From ac65eb6950c5e440886ff21aec18e3fc1afb6bbd Mon Sep 17 00:00:00 2001
From: alina-wroblewska <45227016+alina-wroblewska@users.noreply.github.com>
Date: Wed, 28 Oct 2020 09:23:02 +0100
Subject: [PATCH 01/12] Create LICENSE
---
LICENSE | 674 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 674 insertions(+)
create mode 100644 LICENSE
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..f288702
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+ <one line to give the program's name and a brief idea of what it does.>
+ Copyright (C) <year> <name of author>
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ <program> Copyright (C) <year> <name of author>
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library. If this is what you want to do, use the GNU Lesser General
+Public License instead of this License. But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
--
GitLab
From 9d339859a63787b5211a66616d523b6c066159c6 Mon Sep 17 00:00:00 2001
From: Mateusz Klimaszewski <mk.klimaszewski@gmail.com>
Date: Mon, 14 Sep 2020 08:56:56 +0200
Subject: [PATCH 02/12] Add herberta configuration.
---
README.md | 4 ++++
combo/training/trainer.py | 12 ++++++------
setup.py | 8 ++++----
3 files changed, 14 insertions(+), 10 deletions(-)
diff --git a/README.md b/README.md
index 0ef5706..680673c 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,9 @@
## Installation
+### HERBERTA notes:
+
+Install herberta transformers package **before** running command below
+
Clone this repository and run:
```bash
python setup.py develop
diff --git a/combo/training/trainer.py b/combo/training/trainer.py
index 2096d48..234bdd7 100644
--- a/combo/training/trainer.py
+++ b/combo/training/trainer.py
@@ -54,12 +54,12 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
batch_callbacks: List[training.BatchCallback] = None,
epoch_callbacks: List[training.EpochCallback] = None, distributed: bool = False, local_rank: int = 0,
world_size: int = 1, num_gradient_accumulation_steps: int = 1,
- opt_level: Optional[str] = None) -> None:
+ use_amp: bool = False) -> None:
super().__init__(model, optimizer, data_loader, patience, validation_metric, validation_data_loader, num_epochs,
serialization_dir, checkpointer, cuda_device, grad_norm, grad_clipping,
learning_rate_scheduler, momentum_scheduler, tensorboard_writer, moving_average,
batch_callbacks, epoch_callbacks, distributed, local_rank, world_size,
- num_gradient_accumulation_steps, opt_level)
+ num_gradient_accumulation_steps, use_amp)
# TODO extract param to constructor (+ constructor method?)
self.validate_every_n = 5
@@ -125,7 +125,8 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
self.model,
val_loss,
val_reg_loss,
- num_batches,
+ num_batches=num_batches,
+ batch_loss=None,
reset=True,
world_size=self._world_size,
cuda_device=self.cuda_device,
@@ -231,7 +232,7 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
world_size: int = 1,
num_gradient_accumulation_steps: int = 1,
opt_level: Optional[str] = None,
- no_grad: List[str] = None,
+ use_amp: bool = False,
optimizer: common.Lazy[optimizers.Optimizer] = None,
learning_rate_scheduler: common.Lazy[learning_rate_schedulers.LearningRateScheduler] = None,
momentum_scheduler: common.Lazy[momentum_schedulers.MomentumScheduler] = None,
@@ -258,8 +259,7 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
distributed=distributed,
world_size=world_size,
num_gradient_accumulation_steps=num_gradient_accumulation_steps,
- opt_level=opt_level,
- no_grad=no_grad,
+ use_amp=use_amp,
optimizer=optimizer,
learning_rate_scheduler=learning_rate_scheduler,
momentum_scheduler=momentum_scheduler,
diff --git a/setup.py b/setup.py
index d3aac1a..228e025 100644
--- a/setup.py
+++ b/setup.py
@@ -3,17 +3,17 @@ from setuptools import find_packages, setup
REQUIREMENTS = [
'absl-py==0.9.0',
- 'allennlp==1.0.0',
+ 'allennlp==1.1.0',
'conllu==2.3.2',
'dataclasses-json==0.5.2',
'joblib==0.14.1',
'jsonnet==0.15.0',
'requests==2.23.0',
- 'overrides==3.0.0',
+ 'overrides==3.1.0',
'tensorboard==2.1.0',
- 'torch>=1.5.0,<1.6.0',
+ 'torch==1.6.0',
'tqdm==4.43.0',
- 'transformers==2.9.1',
+ 'transformers>=3.0.0,<3.1.0',
'urllib3==1.24.2',
]
--
GitLab
From dfec6d56ca1f642c45150497d0fdf986b2c62c9d Mon Sep 17 00:00:00 2001
From: Mateusz Klimaszewski <mk.klimaszewski@gmail.com>
Date: Mon, 14 Sep 2020 14:28:37 +0200
Subject: [PATCH 03/12] Fix herberta training.
---
README.md | 6 +-----
combo/data/api.py | 11 +++++++++--
combo/data/dataset.py | 19 +++++++++++++++++--
combo/main.py | 12 +++++++-----
combo/predict.py | 2 +-
combo/training/trainer.py | 1 +
setup.py | 2 +-
7 files changed, 37 insertions(+), 16 deletions(-)
diff --git a/README.md b/README.md
index 680673c..c5d0fd0 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,5 @@
## Installation
-### HERBERTA notes:
-
-Install herberta transformers package **before** running command below
-
Clone this repository and run:
```bash
python setup.py develop
@@ -86,7 +82,7 @@ Input: one sentence per line.
