diff --git a/ud_script/README.md b/ud_script/README.md
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+++ b/ud_script/README.md
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+## Description
+
+The script [train_ud_version.py](/combo/ud_script/train_ud_version.py) allows for training multiple combo models on specific UD treebank version. To run the script, three parameters are required:
+- `output_directory` - path to the location where training results will be saved
+- `treebank_id`
+- `treebank_version`
+
+To find the `treebank_id` and `treebank_version`, visit https://universaldependencies.org/#download. The `treebank_version` is indicated at the beginning of the UD version, while the `treebank_id` is the value at the end of the link used to download it. See the attached image where both values for UD2.11 are highlighted in yellow.
+
+
+
+The script will automatically download and extract the UD data into the folder `output_directory`/ud_treebanks-`treebank_version`. Then, it creates a subfolder `output_directory/results` containing:
+- `serialization_directories` - folder with training results
+- `completed_training.txt` - a text file with the names of UD treebanks on which training was successfully completed
+- `skipped_training.csv` - a csv file with two columns, the first containing names of UD treebanks, the second listing reasons why training failed. Possible reasons include:
+ - Dev or test or train file missing - it is expected that there is a .conllu file in the UD directory that contains train, dev, and test in its name. Otherwise, this error is thrown.
+ - Training file less than 1000 bytes - if the training file has less than 1000 bytes, training is skipped.
+ - Training file corrupted - number of columns is less than 10.
+ - Specify transformer model for language code: <lang_code> - No BERT model was assigned to the specified language. To address this, modify the `LANG2TRANSFORMER` variable in the file [constants](/combo/ud_script/constants.py).
+ - Command ... returned non-zero exit status 1 - An error was thrown during the training process. You need to examine logs from this particular training to understand what happened.
+
+If script was interrupted at some point, you can rerun it with the same command. Based on values in completed_training reruned script will ommit training on UD treebanks that already have model.
+
+Some of the models need adjusted value of word_batch_size, default value will be used unless you specify <UD trebank> <word_batch_size> pair in `UD_2_BATCH_SIZE` constant in [constants](/combo/ud_script/constants.py).
+
+## Example usage
+Terminal command:
+```
+python train_ud_version.py --treebank_id 1-5287 --treebank_version 2.13 --output_directory C:\Users\abc\Desktop
+```
diff --git a/ud_script/constants.py b/ud_script/constants.py
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index 0000000000000000000000000000000000000000..c92d352e68e120041be011b58586b9a19d667c90
--- /dev/null
+++ b/ud_script/constants.py
@@ -0,0 +1,300 @@
+LINKS = [
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+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.sq.300.vec.gz",
+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.als.300.vec.gz",
+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.am.300.vec.gz",
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+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.be.300.vec.gz",
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+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.my.300.vec.gz",
