Lifting Sequence Length Limitations of NLP Models using Autoencoders
Data Packing Process for MLPERF BERT - Habana Developers
Longformer: The Long-Document Transformer – arXiv Vanity
Transfer Learning NLP|Fine Tune Bert For Text Classification
Main hyperparameters for fine-tuning the BERT model. | Download Scientific Diagram
Introducing Packed BERT for 2x Training Speed-up in Natural Language Processing
Introducing Packed BERT for 2x Training Speed-up in Natural Language Processing
Variable-Length Sequences in TensorFlow Part 1: Optimizing Sequence Padding - Carted Blog
BERT Text Classification for Everyone | KNIME
Classifying long textual documents (up to 25 000 tokens) using BERT | by Sinequa | Medium
Automatic text classification of actionable radiology reports of tinnitus patients using bidirectional encoder representations from transformer (BERT) and in-domain pre-training (IDPT) | BMC Medical Informatics and Decision Making | Full Text
Bidirectional Encoder Representations from Transformers (BERT)
BERT Text Classification for Everyone | KNIME
Max Sequence length. · Issue #8 · HSLCY/ABSA-BERT-pair · GitHub
Comparing Swedish BERT models for text classification with Knime - Redfield
token indices sequence length is longer than the specified maximum sequence length · Issue #1791 · huggingface/transformers · GitHub
what is the max length of the context? · Issue #190 · google-research/bert · GitHub
Results of BERT4TC-S with different sequence lengths on AGnews and DBPedia. | Download Scientific Diagram
Hugging Face on Twitter: "🛠The tokenizers now have a simple and backward compatible API with simple access to the most common use-cases: - no truncation and no padding - truncating to the
Customer Ticket BERT
nlp - How to use Bert for long text classification? - Stack Overflow
Scaling-up BERT Inference on CPU (Part 1)
How to Fine Tune BERT for Text Classification using Transformers in Python - Python Code
