diff --git a/combo/data/dataset.py b/combo/data/dataset.py
index dc7b9020851d10cf87ebe16cfc015069e651de46..3993a55c84b9d9486826f756144ef846bedde481 100644
--- a/combo/data/dataset.py
+++ b/combo/data/dataset.py
@@ -98,7 +98,7 @@ class UniversalDependenciesDatasetReader(DatasetReader):
for conllu_file in file_path:
file = pathlib.Path(conllu_file)
- assert conllu_file and file.exists(), f"File with path '{conllu_file}' does not exists!"
+ assert conllu_file and file.exists(), f"File with path '{conllu_file}' does not exist!"
with file.open("r", encoding="utf-8") as f:
for annotation in conllu.parse_incr(f, fields=self.fields, field_parsers=self.field_parsers):
yield self.text_to_instance(annotation)
diff --git a/combo/data/dataset_readers/universal_dependencies_dataset_reader.py b/combo/data/dataset_readers/universal_dependencies_dataset_reader.py
index 14866e7e8a0654f08b8b4328cc0681034fbad8ee..8117c45bb509c991699a9b11e5f188e29e2e08fc 100644
--- a/combo/data/dataset_readers/universal_dependencies_dataset_reader.py
+++ b/combo/data/dataset_readers/universal_dependencies_dataset_reader.py
@@ -125,7 +125,7 @@ class UniversalDependenciesDatasetReader(DatasetReader, ABC):
for conllu_file in file_path:
file = pathlib.Path(conllu_file)
- assert conllu_file and file.exists(), f"File with path '{conllu_file}' does not exists!"
+ assert conllu_file and file.exists(), f"File with path '{conllu_file}' does not exist!"
with file.open("r", encoding="utf-8") as f:
for annotation in conllu.parse_incr(f, fields=self.fields, field_parsers=self.field_parsers):
yield self.text_to_instance(annotation)
diff --git a/combo/data/vocabulary.py b/combo/data/vocabulary.py
index 0b3e0c6acb3549a53390aee1dc1b672f9c0b7ed7..3cf5fed37405635090686edbed331ae7e1ee554e 100644
--- a/combo/data/vocabulary.py
+++ b/combo/data/vocabulary.py
@@ -300,6 +300,7 @@ class Vocabulary(FromParameters):
filename = os.path.join(directory, namespace_filename)
vocab.set_from_file(filename, is_padded, namespace=namespace, oov_token=oov_token)
+ get_slices_if_not_provided(vocab)
vocab.constructed_from = 'from_files'
return vocab
diff --git a/combo/main.py b/combo/main.py
index 6611a2d50df39f95c78d3c3ada7981382a7b8602..a426b10e88f1f801a64f7e1d83b192a834c832b5 100755
--- a/combo/main.py
+++ b/combo/main.py
@@ -1,6 +1,7 @@
import json
import logging
import pathlib
+import tempfile
from typing import Dict
import torch
@@ -15,6 +16,7 @@ from combo.utils import checks
from config import resolve
from default_model import default_ud_dataset_reader, default_data_loader
from models import ComboModel
+from modules.archival import load_archive, archive
from predict import COMBO
logger = logging.getLogger(__name__)
@@ -83,30 +85,13 @@ flags.DEFINE_integer(name="batch_size", default=1,
help="Prediction batch size.")
flags.DEFINE_boolean(name="silent", default=True,
help="Silent prediction to file (without printing to console).")
+flags.DEFINE_boolean(name="finetuning", default=False,
+ help="Finetuning mode for training.")
flags.DEFINE_enum(name="predictor_name", default="combo-lambo",
enum_values=["combo", "combo-spacy", "combo-lambo"],
help="Use predictor with whitespace, spacy or lambo (recommended) tokenizer.")