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+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.ia.300.vec.gz",
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+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.mg.300.vec.gz",
+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.ms.300.vec.gz",
+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.ml.300.vec.gz",
+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.mt.300.vec.gz",
+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.gv.300.vec.gz",
+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.mr.300.vec.gz",
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+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.mhr.300.vec.gz",
+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.min.300.vec.gz",
+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.xmf.300.vec.gz",
+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.mwl.300.vec.gz",
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+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.pnb.300.vec.gz",
+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.yi.300.vec.gz",
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+ "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.zea.300.vec.gz",
+]
+
+CODE_2_LANG = {
+ "af": "Afrikaans",
+ "aii": "Assyrian",
+ "ajp": "South_Levantine_Arabic",
+ "akk": "Akkadian",
+ "am": "Amharic",
+ "apu": "Apurina",
+ "aqz": "Akuntsu",
+ "ar": "Arabic",
+ "be": "Belarusian",
+ "bg": "Bulgarian",
+ "bho": "Bhojpuri",
+ "bm": "Bambara",
+ "br": "Breton",
+ "bxr": "Buryat",
+ "ca": "Catalan",
+ "ckt": "Chukchi",
+ "cop": "Coptic",
+ "cs": "Czech",
+ "cu": "Old_Church_Slavonic",
+ "cy": "Welsh",
+ "da": "Danish",
+ "de": "German",
+ "el": "Greek",
+ "en": "English",
+ "es": "Spanish",
+ "et": "Estonian",
+ "eu": "Basque",
+ "fa": "Persian",
+ "fi": "Finnish",
+ "fo": "Faroese",
+ "fr": "French",
+ "fro": "Old_French",
+ "ga": "Irish",
+ "gd": "Scottish_Gaelic",
+ "gl": "Galician",
+ "got": "Gothic",
+ "grc": "Ancient_Greek",
+ "gsw": "Swiss_German",
+ "gun": "Mbya_Guarani",
+ "gv": "Manx",
+ "he": "Hebrew",
+ "hi": "Hindi",
+ "hr": "Croatian",
+ "hsb": "Upper_Sorbian",
+ "hu": "Hungarian",
+ "hy": "Armenian",
+ "id": "Indonesian",
+ "is": "Icelandic",
+ "it": "Italian",
+ "ja": "Japanese",
+ "kfm": "Khunsari",
+ "kk": "Kazakh",
+ "kmr": "Kurmanji",
+ "ko": "Korean",
+ "koi": "Komi_Permyak",
+ "kpv": "Komi_Zyrian",
+ "krl": "Karelian",
+ "la": "Latin",
+ "lt": "Lithuanian",
+ "lv": "Latvian",
+ "lzh": "Classical_Chinese",
+ "mdf": "Moksha",
+ "mr": "Marathi",
+ "mt": "Maltese",
+ "myu": "Munduruku",
+ "myv": "Erzya",
+ "nl": "Dutch",
+ "no": "Norwegian",
+ "nyq": "Nayini",
+ "olo": "Livvi",
+ "orv": "Old_Russian",
+ "otk": "Old_Turkish",
+ "pcm": "Naija",
+ "pl": "Polish",
+ "pt": "Portuguese",
+ "qhe": "Hindi_English",
+ "qtd": "Turkish_German",
+ "ro": "Romanian",
+ "ru": "Russian",
+ "sa": "Sanskrit",
+ "sk": "Slovak",
+ "sl": "Slovenian",
+ "sme": "North_Sami",
+ "sms": "Skolt_Sami",
+ "soj": "Soi",
+ "sq": "Albanian",
+ "sr": "Serbian",
+ "sv": "Swedish",
+ "swl": "Swedish_Sign_Language",
+ "ta": "Tamil",
+ "te": "Telugu",
+ "th": "Thai",
+ "tl": "Tagalog",
+ "tpn": "Tupinamba",
+ "tr": "Turkish",
+ "ug": "Uyghur",
+ "uk": "Ukrainian",
+ "ur": "Urdu",
+ "vi": "Vietnamese",
+ "wbp": "Warlpiri",
+ "wo": "Wolof",
+ "yo": "Yoruba",
+ "yue": "Cantonese",
+ "zh": "Chinese",
+}
+
+UD_2_BATCH_SIZE = {
+ "UD_German-HDT" : 1000,
+ "UD_Marathi-UFAL" : 1000,