Output: List of token jsons.
```bash
-combo --mode predict --model_path your_model_tar_gz --input_file your_text_file --output_file your_output_file --silent
+combo --mode predict --model_path your_model_tar_gz --input_file your_text_file --output_file your_output_file --silent --noconllu_format
```
#### Advanced
diff --git a/combo/data/api.py b/combo/data/api.py
index b0763b6..10a3a72 100644
--- a/combo/data/api.py
+++ b/combo/data/api.py
@@ -20,6 +20,7 @@ class Token:
deprel: Optional[str] = None
deps: Optional[str] = None
misc: Optional[str] = None
+ semrel: Optional[str] = None
@dataclass_json
@@ -37,8 +38,14 @@ class _TokenList(conllu.TokenList):
return 'TokenList<' + ', '.join(token['token'] for token in self) + '>'
-def sentence2conllu(sentence: Sentence) -> conllu.TokenList:
- tokens = [collections.OrderedDict(t.to_dict()) for t in sentence.tokens]
+def sentence2conllu(sentence: Sentence, keep_semrel: bool = True) -> conllu.TokenList:
+ tokens = []
+ for token in sentence.tokens:
+ token_dict = collections.OrderedDict(token.to_dict())
+ # Remove semrel to have default conllu format.
+ if not keep_semrel:
+ del token_dict["semrel"]
+ tokens.append(token_dict)
# Range tokens must be tuple not list, this is conllu library requirement
for t in tokens:
if type(t["id"]) == list:
diff --git a/combo/data/dataset.py b/combo/data/dataset.py
index b5f5c30..459a755 100644
--- a/combo/data/dataset.py
+++ b/combo/data/dataset.py
@@ -41,7 +41,7 @@ class UniversalDependenciesDatasetReader(allen_data.DatasetReader):
"Features and targets cannot share elements! "
"Remove {} from either features or targets.".format(intersection)
)
- self._use_sem = use_sem
+ self.use_sem = use_sem
# *.conllu readers configuration
fields = list(parser.DEFAULT_FIELDS)
@@ -49,7 +49,7 @@ class UniversalDependenciesDatasetReader(allen_data.DatasetReader):
field_parsers = parser.DEFAULT_FIELD_PARSERS
# Do not make it nullable
field_parsers.pop("xpostag", None)
- if self._use_sem:
+ if self.use_sem:
fields = list(fields)
fields.append("semrel")
field_parsers["semrel"] = lambda line, i: line[i]
@@ -113,8 +113,23 @@ class UniversalDependenciesDatasetReader(allen_data.DatasetReader):
fields_[target_name] = allen_fields.SequenceLabelField(target_values, text_field,
label_namespace=target_name + "_labels")
+ # Restore feats fields to string representation
+ # parser.serialize_field doesn't handle key without value
+ for token in tree.tokens:
+ if "feats" in token:
+ feats = token["feats"]
+ if feats:
+ feats_values = []
+ for k, v in feats.items():
+ feats_values.append('='.join((k, v)) if v else k)
+ field = "|".join(feats_values)
+ else:
+ field = "_"
+ token["feats"] = field
+
# metadata
fields_["metadata"] = allen_fields.MetadataField({"input": tree, "field_names": self.fields})
+
return allen_data.Instance(fields_)
@staticmethod
diff --git a/combo/main.py b/combo/main.py
index 4dc0056..c7aac87 100644
--- a/combo/main.py
+++ b/combo/main.py
@@ -13,7 +13,7 @@ from allennlp.common import checks as allen_checks, util
from allennlp.models import archival
from combo import predict
-from combo.data import dataset
+from combo.data import api, dataset
from combo.utils import checks
logger = logging.getLogger(__name__)
@@ -76,6 +76,8 @@ flags.DEFINE_string(name="model_path", default=None,
help="Pretrained model path.")
flags.DEFINE_string(name="input_file", default=None,
help="File to predict path")
+flags.DEFINE_boolean(name="conllu_format", default=True,
+ help="Prediction based on conllu format (instead of raw text).")
flags.DEFINE_integer(name="batch_size", default=1,
help="Prediction batch size.")