-def get_model_for_training(config_path) -> TrainableCombo:
- with open(config_path, 'rb') as f:
- config_json = json.load(f)
- train_data_loader = resolve(config_json['train_data_loader'])
- val_data_loader = resolve(config_json['val_data_loader'])
-
- model = ComboModel.from_parameters(config_json['model']['parameters'])
- train_data_loader.index_with(model.vocab)
- val_data_loader.index_with(model.vocab)
- nlp = TrainableCombo(model, torch.optim.Adam,
- optimizer_kwargs={'betas': [0.9, 0.9], 'lr': 0.002},
- validation_metrics=['EM'])
- trainer = pl.Trainer(max_epochs=FLAGS.num_epochs,
- default_root_dir=FLAGS.serialization_dir,
- gradient_clip_val=5)
- trainer.fit(model=nlp, train_dataloaders=train_data_loader, val_dataloaders=val_data_loader)
- return model
-
-
def get_saved_model(parameters) -> ComboModel:
return ComboModel.load(os.path.join(FLAGS.model_path),
config=parameters,
@@ -130,8 +115,59 @@ def get_predictor() -> COMBO:
def run(_):
if FLAGS.mode == 'train':
- trained_nlp = get_model_for_training(FLAGS.config_path)
- trained_nlp.save(FLAGS.serialization_dir)
+ if not FLAGS.finetuning:
+ checks.file_exists(FLAGS.config_path)
+ with open(FLAGS.config_path, 'r') as f:
+ params = json.load(f)
+ params = {**params, **_get_ext_vars()}
+
+ serialization_dir = tempfile.mkdtemp(prefix='combo', dir=FLAGS.serialization_dir)
+
+ model = resolve(params['model'])
+
+ if 'data_loader' in params:
+ train_data_loader = resolve(params['data_loader'])
+ else:
+ checks.file_exists(FLAGS.training_data_path)
+ train_data_loader = default_data_loader(default_ud_dataset_reader(),
+ FLAGS.training_data_path)
+ train_data_loader.index_with(model.vocab)
+
+ validation_data_loader = None
+ if 'validation_data_loader' in params:
+ validation_data_loader = resolve(params['validation_data_loader'])
+ validation_data_loader.index_with(model.vocab)
+ elif FLAGS.validation_data_path:
+ checks.file_exists(FLAGS.validation_data_path)
+ validation_data_loader = default_data_loader(default_ud_dataset_reader(),
+ FLAGS.validation_data_path)
+ validation_data_loader.index_with(model.vocab)
+
+ else:
+ model, train_data_loader, validation_data_loader = load_archive(FLAGS.model_path)
+
+ serialization_dir = tempfile.mkdtemp(prefix='combo', suffix='-finetuning', dir=FLAGS.serialization_dir)
+
+ if not train_data_loader:
+ checks.file_exists(FLAGS.finetuning_training_data_path)
+ train_data_loader = default_data_loader(default_ud_dataset_reader(),
+ FLAGS.finetuning_training_data_path)
+ if not validation_data_loader and FLAGS.finetuning_validation_data_path:
+ checks.file_exists(FLAGS.finetuning_validation_data_path)
+ validation_data_loader = default_data_loader(default_ud_dataset_reader(),
+ FLAGS.finetuning_validation_data_path)
+
+ nlp = TrainableCombo(model, torch.optim.Adam,
+ optimizer_kwargs={'betas': [0.9, 0.9], 'lr': 0.002},
+ validation_metrics=['EM'])
+ 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)
+
+ archive(model, serialization_dir)
+ logger.info(f"Training model stored in: {serialization_dir}")
+
elif FLAGS.mode == 'predict':
predictor = get_predictor()
sentence = input("Sentence:")
@@ -140,29 +176,6 @@ def run(_):
for token in prediction.tokens:
print("{:15} {:15} {:10} {:10} {:10}".format(token.text, token.lemma, token.upostag, token.head,
token.deprel))
- elif FLAGS.mode == 'finetune':
- checks.file_exists(FLAGS.model_path)
- with open(os.path.join(FLAGS.model_path, 'params.json'), 'r') as f:
- serialized = json.load(f)
- if 'dataset_reader' in serialized:
- dataset_reader = resolve(serialized['dataset_reader'])
- else:
- dataset_reader = default_ud_dataset_reader()
- model = get_saved_model(serialized)
- nlp = TrainableCombo(model, torch.optim.Adam,