+ "UD_Telugu-MTG" : 500
+}
+
+LANG2TRANSFORMER = {
+ "en": "bert-base-cased",
+ "pl": "allegro/herbert-large-cased",
+ "zh": "bert-base-chinese",
+ "fi": "TurkuNLP/bert-base-finnish-cased-v1",
+ "ko": "kykim/bert-kor-base",
+ "de": "dbmdz/bert-base-german-cased",
+ "ar": "aubmindlab/bert-base-arabertv2",
+ "eu": "ixa-ehu/berteus-base-cased",
+ "tr": "dbmdz/bert-base-turkish-cased",
+ "bg": "xlm-roberta-large",
+ "nl": "xlm-roberta-large",
+ "fr": "camembert-base",
+ "it": "xlm-roberta-large",
+ "ru": "xlm-roberta-large",
+ "sv": "xlm-roberta-large",
+ "uk": "xlm-roberta-large",
+ "ta": "xlm-roberta-large",
+ "sk": "xlm-roberta-large",
+ "lt": "xlm-roberta-large",
+ "lv": "xlm-roberta-large",
+ "cs": "xlm-roberta-large",
+ "et": "xlm-roberta-large",
+}
+
+LANG_UD2FASTTEXT = {}
\ No newline at end of file
diff --git a/ud_script/imgs/instruction_ud_version.png b/ud_script/imgs/instruction_ud_version.png
new file mode 100644
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diff --git a/ud_script/train_ud_version.py b/ud_script/train_ud_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..09009f6eaa414ec58fae9fe0f26281a0e0de9b03
--- /dev/null
+++ b/ud_script/train_ud_version.py
@@ -0,0 +1,89 @@
+from string import Template
+from pathlib import Path
+from utils import (
+ download_file,
+ extract_tgz,
+ build_list_of_uds_to_train,
+ download_fasttext,
+ write_to_csv,
+ train_one_model)
+import pandas as pd
+import argparse
+
+# Argument parsing
+parser = argparse.ArgumentParser()
+parser.add_argument("--treebank_id", type=str, default="", help="ID of the treebank")
+parser.add_argument("--treebank_version", type=str, default="", help="Version of the treebank")
+parser.add_argument("--output_dir", type=str, default="", help="Base directory for output")
+args = parser.parse_args()
+
+url = Template("https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/$treebank_id/ud-treebanks-v$version.tgz")
+
+def main():
+ treebank_id = args.treebank_id
+ treebank_version = args.treebank_version
+ output_dir = args.output_dir
+ ud_output_dir = str(Path(output_dir) / f'ud-treebanks-v{treebank_version}.tgz')
+
+ # make output directory if it doesn't exist
+ Path(output_dir).mkdir(parents=True, exist_ok=True)
+ (Path(output_dir) / "results").mkdir(parents=True, exist_ok=True)
+
+ ud_url = url.substitute(treebank_id=treebank_id,
+ version=treebank_version)
+ # Download UD treebank
+ if not Path(ud_output_dir).exists():
+ print("Downloading UD treebank")
+ download_file(url=ud_url,
+ local_filename=ud_output_dir)
+ else:
+ print("UD treebank already downloaded")
+
+ # Extract UD treebank
+ if not (Path(output_dir) / f'ud-treebanks-v{treebank_version}').exists():
+ print("Extracting UD treebank")
+ extract_tgz(tgz_path=ud_output_dir,
+ extract_path=output_dir)
+ else :
+ print("UD treebank already extracted")
+
+ print("Checking UD data")
+ ud_folder_directory = output_dir + f'/ud-treebanks-v{treebank_version}'
+ training_queue = build_list_of_uds_to_train(UD_dir=ud_folder_directory)
+
+ # Read UDs with completed training
+ completed_training_path = Path(output_dir) / "results" / "completed_training.txt"
+ completed_training_path.touch(exist_ok=True)
+ with open(completed_training_path, 'r', encoding='utf-8') as file:
+ completed_training = [line.strip() for line in file]
+
+ # Train on transformer models
+ for ud_treebank in training_queue.keys():
+ if ud_treebank not in completed_training:
+ print(f"Training {ud_treebank}")
+ try:
+ # Start training
+ train_one_model(treebank = ud_treebank,