flags.DEFINE_boolean(name="silent", default=True,
@@ -136,13 +138,13 @@ def run(_):
model=model,
dataset_reader=dataset_reader
)
- test_path = FLAGS.test_path
- test_trees = dataset_reader.read(test_path)
+ test_trees = dataset_reader.read(FLAGS.test_path)
with open(FLAGS.output_file, "w") as file:
for tree in test_trees:
- file.writelines(predictor.predict_instance_as_tree(tree).serialize())
+ file.writelines(api.sentence2conllu(predictor.predict_instance(tree),
+ keep_semrel=dataset_reader.use_sem).serialize())
else:
- use_dataset_reader = ".conllu" in FLAGS.input_file.lower()
+ use_dataset_reader = FLAGS.conllu_format
predictor = _get_predictor()
if use_dataset_reader:
predictor.line_to_conllu = True
diff --git a/combo/predict.py b/combo/predict.py
index 0ee80a9..ebbb372 100644
--- a/combo/predict.py
+++ b/combo/predict.py
@@ -128,7 +128,7 @@ class SemanticMultitaskPredictor(predictor.Predictor):
# Check whether serialized (str) tree or token's list
# Serialized tree has already separators between lines
if self.line_to_conllu:
- return sentence2conllu(outputs).serialize()
+ return sentence2conllu(outputs, keep_semrel=self._dataset_reader.use_sem).serialize()
else:
return outputs.to_json()
diff --git a/combo/training/trainer.py b/combo/training/trainer.py
index 234bdd7..772b9b0 100644
--- a/combo/training/trainer.py
+++ b/combo/training/trainer.py
@@ -127,6 +127,7 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
val_reg_loss,
num_batches=num_batches,
batch_loss=None,
+ batch_reg_loss=None,
reset=True,
world_size=self._world_size,
cuda_device=self.cuda_device,
diff --git a/setup.py b/setup.py
index 228e025..dd21555 100644
--- a/setup.py
+++ b/setup.py
@@ -14,7 +14,7 @@ REQUIREMENTS = [
'torch==1.6.0',
'tqdm==4.43.0',
'transformers>=3.0.0,<3.1.0',
- 'urllib3==1.24.2',
+ 'urllib3>=1.25.11',
]
setup(
--
GitLab
From 2a41300666a4b8bd5e9ebc97f92f56367b8b13fa Mon Sep 17 00:00:00 2001
From: Mateusz Klimaszewski <mk.klimaszewski@gmail.com>
Date: Tue, 3 Nov 2020 09:54:50 +0100
Subject: [PATCH 04/12] Update allennlp to 1.2.0.
---
combo/data/token_indexers/__init__.py | 1 +
...etrained_transformer_mismatched_indexer.py | 85 +++++++++++++++++++
combo/models/embeddings.py | 8 +-
combo/training/trainer.py | 20 +++--
config.template.jsonnet | 6 +-
setup.py | 5 +-
6 files changed, 111 insertions(+), 14 deletions(-)
create mode 100644 combo/data/token_indexers/pretrained_transformer_mismatched_indexer.py
diff --git a/combo/data/token_indexers/__init__.py b/combo/data/token_indexers/__init__.py
index 1b918b3..550a80b 100644
--- a/combo/data/token_indexers/__init__.py
+++ b/combo/data/token_indexers/__init__.py
@@ -1,2 +1,3 @@
+from .pretrained_transformer_mismatched_indexer import PretrainedTransformerMismatchedIndexer
from .token_characters_indexer import TokenCharactersIndexer
from .token_features_indexer import TokenFeatsIndexer
diff --git a/combo/data/token_indexers/pretrained_transformer_mismatched_indexer.py b/combo/data/token_indexers/pretrained_transformer_mismatched_indexer.py
new file mode 100644
index 0000000..fbe2368
--- /dev/null
+++ b/combo/data/token_indexers/pretrained_transformer_mismatched_indexer.py
@@ -0,0 +1,85 @@
+from typing import Optional, Dict, Any, List, Tuple
+
+from allennlp import data
+from allennlp.data import token_indexers, tokenizers
+
+
+@data.TokenIndexer.register("pretrained_transformer_mismatched_fixed")
+class PretrainedTransformerMismatchedIndexer(token_indexers.PretrainedTransformerMismatchedIndexer):
+
+ def __init__(self, model_name: str, namespace: str = "tags", max_length: int = None,
+ tokenizer_kwargs: Optional[Dict[str, Any]] = None, **kwargs) -> None:
+ # The matched version v.s. mismatchedńskie
+ super().__init__(model_name, namespace, max_length, tokenizer_kwargs, **kwargs)
+ self._matched_indexer = PretrainedTransformerIndexer(
+ model_name,
+ namespace=namespace,
+ max_length=max_length,
+ tokenizer_kwargs=tokenizer_kwargs,
+ **kwargs,
+ )
+ self._allennlp_tokenizer = self._matched_indexer._allennlp_tokenizer
+ self._tokenizer = self._matched_indexer._tokenizer
+ self._num_added_start_tokens = self._matched_indexer._num_added_start_tokens
+ self._num_added_end_tokens = self._matched_indexer._num_added_end_tokens
+
+
+class PretrainedTransformerIndexer(token_indexers.PretrainedTransformerIndexer):
+
+ def __init__(
+ self,
+ model_name: str,
+ namespace: str = "tags",
+ max_length: int = None,
+ tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+ **kwargs,
+ ) -> None:
+ super().__init__(model_name, namespace, max_length, tokenizer_kwargs, **kwargs)
+ self._namespace = namespace
+ self._allennlp_tokenizer = PretrainedTransformerTokenizer(
+ model_name, tokenizer_kwargs=tokenizer_kwargs
+ )
+ self._tokenizer = self._allennlp_tokenizer.tokenizer
+ self._added_to_vocabulary = False
+
+ self._num_added_start_tokens = len(self._allennlp_tokenizer.single_sequence_start_tokens)
+ self._num_added_end_tokens = len(self._allennlp_tokenizer.single_sequence_end_tokens)
+
+ self._max_length = max_length
+ if self._max_length is not None:
+ num_added_tokens = len(self._allennlp_tokenizer.tokenize("a")) - 1
+ self._effective_max_length = ( # we need to take into account special tokens
+ self._max_length - num_added_tokens
+ )
+ if self._effective_max_length <= 0:
+ raise ValueError(
+ "max_length needs to be greater than the number of special tokens inserted."
