From c7327132fbaf1e34c5330771422e1ca3882e7ee2 Mon Sep 17 00:00:00 2001
From: Maja Jablonska <majajjablonska@gmail.com>
Date: Wed, 15 Nov 2023 17:22:25 +1100
Subject: [PATCH] Add CLI parameters
---
combo/combo_model.py | 16 ++-
combo/config/__init__.py | 2 +-
combo/config/from_parameters.py | 59 +++++++++-
combo/main.py | 169 +++++++++++++++++++++++------
combo/polish_model_training.ipynb | 94 ++++++++--------
docs/training.md | 26 +++++
tests/config/test_configuration.py | 59 ++++++++++
7 files changed, 334 insertions(+), 91 deletions(-)
create mode 100644 docs/training.md
diff --git a/combo/combo_model.py b/combo/combo_model.py
index 4959b72..79c9f36 100644
--- a/combo/combo_model.py
+++ b/combo/combo_model.py
@@ -23,6 +23,7 @@ from combo.nn import utils
from combo.nn.utils import get_text_field_mask
from combo.predictors import Predictor
from combo.utils import metrics
+from utils import ConfigurationError
@Registry.register("semantic_multitask")
@@ -165,7 +166,10 @@ class ComboModel(Model, FromParameters):
if self.morphological_feat:
mapped_gold_labels = []
for _, cat_indices in self.morphological_feat.slices.items():
- mapped_gold_labels.append(feats[:, :, cat_indices].argmax(dim=-1))
+ try:
+ mapped_gold_labels.append(feats[:, :, cat_indices].argmax(dim=-1))
+ except TypeError:
+ raise ConfigurationError('Feats is None - if no feats are provided, the morphological_feat property should be set to None.')
feats = torch.stack(mapped_gold_labels, dim=-1)
@@ -184,11 +188,11 @@ class ComboModel(Model, FromParameters):
relations_loss, head_loss = parser_output["loss"]
enhanced_relations_loss, enhanced_head_loss = enhanced_parser_output["loss"]
losses = {
- "upostag_loss": upos_output["loss"],
- "xpostag_loss": xpos_output["loss"],
- "semrel_loss": semrel_output["loss"],
- "feats_loss": morpho_output["loss"],
- "lemma_loss": lemma_output["loss"],
+ "upostag_loss": upos_output.get("loss"),
+ "xpostag_loss": xpos_output.get("loss"),
+ "semrel_loss": semrel_output.get("loss"),
+ "feats_loss": morpho_output.get("loss"),
+ "lemma_loss": lemma_output.get("loss"),
"head_loss": head_loss,
"deprel_loss": relations_loss,
"enhanced_head_loss": enhanced_head_loss,
diff --git a/combo/config/__init__.py b/combo/config/__init__.py
index 948f357..e956d66 100644
--- a/combo/config/__init__.py
+++ b/combo/config/__init__.py
@@ -1,2 +1,2 @@
-from .from_parameters import FromParameters, resolve
+from .from_parameters import FromParameters, override_parameters, resolve
from .registry import Registry
diff --git a/combo/config/from_parameters.py b/combo/config/from_parameters.py
index e853042..da9dafb 100644
--- a/combo/config/from_parameters.py
+++ b/combo/config/from_parameters.py
@@ -19,7 +19,6 @@ def get_matching_arguments(args: Dict[str, Any], func: Callable) -> Dict[str, An
def _resolve(values: typing.Union[Dict[str, Any], str], pass_down_parameters: Dict[str, Any] = None) -> Any:
-
if isinstance(values, Params):
values = Params.as_dict()
@@ -148,7 +147,7 @@ class FromParameters:
if pn in pass_down_parameter_names:
continue
parameters_dict[pn] = serialize_single_value(param_value,
- pass_down_parameter_names+self.pass_down_parameter_names())
+ pass_down_parameter_names + self.pass_down_parameter_names())
return parameters_dict
def serialize(self, pass_down_parameter_names: List[str] = None) -> Dict[str, Any]:
@@ -166,3 +165,59 @@ def resolve(parameters: Dict[str, Any], pass_down_parameters: Dict[str, Any] = N
pass_down_parameters = pass_down_parameters or {}
clz, clz_init = Registry.resolve(parameters['type'])
return clz.from_parameters(parameters['parameters'], clz_init, pass_down_parameters)
+
+
+def flatten_dictionary(d, parent_key='', sep='/'):
+ """
+ Flatten a nested dictionary.