+ treebanks_dir = ud_folder_directory,
+ output_dir = output_dir,
+ targets = training_queue[ud_treebank],
+ use_fasttext = False)
+
+ # Save info that training was completed
+ with open(completed_training_path, 'a') as file: file.write(ud_treebank+"\n")
+
+ # Delete this info from skipped_training.csv if there is any
+ df = pd.read_csv(Path(output_dir) / "results" / "skipped_training.csv")
+
+ filtered_df = df[~df.iloc[:, 0].str.contains(ud_treebank)]
+ filtered_df.to_csv(Path(output_dir) / "results" / "skipped_training.csv",
+ index=False)
+ except Exception as e:
+ error = str(e)
+ write_to_csv(treebank=ud_treebank,
+ reason=error,
+ csv_file_path=Path(output_dir) / "results" / "skipped_training.csv")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/ud_script/utils.py b/ud_script/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9d209ee8da4ea5cb1aff54a2f92c646bc8378b9
--- /dev/null
+++ b/ud_script/utils.py
@@ -0,0 +1,309 @@
+import requests
+from tqdm import tqdm
+import tarfile
+from pathlib import Path
+import csv
+import shutil
+from constants import (
+ LINKS,
+ CODE_2_LANG,
+ UD_2_BATCH_SIZE,
+ LANG2TRANSFORMER,
+ LANG_UD2FASTTEXT)
+import subprocess
+import os
+
+
+def download_file(url : str,
+ local_filename : str,
+ show_progress : bool=True):
+ """
+ Downloads a file from the given URL and saves it locally.
+
+ Args:
+ url (str): The URL of the file to download.
+ local_filename (str): The path and filename to save the downloaded file locally.
+ show_progress (bool, optional): Whether to show a progress bar during the download.
+ Defaults to True.
+ """
+ # Send a GET request to the URL with stream=True to stream the download
+ with requests.get(url, stream=True) as response:
+ # Raise an error for bad responses
+ response.raise_for_status()
+
+ # Get the total size of the file from the Content-Length header
+ total_size_in_bytes = int(response.headers.get('content-length', 0))
+
+ # Open a local file with write-binary mode
+ with open(local_filename, 'wb') as file:
+ if show_progress:
+ # Use tqdm for a progress bar if show_progress is True
+ with tqdm(
+ desc=local_filename,
+ total=total_size_in_bytes,
+ unit='iB',
+ unit_scale=True,
+ unit_divisor=1024,
+ ) as progress_bar:
+ for chunk in response.iter_content(chunk_size=1024):
+ size = file.write(chunk)
+ progress_bar.update(size)
+ else:
+ # If not showing progress, just write the file without a progress bar
+ for chunk in response.iter_content(chunk_size=1024):
+ file.write(chunk)
+
+ print(f"File downloaded and saved as {local_filename}")
+
+
+def download_fasttext(output_dir: str):
+ """
+ Downloads FastText vectors for different languages.
+
+ Args:
+ output_dir (str): The directory where the FastText vectors will be downloaded.
+ """
+ output_dir = Path(output_dir)
+ for link in LINKS:
+ lang_code = link.split(".")[-4]
+
+ if lang_code not in CODE_2_LANG:
+ print(f"Unknown code {lang_code}.")
+ continue
+
+ output_file = output_dir / CODE_2_LANG[lang_code]
+ output_file.mkdir(exist_ok=True, parents=True)
+ if (output_file / 'vectors.vec.gz').exists():
+ continue
+
+ download_file(url=link,
+ local_filename=str(output_file / 'vectors.vec.gz'),
+ show_progress=False)
+
+
+def extract_tgz(tgz_path : str,
+ extract_path : str ="."):
+ """
+ Extracts the contents of a .tgz file to the specified extract path.
+
+ Args:
+ tgz_path (str): The path to the .tgz file.
+ extract_path (str, optional): The path where the contents will be extracted. Defaults to the current directory.