+ )
+
+
+class PretrainedTransformerTokenizer(tokenizers.PretrainedTransformerTokenizer):
+
+ def _intra_word_tokenize(
+ self, string_tokens: List[str]
+ ) -> Tuple[List[data.Token], List[Optional[Tuple[int, int]]]]:
+ tokens: List[data.Token] = []
+ offsets: List[Optional[Tuple[int, int]]] = []
+ for token_string in string_tokens:
+ wordpieces = self.tokenizer.encode_plus(
+ token_string,
+ add_special_tokens=False,
+ return_tensors=None,
+ return_offsets_mapping=False,
+ return_attention_mask=False,
+ )
+ wp_ids = wordpieces["input_ids"]
+
+ if len(wp_ids) > 0:
+ offsets.append((len(tokens), len(tokens) + len(wp_ids) - 1))
+ tokens.extend(
+ data.Token(text=wp_text, text_id=wp_id)
+ for wp_id, wp_text in zip(wp_ids, self.tokenizer.convert_ids_to_tokens(wp_ids))
+ )
+ else:
+ offsets.append(None)
+ return tokens, offsets
diff --git a/combo/models/embeddings.py b/combo/models/embeddings.py
index edb37a1..5cad959 100644
--- a/combo/models/embeddings.py
+++ b/combo/models/embeddings.py
@@ -1,5 +1,5 @@
"""Embeddings."""
-from typing import Optional
+from typing import Optional, Dict, Any
import torch
import torch.nn as nn
@@ -110,8 +110,10 @@ class TransformersWordEmbedder(token_embedders.PretrainedTransformerMismatchedEm
projection_dim: int,
projection_activation: Optional[allen_nn.Activation] = lambda x: x,
projection_dropout_rate: Optional[float] = 0.0,
- freeze_transformer: bool = True):
- super().__init__(model_name)
+ freeze_transformer: bool = True,
+ tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+ transformer_kwargs: Optional[Dict[str, Any]] = None):
+ super().__init__(model_name, tokenizer_kwargs=tokenizer_kwargs, transformer_kwargs=transformer_kwargs)
self.freeze_transformer = freeze_transformer
if self.freeze_transformer:
self._matched_embedder.eval()
diff --git a/combo/training/trainer.py b/combo/training/trainer.py
index 772b9b0..aeb9f09 100644
--- a/combo/training/trainer.py
+++ b/combo/training/trainer.py
@@ -3,7 +3,7 @@ import logging
import os
import time
import traceback
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
import torch
import torch.distributed as dist
@@ -30,7 +30,8 @@ logger = logging.getLogger(__name__)
@training.EpochCallback.register("transfer_patience")
class TransferPatienceEpochCallback(training.EpochCallback):
- def __call__(self, trainer: "training.GradientDescentTrainer", metrics: Dict[str, Any], epoch: int) -> None:
+ def __call__(self, trainer: "training.GradientDescentTrainer", metrics: Dict[str, Any], epoch: int,
+ is_master: bool) -> None:
if trainer._learning_rate_scheduler and trainer._learning_rate_scheduler.patience is not None:
trainer._metric_tracker._patience = trainer._learning_rate_scheduler.patience
trainer._metric_tracker._epochs_with_no_improvement = 0
@@ -45,20 +46,23 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
patience: Optional[int] = None, validation_metric: str = "-loss",
validation_data_loader: data.DataLoader = None, num_epochs: int = 20,
serialization_dir: Optional[str] = None, checkpointer: checkpointer.Checkpointer = None,
- cuda_device: int = -1,
+ cuda_device: Optional[Union[int, torch.device]] = -1,
grad_norm: Optional[float] = None, grad_clipping: Optional[float] = None,
learning_rate_scheduler: Optional[learning_rate_schedulers.LearningRateScheduler] = None,
momentum_scheduler: Optional[momentum_schedulers.MomentumScheduler] = None,
tensorboard_writer: allen_tensorboard_writer.TensorboardWriter = None,
moving_average: Optional[moving_average.MovingAverage] = None,
batch_callbacks: List[training.BatchCallback] = None,
- epoch_callbacks: List[training.EpochCallback] = None, distributed: bool = False, local_rank: int = 0,
+ epoch_callbacks: List[training.EpochCallback] = None,
+ end_callbacks: List[training.EpochCallback] = None,
+ trainer_callbacks: List[training.TrainerCallback] = None,
+ distributed: bool = False, local_rank: int = 0,
world_size: int = 1, num_gradient_accumulation_steps: int = 1,
use_amp: bool = False) -> None:
super().__init__(model, optimizer, data_loader, patience, validation_metric, validation_data_loader, num_epochs,
serialization_dir, checkpointer, cuda_device, grad_norm, grad_clipping,
learning_rate_scheduler, momentum_scheduler, tensorboard_writer, moving_average,
- batch_callbacks, epoch_callbacks, distributed, local_rank, world_size,
+ batch_callbacks, epoch_callbacks, end_callbacks, trainer_callbacks, distributed, local_rank, world_size,
num_gradient_accumulation_steps, use_amp)