- optimizer_kwargs={'betas': [0.9, 0.9], 'lr': 0.002},
- validation_metrics=['EM'])
- trainer = pl.Trainer(max_epochs=FLAGS.num_epochs,
- default_root_dir=FLAGS.serialization_dir,
- gradient_clip_val=5)
- train_data_loader = default_data_loader(dataset_reader,
- FLAGS.finetuning_training_data_path)
- val_data_loader = default_data_loader(dataset_reader,
- FLAGS.finetuning_validation_data_path)
- train_data_loader.index_with(model.vocab)
- val_data_loader.index_with(model.vocab)
- trainer.fit(model=nlp, train_dataloaders=train_data_loader, val_dataloaders=val_data_loader)
- model.save(FLAGS.serialization_dir)
def _get_ext_vars(finetuning: bool = False) -> Dict:
@@ -178,11 +191,11 @@ def _get_ext_vars(finetuning: bool = False) -> Dict:
"features": " ".join(FLAGS.features),
"targets": " ".join(FLAGS.targets),
"type": "finetuning" if finetuning else "default",
- "embedding_dim": str(FLAGS.embedding_dim),
- "cuda_device": str(FLAGS.cuda_device),
- "num_epochs": str(FLAGS.num_epochs),
- "word_batch_size": str(FLAGS.word_batch_size),
- "use_tensorboard": str(FLAGS.tensorboard),
+ "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),
}
diff --git a/combo/models/__init__.py b/combo/models/__init__.py
index 94664c634c1899b8682e1b32242eee10c26fdf4f..0d3fb493f34e3636fff102ec54790f0a856e4ba5 100644
--- a/combo/models/__init__.py
+++ b/combo/models/__init__.py
@@ -1,2 +1,3 @@
from .encoder import ComboStackedBidirectionalLSTM, ComboEncoder
from .combo_model import ComboModel
+from .model import Model
diff --git a/combo/modules/archival.py b/combo/modules/archival.py
index c32d6e8a9e59abcf0359c2c20d0b005526ee213c..85586b3af4fbd4fd82dfa2ee70b1695657e843c6 100644
--- a/combo/modules/archival.py
+++ b/combo/modules/archival.py
@@ -11,7 +11,7 @@ from io import BytesIO
from tempfile import TemporaryDirectory
from config import resolve
-from data import DatasetReader
+from data.dataset_loaders import DataLoader
from modules.model import Model
@@ -21,8 +21,8 @@ CACHE_DIRECTORY = str(CACHE_ROOT / "cache")
class Archive(NamedTuple):
model: Model
- dataset_reader: Optional[DatasetReader]
- validation_dataset_reader: Optional[DatasetReader]
+ data_loader: Optional[DataLoader]
+ validation_data_loader: Optional[DataLoader]
def add_to_tar(tar_file: tarfile.TarFile, out_stream: BytesIO, data: bytes, name: str):
@@ -34,7 +34,9 @@ def add_to_tar(tar_file: tarfile.TarFile, out_stream: BytesIO, data: bytes, name
def archive(model: Model,
- serialization_dir: Union[PathLike, str]) -> str:
+ serialization_dir: Union[PathLike, str],
+ data_loader: Optional[DataLoader] = None,
+ validation_data_loader: Optional[DataLoader] = None) -> str:
parameters = {'vocabulary': {
'type': 'from_files_vocabulary',
'parameters': {
@@ -44,6 +46,11 @@ def archive(model: Model,
}
}, 'model': model.serialize()}
+ if data_loader:
+ parameters['data_loader'] = data_loader.serialize()
+ if validation_data_loader:
+ parameters['validation_data_loader'] = validation_data_loader.serialize()
+
with (TemporaryDirectory(os.path.join('tmp')) as t,
BytesIO() as out_stream,
tarfile.open(os.path.join(serialization_dir, 'model.tar.gz'), 'w|gz') as tar_file):
@@ -71,13 +78,13 @@ def load_archive(url_or_filename: Union[PathLike, str],
with open(os.path.join(archive_file, 'config.json'), 'r') as f:
config = json.load(f)
- dataset_reader, validation_dataset_reader = None, None
+ data_loader, validation_data_loader = None, None
- if 'dataset_reader' in config:
- dataset_reader = resolve(config['dataset_reader'])
- if 'validation_dataset_reader' in config:
- validation_dataset_reader = resolve(config['validation_dataset_reader'])
+ if 'data_loader' in config:
+ data_loader = resolve(config['data_loader'])