+ """
+ # Open the .tgz file
+ with tarfile.open(tgz_path, "r:gz") as tar:
+ # Get the total number of items within the archive for the progress bar
+ total = len(tar.getmembers())
+
+ # Setup tqdm progress bar
+ with tqdm(total=total, unit='files', desc="Extracting") as progress_bar:
+ # Extract each member individually
+ for member in tar.getmembers():
+ tar.extract(member, path=extract_path)
+ # Update the progress bar per file extracted
+ progress_bar.update(1)
+ print(f"Files have been extracted to: {extract_path}")
+
+
+def write_to_csv(treebank : str,
+ reason : str,
+ csv_file_path : Path):
+ # Open the CSV file and append the directory name and reason
+ with open(csv_file_path, mode='a', newline='') as file:
+ writer = csv.writer(file)
+ writer.writerow([treebank, reason])
+
+
+def build_list_of_uds_to_train(UD_dir: str):
+ """
+ Builds a dictionary of UD treebanks to train with their respective targets training commands. It ommits UDs that
+ have training file of size less than 1000 bytes, corrupted files (less than 10 columns) or missing files.
+ All of reasons are saved in skipped_training.csv
+
+ Args:
+ UD_dir (str): The directory path containing the UD treebanks.
+
+ Returns:
+ dict: A dictionary mapping UD treebank names to their training commands.
+ """
+ UD_dir = Path(UD_dir)
+ csv_file_path = UD_dir.parent / "results" / "skipped_training.csv"
+ UD_to_targets = {}
+
+ # Create file with skipped UD treebanks
+ with open(csv_file_path, mode='w', newline='') as file:
+ writer = csv.writer(file)
+ writer.writerow(["Directory Name", "Reason"])
+
+ # Iterate through each item in the directory
+ for item in tqdm(UD_dir.iterdir()):
+ # Check if the item is a directory
+ if item.is_dir():
+
+ skipped = False
+ # Get files in directory (ignoring directories)
+ onlyfiles = [f for f in item.iterdir() if f.is_file()]
+
+ # Check for the presence of specific files
+ has_dev = any(["dev" in f.name and f.suffix == ".conllu" for f in onlyfiles])
+ has_test = any(["test" in f.name and f.suffix == ".conllu" for f in onlyfiles])
+ has_train = any(["train" in f.name and f.suffix == ".conllu" for f in onlyfiles])
+
+ if not (has_train and has_dev and has_test):
+ write_to_csv(treebank=item.name,
+ reason="Dev or test or train file missing",
+ csv_file_path=csv_file_path)
+ continue
+
+ # If training file are too small - skip training
+ for f in item.iterdir():
+ tmp_path = Path()
+ if f.suffix == ".conllu" and ("dev" in f.name or "test" in f.name or "train" in f.name):
+ tmp_path = f
+ if "train" in f.name:
+ train_file = f
+ if tmp_path and tmp_path.is_file() and tmp_path.stat().st_size < 1000:
+ write_to_csv(treebank=item.name,
+ reason="Training file less than 1000 bytes",
+ csv_file_path=csv_file_path)
+ continue
+
+ lemmas, upos, xpos, feats, head, deprel = [], [], [], [], [], []
+
+ # Checking what data is inside training file
+ with train_file.open('r', encoding='utf-8') as rf:
+ sentences_processed = 0
+ for line in rf:
+ # omit lines that are comments
+ if line.startswith("#"):
+ continue
+
+ # always read only 10 sentences
+ if line == "\n":
+ if sentences_processed == 10:
+ break
+ else:
+ sentences_processed += 1
+ continue
+
+ words = line.split("\t")
+ if len(words) == 10:
+ lemmas.append(words[2])
+ upos.append(words[3])
+ xpos.append(words[4])
+ feats.append(words[5])
+ head.append(words[6])
+ deprel.append(words[7])
+ else:
+ write_to_csv(treebank=item.name,
+ reason="Training file corrupted - number of columns is less than 10.",
+ csv_file_path=csv_file_path)
+ skipped = True
+ break
+
+
+ if skipped:
+ continue
+
+ # Create targets command by adding
+ command = " --targets head,deprel"
+ if set(lemmas) != "-":
+ command += ",lemma"
+ if set(upos) != "-":
+ command += ",upostag"
+ if set(xpos) != "-":
+ command += ",xpostag"
+ if set(feats) != "-":
+ command += ",feats"
+
+ # Save training command
+ UD_to_targets[item.name] = command
+
+ return UD_to_targets
+
+
+def execute_command(command: str, output_file=None):
+ """
+ Executes a command in the system shell.