# TODO extract param to constructor (+ constructor method?)
self.validate_every_n = 5
@@ -93,7 +97,7 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
metrics["best_validation_" + key] = value
for callback in self._epoch_callbacks:
- callback(self, metrics={}, epoch=-1)
+ callback(self, metrics={}, epoch=-1, is_master=True)
for epoch in range(epoch_counter, self._num_epochs):
epoch_start_time = time.time()
@@ -190,7 +194,7 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
dist.barrier()
for callback in self._epoch_callbacks:
- callback(self, metrics=metrics, epoch=epoch)
+ callback(self, metrics=metrics, epoch=epoch, is_master=self._master)
epoch_elapsed_time = time.time() - epoch_start_time
logger.info("Epoch duration: %s", datetime.timedelta(seconds=epoch_elapsed_time))
@@ -243,7 +247,7 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
batch_callbacks: List[training.BatchCallback] = None,
epoch_callbacks: List[training.EpochCallback] = None,
) -> "training.Trainer":
- if tensorboard_writer.construct() is None:
+ if tensorboard_writer is None:
tensorboard_writer = common.Lazy(combo_tensorboard_writer.NullTensorboardWriter)
return super().from_partial_objects(
model=model,
diff --git a/config.template.jsonnet b/config.template.jsonnet
index 57f02ae..8e5ddc9 100644
--- a/config.template.jsonnet
+++ b/config.template.jsonnet
@@ -112,8 +112,10 @@ assert pretrained_tokens == null || pretrained_transformer_name == null: "Can't
use_sem: if in_targets("semrel") then true else false,
token_indexers: {
token: if use_transformer then {
- type: "pretrained_transformer_mismatched",
+ type: "pretrained_transformer_mismatched_fixed",
model_name: pretrained_transformer_name,
+ tokenizer_kwargs: if std.startsWith(pretrained_transformer_name, "allegro/herbert")
+ then {use_fast: false} else {},
} else {
# SingleIdTokenIndexer, token as single int
type: "single_id",
@@ -202,6 +204,8 @@ assert pretrained_tokens == null || pretrained_transformer_name == null: "Can't
type: "transformers_word_embeddings",
model_name: pretrained_transformer_name,
projection_dim: projected_embedding_dim,
+ tokenizer_kwargs: if std.startsWith(pretrained_transformer_name, "allegro/herbert")
+ then {use_fast: false} else {},
} else {
type: "embeddings_projected",
embedding_dim: embedding_dim,
diff --git a/setup.py b/setup.py
index dd21555..6ce7e3c 100644
--- a/setup.py
+++ b/setup.py
@@ -3,8 +3,9 @@ from setuptools import find_packages, setup
REQUIREMENTS = [
'absl-py==0.9.0',
- 'allennlp==1.1.0',
+ 'allennlp==1.2.0',
'conllu==2.3.2',
+ 'dataclasses==0.5',
'dataclasses-json==0.5.2',
'joblib==0.14.1',
'jsonnet==0.15.0',
@@ -13,7 +14,7 @@ REQUIREMENTS = [
'tensorboard==2.1.0',
'torch==1.6.0',
'tqdm==4.43.0',
- 'transformers>=3.0.0,<3.1.0',
+ 'transformers>=3.4.0,<3.5',
'urllib3>=1.25.11',
]
--
GitLab
From ea87067930488a1b0cbf3d06944824d504f89531 Mon Sep 17 00:00:00 2001
From: Mateusz Klimaszewski <mk.klimaszewski@gmail.com>
Date: Tue, 3 Nov 2020 10:33:11 +0100
Subject: [PATCH 05/12] Add TODO note for next AllenNLP update.
---
.../pretrained_transformer_mismatched_indexer.py | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/combo/data/token_indexers/pretrained_transformer_mismatched_indexer.py b/combo/data/token_indexers/pretrained_transformer_mismatched_indexer.py
index fbe2368..3eee80e 100644
--- a/combo/data/token_indexers/pretrained_transformer_mismatched_indexer.py
+++ b/combo/data/token_indexers/pretrained_transformer_mismatched_indexer.py
@@ -6,10 +6,11 @@ from allennlp.data import token_indexers, tokenizers
@data.TokenIndexer.register("pretrained_transformer_mismatched_fixed")
class PretrainedTransformerMismatchedIndexer(token_indexers.PretrainedTransformerMismatchedIndexer):
+ """TODO(mklimasz) Remove during next allennlp update, fixed on allennlp master."""
def __init__(self, model_name: str, namespace: str = "tags", max_length: int = None,
tokenizer_kwargs: Optional[Dict[str, Any]] = None, **kwargs) -> None:
- # The matched version v.s. mismatchedńskie
+ # The matched version v.s. mismatched
super().__init__(model_name, namespace, max_length, tokenizer_kwargs, **kwargs)
self._matched_indexer = PretrainedTransformerIndexer(
model_name,
--
GitLab
From 7ebf88b84bbccfd91a023ba79c321fecbc8bdf13 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alina=20Wr=C3=B3blewska?= <alina@ipipan.waw.pl>
Date: Tue, 28 Jul 2020 18:13:38 +0200
Subject: [PATCH 06/12] Readme added
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index c5d0fd0..0ef5706 100644
--- a/README.md
+++ b/README.md
@@ -82,7 +82,7 @@ Input: one sentence per line.
Output: List of token jsons.