+ if 'validation_data_loader' in config:
+ validation_data_loader = resolve(config['validation_data_loader'])
return Archive(model=model,
- dataset_reader=dataset_reader,
- validation_dataset_reader=validation_dataset_reader)
+ data_loader=data_loader,
+ validation_data_loader=validation_data_loader)
diff --git a/combo/nn/regularizers/__init__.py b/combo/nn/regularizers/__init__.py
index 27f101d7bd31e0502eee7722c973b0d2629f62fe..bc98f43f1cf7b169c544223bf7a358a6d7dbb9fb 100644
--- a/combo/nn/regularizers/__init__.py
+++ b/combo/nn/regularizers/__init__.py
@@ -1,2 +1,3 @@
from .regularizers import *
-from .regularizer import *
\ No newline at end of file
+from .regularizer import *
+from .regularizer_applicator import RegularizerApplicator
diff --git a/combo/polish_model_training.ipynb b/combo/polish_model_training.ipynb
index 7efc01e13779e3690e2efd7a473c6785cd162af8..6672180436d81f0d3d0d3e491b9b8ee02df3f80e 100644
--- a/combo/polish_model_training.ipynb
+++ b/combo/polish_model_training.ipynb
@@ -2,27 +2,27 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"outputs": [],
"source": [
"# The path where the training and validation datasets are stored\n",
- "TRAINING_DATA_PATH: str = '/Users/majajablonska/Downloads/PDBUD-master-85167180bcbe0565a09269257456961365cf6ff3/PDB-UD/PDB-UD/PDBUD_train.conllu'\n",
- "VALIDATION_DATA_PATH: str = '/Users/majajablonska/Downloads/PDBUD-master-85167180bcbe0565a09269257456961365cf6ff3/PDB-UD/PDB-UD/PDBUD_val.conllu'\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",
"# The path where the model can be saved to\n",
"SERIALIZATION_DIR: str = \"/Users/majajablonska/Documents/Workspace/combotest\""
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:36:28.266635Z",
- "start_time": "2023-10-11T08:36:27.158291Z"
+ "end_time": "2023-10-15T12:35:09.621320Z",
+ "start_time": "2023-10-15T12:35:09.407839Z"
}
},
"id": "b28c7d8bacb08d02"
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"outputs": [],
"source": [
"import os\n",
@@ -52,15 +52,15 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:36:31.986641Z",
- "start_time": "2023-10-11T08:36:27.382007Z"
+ "end_time": "2023-10-15T12:35:17.384934Z",
+ "start_time": "2023-10-15T12:35:09.418819Z"
}
},
"id": "initial_id"
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"outputs": [
{
"name": "stdout",
@@ -78,7 +78,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "2ccd5ef8438f48418d1c2b29f2aa789b"
+ "model_id": "22fcc24a17304631b9ce8b5738210612"
}
},
"metadata": {},
@@ -90,7 +90,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "5cb100d63b854e0aad0f39db0679be5c"
+ "model_id": "a14b090b880f4d6a8314acedeadd8c1f"
}
},
"metadata": {},
@@ -102,7 +102,19 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "0d5396d8d87945a692d2d71fd1bf9b14"
+ "model_id": "4c47cc124c6f42db9fb7b3b9a07709de"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "building vocabulary: 0it [00:00, ?it/s]",
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "10cc5ff44d5f48b98c9c35de7eec0b1e"
}
},
"metadata": {},
@@ -165,20 +177,30 @@
" only_include_pretrained_words=False,\n",
" oov_token='_',\n",
" padding_token='__PAD__'\n",
- ")"
+ ")\n",
+ "\n",
+ "val_vocabulary = Vocabulary.from_data_loader_extended(\n",
+ " val_data_loader,\n",
+ " non_padded_namespaces=['head_labels'],\n",
+ " only_include_pretrained_words=False,\n",
+ " oov_token='_',\n",
+ " padding_token='__PAD__'\n",
+ ")\n",
+ "\n",
+ "vocabulary.extend_from_vocab(val_vocabulary)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:37:01.898078Z",
- "start_time": "2023-10-11T08:36:31.982596Z"
+ "end_time": "2023-10-15T12:35:40.187804Z",
+ "start_time": "2023-10-15T12:35:17.370019Z"
}
},
"id": "d74957f422f0b05b"
