A list of NLP(Natural Language Processing) tutorials built on Tensorflow 2.0.
위키독스 딥 러닝을 이용한 자연어 처리 입문 의 자연어 처리 튜토리얼 저장소입니다.
기본적으로 딥 러닝 프레임워크 Tensorflow 2.0+을 사용하고 있습니다.
코드의 기반이 되는 이론적인 설명은 무료 e-book을 참고바랍니다.
e-book : https://wikidocs.net/book/2155
많은 딥 러닝 또는 자연어 처리 입문자들에게 도움이 되기를 바랍니다.
각 실습 파일의 py 파일을 열어보면 Colab 링크를 기재해두었습니다.
해당 링크를 Chrome 브라우저에서 복사 + 붙여넣기 하시면 별도 파이썬 설치 바로 실습이 가능합니다.
model = TFBertForSequenceClassification.from_pretrained("klue/bert-base", num_labels=2, from_pt=True) loss =tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy') model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
glove 모듀을 사용하기 위해 라이브러리 설치 및 임포트 하려고 했는데,
pip install glove_python_binary 이 코드로 해도 실패하고,
pip install glove_python 만 입력해도 실패합니다.
혹시 해당 라이브러리를 설치하고 임포트 할 수 있는 방법이 있을까요?
Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.
If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.
https://github.com/ukairia777/tensorflow-nlp-tutorial/blob/main/02.%20Text%20Preprocessing/2-10.%20text_preprocessing_tools_for_korean_text.py
내 파일 내용이 주제와 다른 거 같습니다. 또한, colab 경로도 다른 거 같습니다.
안녕하세요! 작가님 덕분에 너무 유용하게 자료들 참고하며 학수하고 있습니다. 질문이 하나 있는데요.
18-3. kor_bert_nsmc_tpu class TFBertForSequenceClassification(tf.keras.Model) 로 정의하여 학습시킨
model = TFBertForSequenceClassification("klue/bert-base") model.fit(train_X, train_y, epochs=2, batch_size=64, validation_split=0.2) results = model.evaluate(test_X, test_y, batch_size=1024) 의 'model'을 colab에서 google drive 내에 저장하는 방법을 여쭤봐도 될까요?
감사합니다! :)
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