+
+ Parameters:
+ d (dict): The input dictionary.
+ parent_key (str): The parent key to use for recursion (default is an empty string).
+ sep (str): The separator to use when concatenating keys (default is '_').
+
+ Returns:
+ dict: A flattened dictionary.
+ """
+ items = []
+ for k, v in d.items():
+ new_key = f"{parent_key}{sep}{k}" if parent_key else k
+ if isinstance(v, dict):
+ items.extend(flatten_dictionary(v, new_key, sep=sep).items())
+ else:
+ items.append((new_key, v))
+ return dict(items)
+
+
+def unflatten_dictionary(flat_dict, sep='/'):
+ """
+ Unflatten a flattened dictionary.
+
+ Parameters:
+ flat_dict (dict): The flattened dictionary.
+ sep (str): The separator used in the flattened keys (default is '_').
+
+ Returns:
+ dict: The unflattened dictionary.
+ """
+ unflattened_dict = {}
+ for key, value in flat_dict.items():
+ keys = key.split(sep)
+ current_level = unflattened_dict
+
+ for k in keys[:-1]:
+ current_level = current_level.setdefault(k, {})
+
+ current_level[keys[-1]] = value
+
+ return unflattened_dict
+
+
+def override_parameters(parameters: Dict[str, Any], override_values: Dict[str, Any]) -> Dict[str, Any]:
+ overriden_parameters = flatten_dictionary(parameters)
+ override_values = flatten_dictionary(override_values)
+ for ko, vo in override_values.items():
+ if ko in overriden_parameters:
+ overriden_parameters[ko] = vo
+
+ return unflatten_dictionary(overriden_parameters)
diff --git a/combo/main.py b/combo/main.py
index ecc455c..44a6a30 100755
--- a/combo/main.py
+++ b/combo/main.py
@@ -18,20 +18,28 @@ from combo.default_model import default_ud_dataset_reader, default_data_loader
from combo.modules.archival import load_archive, archive
from combo.predict import COMBO
from combo.data import api
-from combo.data import DatasetReader
+from config import override_parameters
+from utils import ConfigurationError
logging.setLoggerClass(ComboLogger)
logger = logging.getLogger(__name__)
_FEATURES = ["token", "char", "upostag", "xpostag", "lemma", "feats"]
_TARGETS = ["deprel", "feats", "head", "lemma", "upostag", "xpostag", "semrel", "sent", "deps"]
+
+def handle_error(error: Exception):
+ msg = getattr(error, 'message', str(error))
+ logger.error(msg)
+ print(f'Error: {msg}')
+
+
FLAGS = flags.FLAGS
flags.DEFINE_enum(name="mode", default=None, enum_values=["train", "predict"],
help="Specify COMBO mode: train or predict")
# Common flags
-flags.DEFINE_integer(name="cuda_device", default=-1,
- help="Cuda device idx (default -1 cpu)")
+flags.DEFINE_integer(name="n_cuda_devices", default=-1,
+ help="Number of devices to train on (default -1 auto mode - train on as many as possible)")
flags.DEFINE_string(name="output_file", default="output.log",
help="Predictions result file.")