```bash
-combo --mode predict --model_path your_model_tar_gz --input_file your_text_file --output_file your_output_file --silent --noconllu_format
+combo --mode predict --model_path your_model_tar_gz --input_file your_text_file --output_file your_output_file --silent
```
#### Advanced
--
GitLab
From 43a618224e91fa5018f6633f64e4e9a47163c15a Mon Sep 17 00:00:00 2001
From: Mateusz Klimaszewski <mk.klimaszewski@gmail.com>
Date: Tue, 3 Nov 2020 11:31:21 +0100
Subject: [PATCH 07/12] Fix dataclass package issue for python 3.6.
---
setup.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/setup.py b/setup.py
index 6ce7e3c..9529a0c 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@ REQUIREMENTS = [
'absl-py==0.9.0',
'allennlp==1.2.0',
'conllu==2.3.2',
- 'dataclasses==0.5',
+ 'dataclasses;python_version<"3.7"',
'dataclasses-json==0.5.2',
'joblib==0.14.1',
'jsonnet==0.15.0',
--
GitLab
From 90d7c1ef6de2eeda855a136285615f56ec61a08f Mon Sep 17 00:00:00 2001
From: Mateusz Klimaszewski <mk.klimaszewski@gmail.com>
Date: Tue, 3 Nov 2020 14:32:11 +0100
Subject: [PATCH 08/12] Remove unused patching in test.
---
tests/test_predict.py | 12 ------------
1 file changed, 12 deletions(-)
diff --git a/tests/test_predict.py b/tests/test_predict.py
index 42bc493..2a56bd9 100644
--- a/tests/test_predict.py
+++ b/tests/test_predict.py
@@ -1,8 +1,6 @@
import os
import pathlib
-import shutil
import unittest
-from unittest import mock
import combo.data as data
import combo.predict as predict
@@ -14,16 +12,6 @@ class PredictionTest(unittest.TestCase):
TESTS_ROOT = PROJECT_ROOT / "tests"
FIXTURES_ROOT = TESTS_ROOT / "fixtures"
- def setUp(self) -> None:
- def _cleanup_archive_dir_without_logging(path: str):
- if os.path.exists(path):
- shutil.rmtree(path)
-
- self.patcher = mock.patch(
- "allennlp.models.archival._cleanup_archive_dir", _cleanup_archive_dir_without_logging
- )
- self.mock_cleanup_archive_dir = self.patcher.start()
-
def test_prediction_are_equal_given_the_same_input_in_different_form(self):
# given
raw_sentence = "Test."
--
GitLab
From 828a3ec56a6cd32a406d840ce987c103d3ec763f Mon Sep 17 00:00:00 2001
From: Mateusz Klimaszewski <mk.klimaszewski@gmail.com>
Date: Sat, 7 Nov 2020 19:27:00 +0100
Subject: [PATCH 09/12] Add header for readme.md
---
README.md | 11 +++++++++++
1 file changed, 11 insertions(+)
diff --git a/README.md b/README.md
index 0ef5706..0213823 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,14 @@
+# COMBO
+<p align="center">
+ A GPL-3.0 system, build on top of PyTorch and AllenNLP, for morphosyntactic analysis.
+</p>
+<hr/>
+<p align="center">
+ <a href="https://github.com/ipipan/combo/blob/master/LICENSE">
+ <img alt="License" src="https://img.shields.io/github/license/ipipan/combo.svg?color=blue&cachedrop">
+ </a>
+</p>
+
## Installation
Clone this repository and run:
--
GitLab
From 92b4e5c32ea6979815dbb3dda2b24fe06237fb70 Mon Sep 17 00:00:00 2001
From: Mateusz Klimaszewski <mk.klimaszewski@gmail.com>
Date: Mon, 9 Nov 2020 10:22:28 +0100
Subject: [PATCH 10/12] Add information about pre-trained models.
---
README.md | 8 ++++++++
1 file changed, 8 insertions(+)
diff --git a/README.md b/README.md
index 0213823..87a4657 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,14 @@
</a>
</p>
+[Pre-trained models!](http://mozart.ipipan.waw.pl/~mklimaszewski/models/)
+```python
+import combo.predict as predict
+nlp = predict.SemanticMultitaskPredictor.from_pretrained("polish-herbert-base")
+sentence = nlp("Moje zdanie.")
+print(sentence.tokens)
+```
+
## Installation
Clone this repository and run:
--
GitLab
From 8fba2eddfad66783b8e06b33cef03509e4d7098e Mon Sep 17 00:00:00 2001
From: Mateusz Klimaszewski <mk.klimaszewski@gmail.com>
Date: Mon, 9 Nov 2020 12:59:44 +0100
Subject: [PATCH 11/12] Fix console prediction.
