BERT
bert-5022c47f·4 events·first seen 28d agoAliases: BERT
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Finally, a Replacement for BERT: Introducing ModernBERT
Hugging Face introduces ModernBERT, a modernized encoder-only transformer model designed as a successor to BERT. The model incorporates architectural improvements developed since BERT's 2018 release, targeting better performance on downstream NLP tasks. ModernBERT aims to fill the gap for efficient encoder models in retrieval, classification, and other discriminative tasks where decoder-only LLMs are often overkill.
Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia
This Hugging Face blog post describes how to deploy BERT models on AWS Inferentia chips using the Hugging Face Transformers library and Amazon SageMaker. It covers the workflow for compiling models with AWS Neuron SDK and running optimized inference on Inferentia hardware. The post targets practitioners looking to reduce inference costs and latency for transformer-based NLP workloads.
Forgotten Words: Benchmarking NeoBERT for Dementia Detection in Low-Resource Conversational Filipino and English Speech
This paper presents the first NLP-based dementia detection study for Filipino speech, constructing a parallel bilingual dataset of 4,000 DementiaBank-derived transcripts with manual Filipino translations. Five model families are evaluated across monolingual, zero-shot cross-lingual, and bilingual fine-tuning settings. English-trained BERT degrades sharply on Filipino (Macro-F1 = 0.455), but bilingual fine-tuning recovers performance to Macro-F1 = 0.969–0.973 across all transformer models. The key finding is that multilingual clinical NLP performance is driven by linguistic coverage during training rather than model scale or architecture.
Pre-Train BERT with Hugging Face Transformers and Habana Gaudi
This Hugging Face blog post from August 2022 describes how to pre-train a BERT model from scratch using the Hugging Face Transformers library on Habana Gaudi hardware accelerators. It covers the full pipeline including data preparation, tokenizer training, and masked language modeling pretraining. The post serves as both a technical tutorial and a demonstration of Habana Gaudi's viability as an alternative AI training accelerator.