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"outputs": [],
"source": [
"seq_encoder = ComboEncoder(layer_dropout_probability=0.33,\n",
@@ -193,15 +215,15 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:37:02.701990Z",
- "start_time": "2023-10-11T08:37:01.927460Z"
+ "end_time": "2023-10-15T12:35:40.901949Z",
+ "start_time": "2023-10-15T12:35:40.192629Z"
}
},
"id": "fa724d362fd6bd23"
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"outputs": [
{
"name": "stdout",
@@ -212,9 +234,9 @@
},
{
"data": {
- "text/plain": "<generator object SimpleDataLoader.iter_instances at 0x7fc82dac9430>"
+ "text/plain": "<generator object SimpleDataLoader.iter_instances at 0x7fec3dd685f0>"
},
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -240,21 +262,21 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:37:02.769503Z",
- "start_time": "2023-10-11T08:37:02.705688Z"
+ "end_time": "2023-10-15T12:35:40.965741Z",
+ "start_time": "2023-10-15T12:35:40.904199Z"
}
},
"id": "f8a10f9892005fca"
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"outputs": [
{
"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.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.sso.sso_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.sso.sso_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
+ "Some weights of the model checkpoint at allegro/herbert-base-cased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.sso.sso_relationship.bias', 'cls.predictions.decoder.weight', 'cls.sso.sso_relationship.weight', 'cls.predictions.decoder.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias']\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"
]
@@ -386,15 +408,15 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:37:06.802516Z",
- "start_time": "2023-10-11T08:37:02.748561Z"
+ "end_time": "2023-10-15T12:35:47.716817Z",
+ "start_time": "2023-10-15T12:35:40.960064Z"
}
},
"id": "437d12054baaffa1"
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"outputs": [],
"source": [
"data_loader.index_with(vocabulary)\n",
@@ -405,15 +427,15 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:37:44.084551Z",
- "start_time": "2023-10-11T08:37:06.818779Z"
+ "end_time": "2023-10-15T12:36:27.968154Z",
+ "start_time": "2023-10-15T12:35:47.794229Z"
}
},
"id": "e131e0ec75dc6927"
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"outputs": [],
"source": [
"val_data_loader.index_with(vocabulary)"
@@ -421,15 +443,15 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:38:29.223247Z",
- "start_time": "2023-10-11T08:37:44.066248Z"
+ "end_time": "2023-10-15T12:36:31.913752Z",
+ "start_time": "2023-10-15T12:36:27.951466Z"
}
},
"id": "195c71fcf8170ff"
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"outputs": [
{
"name": "stderr",
@@ -456,15 +478,15 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:38:37.162449Z",
- "start_time": "2023-10-11T08:38:29.186645Z"
+ "end_time": "2023-10-15T12:36:32.265366Z",
+ "start_time": "2023-10-15T12:36:31.923212Z"
}
},
"id": "cefc5173154d1605"
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"outputs": [
{
"name": "stderr",
@@ -475,10 +497,10 @@
"-------------------------------------\n",
"0 | model | ComboModel | 136 M \n",
"-------------------------------------\n",
- "12.1 M Trainable params\n",
+ "12.2 M Trainable params\n",
"124 M Non-trainable params\n",
"136 M Total params\n",
- "546.107 Total estimated model params size (MB)\n"
+ "546.647 Total estimated model params size (MB)\n"
]
},
{
@@ -487,7 +509,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "bf1714d7fc554b17a16d6f0d3ccb96b1"
+ "model_id": "c0fdd5ff1efa43f7b2317abcd0c00fb9"
}
},
"metadata": {},
@@ -509,7 +531,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "63ad53394ef8472e873a9cf9cb093eeb"
