CogKTR is a Knowledge-enhanced Text Representation toolkit for natural language understanding and has the following functions:
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CogKTR provides user-friendly knowledge acquisition interfaces. Users can use our toolkit to enhance the given texts with one click.
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CogKTR supports various knowledge embeddings that can be used directly. And we also implement plenty of knowledge-enhanced methods so researchers can quickly reproduce these models.
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CogKTR supports many built-in NLU tasks to evaluate the effectiveness of knowledge-enhanced methods. In our paradigm, users can easily conduct their research via a pipeline.
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We also release an online demo to show the process of knowledge acquisition and the effect of knowledge enhancement.
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A short introduction video is available here.
This easy-to-use python package has the following features:
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Unified. CogKTR is designed and built on our Unified Knowledge-Enhanced Paradigm, which consists of four stages: knowledge acquisition, knowledge representation, knowledge injection, and knowledge application.
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Knowledgeable. CogKTR integrates multiple knowledge sources, including Wikidata, Wikipedia, ConceptNet, WordNet and CogNet, and implements a series of knowledge-enhanced methods.
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Modular. CogKTR modularizes our proposed paradigm and consists of
Enhancer,Model,CoreandDatamodules, each of which is highly extensible so that researchers can implement new components easily.
# clone CogKTR
git clone https://github.com/CogNLP/CogKTR.git
# install CogKTR
cd cogktr
pip install -e .
pip install -r requirements.txt
import torch.nn as nn
import torch.optim as optim
from cogktr import *
device, output_path = init_cogktr(
device_id=6,
output_path="path/to/store/the/experiments/results",
folder_tag="base_classification",
)
reader = QnliReader(raw_data_path="path/storing/the/raw/dataset")
train_data, dev_data, test_data = reader.read_all()
vocab = reader.read_vocab()
processor = QnliProcessor(plm="bert-base-cased", max_token_len=256, vocab=vocab)
train_dataset = processor.process_train(train_data)
dev_dataset = processor.process_dev(dev_data)
test_dataset = processor.process_test(test_data)
plm = PlmBertModel(pretrained_model_name="bert-base-cased")
model = BaseSentencePairClassificationModel(plm=plm, vocab=vocab)
metric = BaseClassificationMetric(mode="binary")
loss = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.00001)
trainer = Trainer(model,
train_dataset,
dev_data=dev_dataset,
n_epochs=20,
batch_size=50,
loss=loss,
optimizer=optimizer,
scheduler=None,
metrics=metric,
train_sampler=None,
dev_sampler=None,
drop_last=False,
gradient_accumulation_steps=1,
num_workers=5,
print_every=None,
scheduler_steps=None,
validate_steps=100,
save_steps=None,
output_path=output_path,
grad_norm=1,
use_tqdm=True,
device=device,
fp16=False,
fp16_opt_level='O1',
)
trainer.train()
print("end")import torch
import torch.nn as nn
import torch.optim as optim
from cogktr import *
from transformers import BertConfig
from argparse import Namespace
device, output_path = init_cogktr(
device_id=9,
output_path="path/to/store/the/experiments/results",
folder_tag="base_classification_with_knowledge",
)
reader = QnliReader(raw_data_path="path/storing/the/raw/dataset")
train_data, dev_data, test_data = reader.read_all()
vocab = reader.read_vocab()
enhancer = LinguisticsEnhancer(load_ner=False,
load_srl=True,
load_syntax=False,
load_wordnet=False,
cache_path="path/to/cache/the/enhanced_data",
cache_file="cached_file_name",
reprocess=True)
enhanced_train_dict = enhancer.enhance_train(train_data,enhanced_key_1="sentence",enhanced_key_2="question")
enhanced_dev_dict = enhancer.enhance_dev(dev_data,enhanced_key_1="sentence",enhanced_key_2="question")
enhanced_test_dict = enhancer.enhance_test(test_data,enhanced_key_1="sentence",enhanced_key_2="question")
processor = QnliSembertProcessor(plm="bert-base-uncased", max_token_len=256, vocab=vocab,debug=False)
train_dataset = processor.process_train(train_data,enhanced_train_dict)
dev_dataset = processor.process_dev(dev_data,enhanced_dev_dict)
test_dataset = processor.process_test(test_data,enhanced_test_dict)
early_stopping = EarlyStopping(mode="max",patience=3,threshold=0.001,threshold_mode="abs",metric_name="F1")
tag_config = {
"tag_vocab_size":len(vocab["tag_vocab"]),
"hidden_size":10,
"output_dim":10,
"dropout_prob":0.1,
"num_aspect":3
}
tag_config = Namespace(**tag_config)
plm = SembertEncoder.from_pretrained("bert-base-uncased",tag_config=None)
model = SembertForSequenceClassification(
vocab=vocab,
plm=plm,
)
metric = BaseClassificationMetric(mode="binary")
loss = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=2e-5)
trainer = Trainer(model,
train_dataset,
dev_data=dev_dataset,
n_epochs=20,
batch_size=16,
loss=loss,
optimizer=optimizer,
scheduler=None,
metrics=metric,
train_sampler=None,
dev_sampler=None,
drop_last=False,
gradient_accumulation_steps=1,
num_workers=5,
print_every=None,
scheduler_steps=None,
validate_steps=2000, # validation setting
save_steps=None, # when to save model result
output_path=output_path,
grad_norm=1,
use_tqdm=True,
device=device,
callbacks=None,
metric_key=None,
fp16=False,
fp16_opt_level='O1',
)
trainer.train()