---
combo/main.py | 3 +++
combo/predict.py | 3 +++
2 files changed, 6 insertions(+)
diff --git a/combo/main.py b/combo/main.py
index c7aac87..44ad091 100644
--- a/combo/main.py
+++ b/combo/main.py
@@ -146,6 +146,9 @@ def run(_):
else:
use_dataset_reader = FLAGS.conllu_format
predictor = _get_predictor()
+ if FLAGS.input_file == "-":
+ use_dataset_reader = False
+ predictor.without_sentence_embedding = True
if use_dataset_reader:
predictor.line_to_conllu = True
if FLAGS.silent:
diff --git a/combo/predict.py b/combo/predict.py
index ebbb372..b6c7172 100644
--- a/combo/predict.py
+++ b/combo/predict.py
@@ -32,6 +32,7 @@ class SemanticMultitaskPredictor(predictor.Predictor):
self._dataset_reader.generate_labels = False
self._dataset_reader.lazy = True
self._tokenizer = tokenizer
+ self.without_sentence_embedding = False
self.line_to_conllu = line_to_conllu
def __call__(self, sentence: Union[str, List[str], List[List[str]], List[data.Sentence]]):
@@ -127,6 +128,8 @@ class SemanticMultitaskPredictor(predictor.Predictor):
def dump_line(self, outputs: data.Sentence) -> str:
# Check whether serialized (str) tree or token's list
# Serialized tree has already separators between lines
+ if self.without_sentence_embedding:
+ outputs.sentence_embedding = []
if self.line_to_conllu:
return sentence2conllu(outputs, keep_semrel=self._dataset_reader.use_sem).serialize()
else:
--
GitLab
From aaea8f5512b0dbae513bd2a078448996acd4c03c Mon Sep 17 00:00:00 2001
From: Mateusz Klimaszewski <mk.klimaszewski@gmail.com>
Date: Tue, 10 Nov 2020 14:43:10 +0100
Subject: [PATCH 12/12] Split documentation into multiple markdown files.
---
README.md | 122 ++++++-------------------------------------
docs/installation.md | 13 +++++
docs/models.md | 19 +++++++
docs/prediction.md | 40 ++++++++++++++
docs/training.md | 52 ++++++++++++++++++
5 files changed, 139 insertions(+), 107 deletions(-)
create mode 100644 docs/installation.md
create mode 100644 docs/models.md
create mode 100644 docs/prediction.md
create mode 100644 docs/training.md
diff --git a/README.md b/README.md
index 87a4657..19491b3 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
# COMBO
<p align="center">
- A GPL-3.0 system, build on top of PyTorch and AllenNLP, for morphosyntactic analysis.
+ A language-independent NLP system for dependency parsing, part-of-speech tagging, lemmatisation and more built on top of PyTorch and AllenNLP.
</p>
<hr/>
<p align="center">
@@ -9,118 +9,26 @@
</a>
</p>
-[Pre-trained models!](http://mozart.ipipan.waw.pl/~mklimaszewski/models/)
-```python
-import combo.predict as predict
-nlp = predict.SemanticMultitaskPredictor.from_pretrained("polish-herbert-base")
-sentence = nlp("Moje zdanie.")
-print(sentence.tokens)
-```
-
-## Installation
-
-Clone this repository and run:
+## Quick start
+Clone this repository and install COMBO (we suggest using virtualenv/conda with Python 3.6+):
```bash
+git clone https://github.com/ipipan/combo.git
+cd combo
python setup.py develop
```
-
-### Problems & solutions
-* **jsonnet** installation error
-
-use `conda install -c conda-forge jsonnet=0.15.0`
-
-## Training
-
-Command:
-```bash
-combo --mode train \
- --training_data_path your_training_path \
- --validation_data_path your_validation_path
-```
-
-Options:
-```bash
-combo --helpfull
-```
-
-Examples (for clarity without training/validation data paths):
-
-* train on gpu 0
-
- ```bash
- combo --mode train --cuda_device 0
- ```
-
-* use pretrained embeddings:
-
- ```bash
- combo --mode train --pretrained_tokens your_pretrained_embeddings_path --embedding_dim your_embeddings_dim
- ```
-
-* use pretrained transformer embeddings:
-
- ```bash
- combo --mode train --pretrained_transformer_name your_choosen_pretrained_transformer
- ```
-
-* predict only dependency tree:
-
- ```bash
- combo --mode train --targets head,deprel
- ```
-
-* use part-of-speech tags for predicting only dependency tree
-
- ```bash
- combo --mode train --targets head,deprel --features token,char,upostag
- ```
-
-Advanced configuration: [Configuration](#configuration)
-
-## Prediction
-
-### ConLLU file prediction:
-Input and output are both in `*.conllu` format.
-```bash
-combo --mode predict --model_path your_model_tar_gz --input_file your_conllu_file --output_file your_output_file --silent
-```
-
-### Console
-Works for models where input was text-based only.
-
-Interactive testing in console (load model and just type sentence in console).
-
-```bash
-combo --mode predict --model_path your_model_tar_gz --input_file "-" --nosilent
-```
-### Raw text
-Works for models where input was text-based only.
-
-Input: one sentence per line.
-
-Output: List of token jsons.
-
-```bash
-combo --mode predict --model_path your_model_tar_gz --input_file your_text_file --output_file your_output_file --silent
-```
-#### Advanced
-
-There are 2 tokenizers: whitespace and spacy-based (`en_core_web_sm` model).
-
-Use either `--predictor_name semantic-multitask-predictor` or `--predictor_name semantic-multitask-predictor-spacy`.
-
-### Python
+Run the following lines in your Python console to make predictions with a pre-trained model:
```python
import combo.predict as predict
-model_path = "your_model.tar.gz"
-nlp = predict.SemanticMultitaskPredictor.from_pretrained(model_path)
-sentence = nlp("Sentence to parse.")