+ "model_id": "5c0b5cee0be2439588dc665a4bb4ba21"
}
},
"metadata": {},
@@ -521,7 +543,7 @@
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
- "model_id": "8a783c14f75245d9ac3b4339bd4e1a44"
+ "model_id": "32bc016d2f99452db3e6dbc9575e4cc8"
}
},
"metadata": {},
@@ -541,15 +563,15 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:39:43.280379Z",
- "start_time": "2023-10-11T08:38:29.495446Z"
+ "end_time": "2023-10-15T12:38:24.497905Z",
+ "start_time": "2023-10-15T12:36:32.262713Z"
}
},
"id": "e5af131bae4b1a33"
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"outputs": [],
"source": [
"predictor = COMBO(model, dataset_reader)"
@@ -557,15 +579,15 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:39:43.296706Z",
- "start_time": "2023-10-11T08:39:42.829494Z"
+ "end_time": "2023-10-15T12:38:32.892083Z",
+ "start_time": "2023-10-15T12:38:23.228734Z"
}
},
"id": "3e23413c86063183"
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"outputs": [],
"source": [
"a = predictor(\"Cześć, jestem psem.\")"
@@ -573,24 +595,24 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:39:43.578465Z",
- "start_time": "2023-10-11T08:39:42.842561Z"
+ "end_time": "2023-10-15T12:38:32.986864Z",
+ "start_time": "2023-10-15T12:38:25.706104Z"
}
},
"id": "d555d7f0223a624b"
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TOKEN LEMMA UPOS HEAD DEPREL \n",
- "Cześć, ?????a NOUN 0 root \n",
- "jestem ?????a NOUN 1 case \n",
- "psem. ????a PUNCT 1 punct \n"
+ "Cześć, ????? NOUN 0 root \n",
+ "jestem ????? NOUN 1 punct \n",
+ "psem. ???? NOUN 2 punct \n"
]
}
],
@@ -602,35 +624,65 @@
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:39:43.615606Z",
- "start_time": "2023-10-11T08:39:43.250741Z"
+ "end_time": "2023-10-15T12:38:33.022415Z",
+ "start_time": "2023-10-15T12:38:28.029459Z"
}
},
"id": "a68cd3861e1ceb67"
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 15,
+ "outputs": [],
+ "source": [
+ "from modules.archival import archive"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2023-10-15T12:38:33.024270Z",
+ "start_time": "2023-10-15T12:38:28.177734Z"
+ }
+ },
+ "id": "d0f43f4493218b5"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Directory /Users/majajablonska/Documents/Workspace/combotest/saved_model/vocabulary is not empty\n"
- ]
+ "data": {
+ "text/plain": "'/Users/majajablonska/Documents/combo'"
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "model.save(os.path.join(SERIALIZATION_DIR, 'saved_model'))"
+ "archive(model, '/Users/majajablonska/Documents/combo', data_loader, val_data_loader)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2023-10-11T08:39:44.927330Z",
- "start_time": "2023-10-11T08:39:43.254017Z"
+ "end_time": "2023-10-15T13:46:22.801925Z",
+ "start_time": "2023-10-15T13:44:31.564518Z"
}
},
- "id": "d0f43f4493218b5"
+ "id": "ec92aa5bb5bb3605"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-10-15T12:38:32.141265Z"
+ }
+ },
+ "id": "953bd53cccd5f890"
}
],
"metadata": {
diff --git a/combo/predict.py b/combo/predict.py
index e3b285ecf54764acd8e8341b701acd151959e620..9a3a42f327ecbd17502c4c00de394bd78214ed1e 100644
--- a/combo/predict.py
+++ b/combo/predict.py
@@ -18,6 +18,7 @@ from combo.data.dataset_readers.dataset_reader import DatasetReader
from combo.data.instance import JsonDict
from combo.predictors import PredictorModule
from combo.utils import download, graph
+from modules.model import Model
logger = logging.getLogger(__name__)
@@ -26,7 +27,7 @@ logger = logging.getLogger(__name__)
class COMBO(PredictorModule):
@register_arguments
def __init__(self,
- model: models.Model,
+ model: Model,
dataset_reader: DatasetReader,
tokenizer: data.Tokenizer = tokenizers.WhitespaceTokenizer(),
batch_size: int = 1024,