@@ -42,8 +50,8 @@ flags.DEFINE_string(name="validation_data_path", default="", help="Validation da
flags.DEFINE_alias(name="validation_data", original_name="validation_data_path")
flags.DEFINE_string(name="pretrained_tokens", default="",
help="Pretrained tokens embeddings path")
-flags.DEFINE_integer(name="embedding_dim", default=300,
- help="Embeddings dim")
+flags.DEFINE_integer(name="lemmatizer_embedding_dim", default=300,
+ help="Lemmatizer embeddings dim")
flags.DEFINE_integer(name="num_epochs", default=400,
help="Epochs num")
flags.DEFINE_integer(name="word_batch_size", default=2500,
@@ -72,10 +80,8 @@ flags.DEFINE_string(name="finetuning_validation_data_path", default="",
flags.DEFINE_string(name="test_data_path", default=None,
help="Test path file.")
flags.DEFINE_alias(name="test_data", original_name="test_data_path")
-
-# Experimental
flags.DEFINE_boolean(name="use_pure_config", default=False,
- help="Ignore ext flags (experimental).")
+ help="Ignore ext flags.")
# Prediction flags
flags.DEFINE_string(name="model_path", default=None,
@@ -99,37 +105,58 @@ def run(_):
if not FLAGS.finetuning:
prefix = 'Training'
logger.info('Setting up the model for training', prefix=prefix)
- checks.file_exists(FLAGS.config_path)
+ try:
+ checks.file_exists(FLAGS.config_path)
+ except ConfigurationError as e:
+ handle_error(e)
+ return
logger.info(f'Reading parameters from configuration path {FLAGS.config_path}', prefix=prefix)
with open(FLAGS.config_path, 'r') as f:
params = json.load(f)
- params = {**params, **_get_ext_vars()}
+ params = override_parameters(params, _get_ext_vars(True))
+
+ if 'feats' not in FLAGS.features:
+ del params['model']['parameters']['morphological_feat']
serialization_dir = tempfile.mkdtemp(prefix='combo', dir=FLAGS.serialization_dir)
params['vocabulary']['parameters']['directory'] = os.path.join('/'.join(FLAGS.config_path.split('/')[:-1]),
- params['vocabulary']['parameters']['directory'])
+ params['vocabulary']['parameters'][
+ 'directory'])
try:
vocabulary = resolve(params['vocabulary'])
- except KeyError:
- logger.error('No vocabulary in config.json!')
+ except Exception as e:
+ handle_error(e)
return
+ try:
+ model = resolve(override_parameters(params['model'], _get_ext_vars(False)),
+ pass_down_parameters={'vocabulary': vocabulary})
+ except Exception as e:
+ handle_error(e)
+ return
- model = resolve(params['model'], pass_down_parameters={'vocabulary': vocabulary})
dataset_reader = None
if 'data_loader' in params:
logger.info(f'Resolving the training data loader from parameters', prefix=prefix)
- train_data_loader = resolve(params['data_loader'])
+ try:
+ train_data_loader = resolve(params['data_loader'])
+ except Exception as e:
+ handle_error(e)
+ return
else:
checks.file_exists(FLAGS.training_data_path)
logger.info(f'Using a default UD data loader with training data path {FLAGS.training_data_path}',
prefix=prefix)
- train_data_loader = default_data_loader(default_ud_dataset_reader(),
- FLAGS.training_data_path)
+ try:
+ train_data_loader = default_data_loader(default_ud_dataset_reader(),
+ FLAGS.training_data_path)
+ except Exception as e:
+ handle_error(e)
+ return
logger.info('Indexing training data loader')
train_data_loader.index_with(model.vocab)
@@ -180,10 +207,18 @@ def run(_):
nlp = TrainableCombo(model, torch.optim.Adam,
optimizer_kwargs={'betas': [0.9, 0.9], 'lr': 0.002},
validation_metrics=['EM'])
+
+ n_cuda_devices = "auto" if FLAGS.n_cuda_devices == -1 else FLAGS.n_cuda_devices
+
trainer = pl.Trainer(max_epochs=FLAGS.num_epochs,
default_root_dir=serialization_dir,
- gradient_clip_val=5)
