
filename=`md5sum preds.jsonl | head -c 8`.jsonl
mv preds.jsonl $filenameThe script src/transnormer/evaluation/evaluate.py computes a harmonized accuracy score and the normalized Levenshtein distance. The metric and its computation are adopted from Bawden et al. (2022).
usage: evaluate.py [-h] --input-type {jsonl,text} [--ref-file REF_FILE] [--pred-file PRED_FILE]
[--ref-field REF_FIELD] [--pred-field PRED_FIELD] -a ALIGN_TYPES [--sent-wise-file SENT_WISE_FILE]
[--test-config TEST_CONFIG]
Compute evaluation metric(s) for string-to-string normalization (see Bawden et al. 2022). Choose --align-type=both for a harmonized accuracy score.
optional arguments:
-h, --help show this help message and exit
--input-type {jsonl,text}
Type of input files: jsonl or text
--ref-file REF_FILE Path to the input file containing reference normalizations (typically a gold standard)
--pred-file PRED_FILE
Path to the input file containing predicted normalizations
--ref-field REF_FIELD
Name of the field containing reference (for jsonl input)
--pred-field PRED_FIELD
Name of the field containing prediction (for jsonl input)
-a ALIGN_TYPES, --align-types ALIGN_TYPES
Which file's tokenisation to use as reference for alignment. Valid choices are 'both', 'ref',
'pred'. Multiple choices are possible (comma separated)
--sent-wise-file SENT_WISE_FILE
Path to a file where the sentence-wise accuracy scores get saved. For pickled output (list),
the path must match /*.pkl/. Textual output is a comma-separated list
--test-config TEST_CONFIG
Path to the file containing the test configurationsExample call:
python3 src/transnormer/evaluation/evaluate.py \
--input-type jsonl --ref-file hidden/predictions/d037b975.jsonl \
--pred-file hidden/predictions/d037b975.jsonl \
--ref-field=norm --pred-field=pred -a both \
--sent-wise-file hidden/sent_scores/sent_scores_d037b975.pkl \
--test-config hidden/test_configs/d1b1ea77.toml \
>> hidden/eval.jsonlIn this case, the gold normalizations ("ref") and auto-generated normalizations ("pred") are stored in the same JSONL file, therefore --ref-file and --pred-file take the same argument. If ref and pred texts are stored in different files, the files must be in the same order (i.e. example in line 1 of the ref-file refers to the example in line 1 of the pred-file, etc.). Global evaluation metrics are printed to stdout by default and can be redirected, as in the example above.
If you have a single JSONL file with original input, predictions and gold labels, you probably want to write the sentence-wise accuracy scores to this file, that have been computed by evaluate.py. This can be done with src/transnormer/evaluation/add_sent_scores.py:
usage: add_sent_scores.py [-h] [-p PROPERTY] scores data
Write sentence-wise accuracy scores stored SCORES to DATA (jsonl file)
positional arguments:
scores Scores file (either pickled (*.pkl) or comma-separated plain-text).
data Data file (JSONL)
optional arguments:
-h, --help show this help message and exit
-p PROPERTY, --property PROPERTY
Name for the property in which the score gets stored (default: 'score')Example call:
python3 src/transnormer/evaluation/add_sent_scores.py hidden/sent_scores.pkl hidden/predictions/8ae3fd47.jsonlpython3 src/transnormer/evaluation/add_sent_scores.py hidden/sent_scores.pkl hidden/predictions/8ae3fd47.jsonl -p score_iTODO, see this issue
Use jq to create a text-only version from the JSONL files containing the predictions and then call diff on that. Example:
jq -r '.norm' ./8ae3fd47.jsonl > norm
jq -r '.pred' ./8ae3fd47.jsonl > pred
code --diff norm predNote: This section will be continously updated.
Scores on a test set extracted from the DTA EvalCorpus (13 documents, ~18,000 sentences; ~400,000 tokens):
| Method | WordAcc | WordAcc (-i) |
|---|---|---|
| identity | 79.59 | 79.80 |
| translit | 93.91 | 94.17 |
| transnormer | 98.93 | 99.18 |
The metric used is the harmonized word accurracy explained above; -i denotes a case insensitive version (i.e. deviations in casing between prediction and gold normalizaiton are ignored).
For the baseline method identity the historical text is simply treated as the normalization and compared to the gold normalization. The method translit is a version of identity, where extinct German graphemes are replaced by their modern counterparts (e.g. replaces every ſ with s). The transnormer model used here is a byt5-small model, with downstream training on a different section of the DTA EvalCorpus (~204K sentences).
In this section you find information on the institutional and theoretical background of the project.
This project is developed at the Berlin-Brandenburg Academy of Sciences and Humanities (Berlin-Brandenburgische Akademie der Wissenschaften, BBAW) within the national research data infrastructure (Nationale Forschungsdateninfrastruktur, NFDI) Text+.
We use a transformer encoder-decoder model. The encoder-decoder gets warm-started with pre-trained models and fine-tuned on a dataset for lexical normalization.
Intuition: We create a model from an encoder that knows a lot about historical language, and a decoder that knows a lot about modern language and plug them together by training them on gold-normalized data. Both encoder and decoder can be pre-trained on large quantities of unlabeled data (historic/modern), which are more readily available than labeled data.
This normalizer developed in this project is intended to become the successor of the normalizing component of the Cascaded Analysis Broker (CAB), developed at the BBAW by Bryan Jurish (CAB webpage, CAB web service, Jurish (2012)).
CAB utilizes hand-craftet rules, edit distances, lexicon checks hidden markov language models, and an exception lexicon.
transnormer vs. CABOverview of main changes transnormer makes as compared to CAB.
The transnormer models are sequence-to-sequence models. This means, they take as input a string of unnormalized text and return a string of normalized text. This is different from CAB, which is a sequence tagger, where the original text is first tokenized and then, secondly, each token is assigned a single label.
As a consequence, transnormer can normalize word separation, e.g. normalize gehts to geht es and aller Hand to allerhand, while CAB cannot. CAB can only assign one label per token, i.e. would (at best) normalize gehts to the pseudo-token geht_es and normalize each token in aller Hand separately, thereby producing the suboptimal normalization aller Hand. In short, whitespace in output is no longer contingent on the tokenization of the input.
The code in this project is licensed under GNU General Public License v3.0.