+nlp = predict.SemanticMultitaskPredictor.from_pretrained("polish-herbert-base")
+sentence = nlp("Moje zdanie.")
+print(sentence.tokens)
```
-## Configuration
+## Details
+
+- [**Installation**](docs/installation.md)
+- [**Pre-trained models**](docs/models.md)
+- [**Training**](docs/training.md)
+- [**Prediction**](docs/prediction.md)
-### Advanced
-Config template [config.template.jsonnet](config.template.jsonnet) is formed in `allennlp` format so you can freely modify it.
-There is configuration for all the training/model parameters (learning rates, epochs number etc.).
-Some of them use `jsonnet` syntax to get values from configuration flags, however most of them can be modified directly there.
diff --git a/docs/installation.md b/docs/installation.md
new file mode 100644
index 0000000..559c9c7
--- /dev/null
+++ b/docs/installation.md
@@ -0,0 +1,13 @@
+# Installation
+Clone this repository and install COMBO (we suggest using virtualenv/conda with Python 3.6+):
+```bash
+git clone https://github.com/ipipan/combo.git
+cd combo
+python setup.py develop
+combo --helpfull
+```
+
+## Problems & solutions
+* **jsonnet** installation error
+
+use `conda install -c conda-forge jsonnet=0.15.0`
diff --git a/docs/models.md b/docs/models.md
new file mode 100644
index 0000000..485f761
--- /dev/null
+++ b/docs/models.md
@@ -0,0 +1,19 @@
+# Models
+
+Pre-trained models are available [here](http://mozart.ipipan.waw.pl/~mklimaszewski/models/).
+
+## Automatic download
+Python `from_pretrained` method will download the pre-trained model if the provided name (without the extension .tar.gz) matches one of the names in [here](http://mozart.ipipan.waw.pl/~mklimaszewski/models/).
+```python
+import combo.predict as predict
+
+nlp = predict.SemanticMultitaskPredictor.from_pretrained("polish-herbert-base")
+```
+Otherwise it looks for a model in local env.
+
+## Console prediction/Local model
+If you want to use the console version of COMBO, you need to download a pre-trained model manually
+```bash
+wget http://mozart.ipipan.waw.pl/~mklimaszewski/models/polish-herbert-base.tar.gz
+```
+and pass it as a parameter (see [prediction doc](prediction.md)).
diff --git a/docs/prediction.md b/docs/prediction.md
new file mode 100644
index 0000000..89cc74c
--- /dev/null
+++ b/docs/prediction.md
@@ -0,0 +1,40 @@
+# Prediction
+
+## ConLLU file prediction:
+Input and output are both in `*.conllu` format.
+```bash
+combo --mode predict --model_path your_model_tar_gz --input_file your_conllu_file --output_file your_output_file --silent
+```
+
+## Console
+Works for models where input was text-based only.
+
+Interactive testing in console (load model and just type sentence in console).
+
+```bash
+combo --mode predict --model_path your_model_tar_gz --input_file "-" --nosilent
+```
+## Raw text
+Works for models where input was text-based only.
+
+Input: one sentence per line.
+
+Output: List of token jsons.
+
+```bash
+combo --mode predict --model_path your_model_tar_gz --input_file your_text_file --output_file your_output_file --silent --noconllu_format
+```
+### Advanced
+
+There are 2 tokenizers: whitespace and spacy-based (`en_core_web_sm` model).
+
+Use either `--predictor_name semantic-multitask-predictor` or `--predictor_name semantic-multitask-predictor-spacy`.
+
+## Python
+```python
+import combo.predict as predict
+
+model_path = "your_model.tar.gz"
+nlp = predict.SemanticMultitaskPredictor.from_pretrained(model_path)
+sentence = nlp("Sentence to parse.")
+```
diff --git a/docs/training.md b/docs/training.md
new file mode 100644
index 0000000..9dc430a
--- /dev/null
+++ b/docs/training.md
@@ -0,0 +1,52 @@
+# Training
+
+Command:
+```bash
+combo --mode train \
+ --training_data_path your_training_path \
+ --validation_data_path your_validation_path
+```
+
+Options:
+```bash
+combo --helpfull
+```
+
+Examples (for clarity without training/validation data paths):
+
+* train on gpu 0
+
+ ```bash
+ combo --mode train --cuda_device 0
+ ```
+
+* use pretrained embeddings:
+
+ ```bash
+ combo --mode train --pretrained_tokens your_pretrained_embeddings_path --embedding_dim your_embeddings_dim
+ ```
+
+* use pretrained transformer embeddings:
+
+ ```bash
+ combo --mode train --pretrained_transformer_name your_choosen_pretrained_transformer
+ ```
+
+* predict only dependency tree:
+
+ ```bash
+ combo --mode train --targets head,deprel
+ ```
+
+* use part-of-speech tags for predicting only dependency tree
+
+ ```bash
+ combo --mode train --targets head,deprel --features token,char,upostag
+ ```
+
+## Configuration
+
+### Advanced
+Config template [config.template.jsonnet](config.template.jsonnet) is formed in `allennlp` format so you can freely modify it.
+There is configuration for all the training/model parameters (learning rates, epochs number etc.).
+Some of them use `jsonnet` syntax to get values from configuration flags, however most of them can be modified directly there.
\ No newline at end of file
--
GitLab