- trainer.fit(model=nlp, train_dataloaders=train_data_loader, val_dataloaders=validation_data_loader)
+ gradient_clip_val=5,
+ devices=n_cuda_devices)
+ try:
+ trainer.fit(model=nlp, train_dataloaders=train_data_loader, val_dataloaders=validation_data_loader)
+ except Exception as e:
+ handle_error(e)
+ return
logger.info(f'Archiving the model in {serialization_dir}', prefix=prefix)
archive(model, serialization_dir, train_data_loader, validation_data_loader, dataset_reader)
@@ -192,8 +227,9 @@ def run(_):
if FLAGS.test_data_path and FLAGS.output_file:
checks.file_exists(FLAGS.test_data_path)
if not dataset_reader:
- logger.info("No dataset reader in the configuration or archive file - using a default UD dataset reader",
- prefix=prefix)
+ logger.info(
+ "No dataset reader in the configuration or archive file - using a default UD dataset reader",
+ prefix=prefix)
dataset_reader = default_ud_dataset_reader()
logger.info("Predicting test examples", prefix=prefix)
test_trees = dataset_reader.read(FLAGS.test_data_path)
@@ -212,7 +248,7 @@ def run(_):
logger.info("No dataset reader in the configuration or archive file - using a default UD dataset reader",
prefix=prefix)
dataset_reader = default_ud_dataset_reader()
-
+
predictor = COMBO(model, dataset_reader)
if FLAGS.input_file == '-':
@@ -242,23 +278,86 @@ def run(_):
def _get_ext_vars(finetuning: bool = False) -> Dict:
if FLAGS.use_pure_config:
return {}
- return {
- "training_data_path": (
- ",".join(FLAGS.training_data_path if not finetuning else FLAGS.finetuning_training_data_path)),
- "validation_data_path": (
- ",".join(FLAGS.validation_data_path if not finetuning else FLAGS.finetuning_validation_data_path)),
+
+ to_override = {
+ "model": {
+ "parameters": {
+ "lemmatizer": {
+ "parameters": {
+ "embedding_dim": FLAGS.lemmatizer_embedding_dim
+ }
+ },
+ "text_field_embedder": {
+ "parameters": {
+ "token_embedders": {
+ "parameters": {
+ "token": {
+ "parameters": {
+ "model_name": FLAGS.pretrained_transformer_name
+ }
+ }
+ }
+ }
+ }
+ },
+ "serialization_dir": FLAGS.serialization_dir
+ }
+ },
+ "data_loader": {
+ "data_path": (",".join(FLAGS.training_data_path if not finetuning else FLAGS.finetuning_training_data_path)),
+ "parameters": {
+ "reader": {
+ "parameters": {
+ "features": FLAGS.features,
+ "targets": FLAGS.targets,
+ "token_indexers": {
+ "token": {
+ "parameters": {
+ "model_name": FLAGS.pretrained_transformer_name
+ }
+ }
+ }
+ }
+ }
+ }
+ },
+ "validation_data_loader": {
+ "data_path": (",".join(FLAGS.validation_data_path if not finetuning else FLAGS.finetuning_validation_data_path)),
+ "parameters": {
+ "reader": {
+ "parameters": {
+ "features": FLAGS.features,
+ "targets": FLAGS.targets,
+ "token_indexers": {
+ "token": {
+ "parameters": {
+ "model_name": FLAGS.pretrained_transformer_name
+ }
+ }
+ }
+ }
+ }
+ }
+ },
+ "dataset_reader": {
+ "parameters": {
+ "features": FLAGS.features,
+ "targets": FLAGS.targets,
+ "token_indexers": {
+ "token": {
+ "parameters": {
+ "model_name": FLAGS.pretrained_transformer_name
+ }
+ }
+ }
+ }
+ },
"pretrained_tokens": FLAGS.pretrained_tokens,
- "pretrained_transformer_name": FLAGS.pretrained_transformer_name,
- "features": " ".join(FLAGS.features),
- "targets": " ".join(FLAGS.targets),
- "type": "finetuning" if finetuning else "default",
- "embedding_dim": int(FLAGS.embedding_dim),
- "cuda_device": int(FLAGS.cuda_device),
- "num_epochs": int(FLAGS.num_epochs),
"word_batch_size": int(FLAGS.word_batch_size),
- "use_tensorboard": int(FLAGS.tensorboard),
}
+ return to_override
+
def main():
"""Parse flags."""