+
+ Args:
+ command (str): The command to be executed.
+ output_file (str, optional): The file to redirect the command output to. Defaults to None.
+ """
+ env = os.environ.copy()
+ command = [c for c in command.split() if c.strip()]
+ if output_file:
+ with open(output_file, "w") as f:
+ subprocess.run(command, check=True, stdout=f, env=env)
+ else:
+ subprocess.run(command, check=True, env=env)
+
+
+def train_one_model(treebank : str,
+ treebanks_dir : str,
+ output_dir : str,
+ targets : str,
+ use_fasttext : bool = True):
+ # Create LANG_2_CODE dict
+ LANG_2_CODE = {value: key for key, value in CODE_2_LANG.items()}
+
+ treebank_dir = Path(treebanks_dir) / treebank
+ treebank_parts = treebank[3:].split("-")
+ language = treebank_parts[0]
+ if language in LANG_UD2FASTTEXT:
+ language = LANG_UD2FASTTEXT[language]
+
+ # Find training and dev files
+ files = list(treebank_dir.iterdir())
+
+ training_file = [f for f in files if "train" in f.name and ".conllu" in f.name]
+ assert len(training_file) == 1, f"Multiple training file."
+ training_file_path = training_file[0]
+
+ valid_file = [f for f in files if "dev" in f.name and ".conllu" in f.name]
+ assert len(valid_file) == 1, f"Multiple validation file."
+ valid_file_path = valid_file[0]
+
+ # Find fasttexts
+ if use_fasttext:
+ embeddings_dir = Path(output_dir) / "fasttext" / language
+ if not embeddings_dir.exists():
+ raise FileNotFoundError(f"Fasttext for {language} does not exist. If you want to use fasttext for different language, specify mapping in LANG_UD2FASTTEXT.")
+ embeddings_file = [f for f in embeddings_dir.iterdir() if "vectors" in f.name and ".vec" in f.name]
+ assert len(embeddings_file) == 1, f"Couldn't find embeddings file."
+ embeddings_file = embeddings_file[0]
+ else:
+ # Throw error if transformer model is not specified
+ if not LANG_2_CODE[language] in LANG2TRANSFORMER.keys():
+ raise ValueError(f"Specify transformer model for language code: {LANG_2_CODE[language]}.")
+
+ # Create serialization directory
+ serialization_dir = Path(output_dir) / "results" / "serialization_directories" / treebank
+ if serialization_dir.exists() and serialization_dir.is_dir():
+ # Remove the directory and all its contents
+ shutil.rmtree(serialization_dir)
+ serialization_dir.mkdir(exist_ok=True, parents=True)
+
+ # Create command to run training
+ training_data_path = f"--training_data_path {training_file_path} "
+ validation_data_path = f"--validation_data_path {valid_file_path} "
+ if use_fasttext:
+ embeddings = f"--pretrained_tokens {embeddings_dir} "
+ else:
+ transformer_name = LANG2TRANSFORMER[LANG_2_CODE[language]]
+ embeddings = f"--pretrained_transformer_name {transformer_name} "
+ serialization_dir = f"--serialization_dir {serialization_dir} "
+ tokenizer_language = f"--tokenizer_language={language} "
+
+ command = "combo --mode train " + training_data_path + validation_data_path + embeddings + serialization_dir + tokenizer_language
+
+ command += targets
+
+ if treebank in UD_2_BATCH_SIZE:
+ command += f" --word_batch_size {UD_2_BATCH_SIZE[treebank]}"
+
+ # Start training
+ execute_command(command)
+
+
+