Getting-Started-with-Google-BERT:使用BERT构建和训练最先进的自然语言处理模型
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2021-05-02
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Getting-Started-with-Google-BERT:使用BERT构建和训练最先进的自然语言处理模型 （185个子文件） 
.DS_Store 12KB .DS_Store 6KB .DS_Store 6KB .DS_Store 6KB .DS_Store 6KB .DS_Store 6KB .DS_Store 6KB .DS_Store 6KB .DS_Store 6KB .DS_Store 6KB 3.06. Text classification.ipynb 106KB 3.06. Text classification-checkpoint.ipynb 106KB 9.08. Building a document answering model .ipynb 62KB 9.08. Building a document answering model -checkpoint.ipynb 62KB 7.09.Predicting masked word using BETO -checkpoint.ipynb 45KB 7.09.Predicting masked word using BETO .ipynb 45KB 7.07. Getting representation of French sentence with FlauBERT .ipynb 43KB 7.07. Getting representation of French sentence with FlauBERT -checkpoint.ipynb 43KB 4.05. Exploring the RoBERTa tokenizer .ipynb 40KB 4.05. Exploring the RoBERTa tokenizer -checkpoint.ipynb 40KB 3.03. Generating BERT embedding -checkpoint.ipynb 39KB 3.03. Generating BERT embedding .ipynb 39KB 3.04. Extracting embeddings from all encoder layers of BERT.ipynb 38KB 3.04. Extracting embeddings from all encoder layers of BERT-checkpoint.ipynb 38KB 9.09. Document summarization.ipynb 38KB 9.09. Document summarization-checkpoint.ipynb 38KB 7.01. Understanding multilingual BERT -checkpoint.ipynb 31KB 7.11. Next sentence prediction with BERTje.ipynb 30KB 7.11. Next sentence prediction with BERTje-checkpoint.ipynb 30KB 9.07. Sentiment analysis using Ktrain-checkpoint.ipynb 22KB 9.07. Sentiment analysis using Ktrain.ipynb 22KB 3.06. Text classification -checkpoint.ipynb 15KB 1.04. Understanding Self-attention mechanism.ipynb 10KB 9.10. Computing sentence representation using BERT as service-checkpoint.ipynb 9KB 9.10. Computing sentence representation using BERT as service.ipynb 9KB 6.07. Training the BERTSUM model -checkpoint.ipynb 8KB 6.07. Training the BERTSUM model .ipynb 8KB 3.09. Q&A with finetuned BERT -checkpoint.ipynb 8KB 4.03. Extracting embeddings with ALBERT-checkpoint.ipynb 8KB 9.11. Computing contextual word representation -checkpoint.ipynb 7KB 9.11. Computing contextual word representation .ipynb 7KB 3.02. Extracting embeddings from pre-trained BERT .ipynb 7KB 3.02. Extracting embeddings from pre-trained BERT -checkpoint.ipynb 7KB 1.03. Self-attention mechanism .ipynb 6KB 8.07. Finding a similar sentence with Sentence-BERT .ipynb 6KB 8.07. Finding a similar sentence with Sentence-BERT -checkpoint.ipynb 6KB 9.05. Performing text summarization with BART .ipynb 6KB 9.05. Performing text summarization with BART -checkpoint.ipynb 6KB 3.08. Question-Answering Task -checkpoint.ipynb 6KB 8.03. Exploring sentence-transformers library .ipynb 6KB 8.03. Exploring sentence-transformers library -checkpoint.ipynb 6KB 5.01. Knowledge distillation -checkpoint.ipynb 5KB Transferring knowledge from BERT to Neural Networks-checkpoint.ipynb 5KB 4.12. Performing question-answering with pre-trained
SpanBERT .ipynb 5KB 4.12. Performing question-answering with pre-trained
SpanBERT -checkpoint.ipynb 5KB 1.01. Introduction to transformer-checkpoint.ipynb 4KB 1.01. Introduction to transformer.ipynb 4KB DistilBERT - distilled version of BERT -checkpoint.ipynb 4KB 3.01. Pre-trained BERT model-checkpoint.ipynb 4KB 8.05. Computing sentence similarity -checkpoint.ipynb 4KB 8.05. Computing sentence similarity .ipynb 4KB TinyBERT-checkpoint.ipynb 3KB 3.07. Natural language inference -checkpoint.ipynb 2KB 1.02. Understanding Encoder of transformer.ipynb 2KB 3.10. Named-entity recognition -checkpoint.ipynb 2KB 3.05. Finetuning BERT for downstream tasks-checkpoint.ipynb 1KB packt_logo.jpeg 4KB
book_cover.jpg 419KB 19.jpg 60KB amazon_logo.jpg 5KB README.md 10KB
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