diff --git a/combo/polish_model_training.ipynb b/combo/polish_model_training.ipynb
index bd2796f..005fe4b 100644
--- a/combo/polish_model_training.ipynb
+++ b/combo/polish_model_training.ipynb
@@ -6,16 +6,16 @@
"outputs": [],
"source": [
"# The path where the training and validation datasets are stored\n",
- "TRAINING_DATA_PATH: str = '/Users/majajablonska/Documents/PDB/PDBUD_train.conllu'\n",
- "VALIDATION_DATA_PATH: str = '/Users/majajablonska/Documents/PDB/PDBUD_val.conllu'\n",
+ "TRAINING_DATA_PATH: str = '/Users/majajablonska/Documents/PDBUD/train.conllu'\n",
+ "VALIDATION_DATA_PATH: str = '/Users/majajablonska/Documents/PDBUD/val.conllu'\n",
"# The path where the model can be saved to\n",
"SERIALIZATION_DIR: str = \"/Users/majajablonska/Documents/Workspace/combotest\""
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:18:36.376046Z",
- "start_time": "2023-11-13T08:18:36.189836Z"
+ "end_time": "2023-11-13T12:15:21.197003Z",
+ "start_time": "2023-11-13T12:15:19.886422Z"
}
},
"id": "b28c7d8bacb08d02"
@@ -51,8 +51,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:18:41.300316Z",
- "start_time": "2023-11-13T08:18:36.197537Z"
+ "end_time": "2023-11-13T12:15:28.665585Z",
+ "start_time": "2023-11-13T12:15:19.907198Z"
}
},
"id": "initial_id"
@@ -77,7 +77,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "e45b57a7047043a48ccfeacfb49312b5"
+ "model_id": "2179b1be2f484a33948a76d087002182"
}
},
"metadata": {},
@@ -89,7 +89,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "a886ae4451474459b088659ebac076ae"
+ "model_id": "86762d681ee0467e8501de2b34061aad"
}
},
"metadata": {},
@@ -101,7 +101,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "20ff98564c7c43a9971c25f82ceda997"
+ "model_id": "b9e631cb77594ea5aae60e6d15809885"
}
},
"metadata": {},
@@ -169,8 +169,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:19:01.785477Z",
- "start_time": "2023-11-13T08:18:41.119674Z"
+ "end_time": "2023-11-13T12:15:51.717065Z",
+ "start_time": "2023-11-13T12:15:28.442131Z"
}
},
"id": "d74957f422f0b05b"
@@ -192,8 +192,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:19:03.816723Z",
- "start_time": "2023-11-13T08:19:01.774666Z"
+ "end_time": "2023-11-13T12:15:52.574303Z",
+ "start_time": "2023-11-13T12:15:51.724469Z"
}
},
"id": "fa724d362fd6bd23"
@@ -211,7 +211,7 @@
},
{
"data": {
- "text/plain": "<generator object SimpleDataLoader.iter_instances at 0x7fb2d3cdfc80>"
+ "text/plain": "<generator object SimpleDataLoader.iter_instances at 0x7fb512dc4f20>"
},
"execution_count": 5,
"metadata": {},
@@ -239,8 +239,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:19:03.877868Z",
- "start_time": "2023-11-13T08:19:03.826289Z"
+ "end_time": "2023-11-13T12:15:52.641199Z",
+ "start_time": "2023-11-13T12:15:52.583194Z"
}
},
"id": "f8a10f9892005fca"
@@ -263,8 +263,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:19:03.887795Z",
- "start_time": "2023-11-13T08:19:03.870640Z"
+ "end_time": "2023-11-13T12:15:52.659289Z",
+ "start_time": "2023-11-13T12:15:52.625700Z"
}
},
"id": "14413692656b68ac"
@@ -277,7 +277,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "Some weights of the model checkpoint at allegro/herbert-base-cased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.sso.sso_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.sso.sso_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight']\n",
+ "Some weights of the model checkpoint at allegro/herbert-base-cased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.sso.sso_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.sso.sso_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight']\n",
"- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
@@ -411,8 +411,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:19:10.006549Z",
- "start_time": "2023-11-13T08:19:03.885912Z"
+ "end_time": "2023-11-13T12:15:56.509687Z",
+ "start_time": "2023-11-13T12:15:52.658879Z"
}
},
"id": "437d12054baaffa1"
@@ -430,8 +430,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:19:47.953809Z",
- "start_time": "2023-11-13T08:19:09.989582Z"
+ "end_time": "2023-11-13T12:16:30.663344Z",
+ "start_time": "2023-11-13T12:15:56.529656Z"
}
},
"id": "e131e0ec75dc6927"
@@ -446,8 +446,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:19:53.878201Z",
- "start_time": "2023-11-13T08:19:47.940147Z"
+ "end_time": "2023-11-13T12:16:45.453326Z",
+ "start_time": "2023-11-13T12:16:30.488388Z"
}
},
"id": "195c71fcf8170ff"
@@ -481,8 +481,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:19:54.150333Z",
- "start_time": "2023-11-13T08:19:53.874397Z"
+ "end_time": "2023-11-13T12:16:45.785538Z",
+ "start_time": "2023-11-13T12:16:45.365250Z"
}
},
"id": "cefc5173154d1605"
@@ -503,7 +503,7 @@
"12.1 M Trainable params\n",
"124 M Non-trainable params\n",
"136 M Total params\n",
- "546.115 Total estimated model params size (MB)\n"
+ "546.106 Total estimated model params size (MB)\n"
]
},
{
@@ -512,7 +512,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "9f6e199b0fd546f5833fdda238964165"
+ "model_id": "f2dd3228246843428b8fcb8ae932c1f1"
}
},
"metadata": {},
@@ -534,7 +534,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "594c065e5d2441f48ba2b87c7a3f528f"
+ "model_id": "0bcdd388df664784ba19667c6a0593a1"
}
},
"metadata": {},
@@ -546,7 +546,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "bd8bf330a90e4eddb52a3c87af6d2869"
+ "model_id": "a8203342bf454c22b292548d64f085a9"
}
},
"metadata": {},
@@ -566,8 +566,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:20:39.048426Z",
- "start_time": "2023-11-13T08:19:54.147748Z"
+ "end_time": "2023-11-13T12:17:47.659618Z",
+ "start_time": "2023-11-13T12:16:45.706948Z"
}
},
"id": "e5af131bae4b1a33"
@@ -582,8 +582,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:20:39.152284Z",
- "start_time": "2023-11-13T08:20:39.042845Z"
+ "end_time": "2023-11-13T12:17:47.975345Z",
+ "start_time": "2023-11-13T12:17:47.644327Z"
}
},
"id": "3e23413c86063183"
@@ -598,8 +598,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:20:39.228735Z",
- "start_time": "2023-11-13T08:20:39.052747Z"
+ "end_time": "2023-11-13T12:17:47.989681Z",
+ "start_time": "2023-11-13T12:17:47.665490Z"
}
},
"id": "d555d7f0223a624b"
@@ -613,8 +613,8 @@
"output_type": "stream",
"text": [
"TOKEN LEMMA UPOS HEAD DEPREL \n",
- "Cześć, ????? NOUN 0 root \n",
- "jestem ????? NOUN 1 punct \n",
+ "Cześć, ?????? NOUN 0 root \n",
+ "jestem ?????a NOUN 1 punct \n",
"psem. ????? NOUN 1 punct \n"
]
}
@@ -627,8 +627,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:20:39.237630Z",
- "start_time": "2023-11-13T08:20:39.227051Z"
+ "end_time": "2023-11-13T12:17:48.005229Z",
+ "start_time": "2023-11-13T12:17:47.923055Z"
}
},
"id": "a68cd3861e1ceb67"
@@ -643,8 +643,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:20:39.248539Z",
- "start_time": "2023-11-13T08:20:39.233003Z"
+ "end_time": "2023-11-13T12:17:48.008545Z",
+ "start_time": "2023-11-13T12:17:47.928808Z"
}
},
"id": "d0f43f4493218b5"
@@ -668,8 +668,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:24:03.513738Z",
- "start_time": "2023-11-13T08:20:39.250115Z"
+ "end_time": "2023-11-13T12:19:17.944519Z",
+ "start_time": "2023-11-13T12:17:47.965095Z"
}
},
"id": "ec92aa5bb5bb3605"
@@ -684,8 +684,8 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-11-13T08:24:03.668958Z",
- "start_time": "2023-11-13T08:24:02.256799Z"
+ "end_time": "2023-11-13T12:19:17.954324Z",
+ "start_time": "2023-11-13T12:19:17.920401Z"
}
},
"id": "5ad8a827586f65e3"
diff --git a/docs/training.md b/docs/training.md
new file mode 100644
index 0000000..7ac726c
--- /dev/null
+++ b/docs/training.md
@@ -0,0 +1,26 @@
+# Training
+
+Basic command:
+
+```bash
+combo --mode train \
+ --training_data_path your_training_path \
+ --validation_data_path your_validation_path
+```
+
+Options:
+
+```bash
+combo --helpfull
+```
+
+## Examples
+
+For clarity, the training and validation data paths are omitted.
+
+Train on multiple accelerators (default: train on all available ones)
+```bash
+combo --mode train
+ --n_cuda_devices 8
+```
+
diff --git a/tests/config/test_configuration.py b/tests/config/test_configuration.py
index 48083cf..123bfbc 100644
--- a/tests/config/test_configuration.py
+++ b/tests/config/test_configuration.py
@@ -2,6 +2,7 @@ import unittest
import os
from combo.config import Registry
+from combo.config.from_parameters import override_parameters
from combo.data import WhitespaceTokenizer, UniversalDependenciesDatasetReader, Vocabulary
from combo.data.token_indexers.token_characters_indexer import TokenCharactersIndexer
@@ -79,3 +80,61 @@ class ConfigurationTest(unittest.TestCase):
self.assertEqual(type(reconstructed_vocab), Vocabulary)
self.assertEqual(reconstructed_vocab.constructed_from, 'from_files')
self.assertSetEqual(reconstructed_vocab.get_namespaces(), {'animals'})
+
+
+ def test_override_parameters(self):
+ parameters = {
+ 'type': 'base_vocabulary',
+ 'parameters': {
+ 'counter': {'counter': {'test': 0}},
+ 'max_vocab_size': 10
+ }
+ }
+
+ to_override = {'parameters': {'max_vocab_size': 15}}
+
+ self.assertDictEqual({
+ 'type': 'base_vocabulary',
+ 'parameters': {
+ 'counter': {'counter': {'test': 0}},
+ 'max_vocab_size': 15
+ }
+ }, override_parameters(parameters, to_override))
+
+ def test_override_nested_parameters(self):
+ parameters = {
+ 'type': 'base_vocabulary',
+ 'parameters': {
+ 'counter': {'counter': {'test': 0}, 'another_property': 0},
+ 'another_counter': {'counter': {'test': 0}, 'another_property': 0}
+ }
+ }
+
+ to_override = {'parameters': {'another_counter': {'counter': {'test': 1}}}}
+
+ self.assertDictEqual({
+ 'type': 'base_vocabulary',
+ 'parameters': {
+ 'counter': {'counter': {'test': 0}, 'another_property': 0},
+ 'another_counter': {'counter': {'test': 1}, 'another_property': 0}
+ }
+ }, override_parameters(parameters, to_override))
+
+ def test_override_parameters_no_change(self):
+ parameters = {
+ 'type': 'base_vocabulary',
+ 'parameters': {
+ 'counter': {'counter': {'test': 0}},
+ 'max_vocab_size': 10
+ }
+ }
+
+ to_override = {}
+
+ self.assertDictEqual({
+ 'type': 'base_vocabulary',
+ 'parameters': {
+ 'counter': {'counter': {'test': 0}},
+ 'max_vocab_size': 10
+ }
+ }, override_parameters(parameters, to_override))
--
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