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- BERT - Hugging Face
BERT is also very versatile because its learned language representations can be adapted for other NLP tasks by fine-tuning an additional layer or head You can find all the original BERT checkpoints under the BERT collection
- google-bert bert-base-uncased · Hugging Face
BERT has originally been released in base and large variations, for cased and uncased input text The uncased models also strips out an accent markers Chinese and multilingual uncased and cased versions followed shortly after
- neuralmind bert-base-portuguese-cased · Hugging Face
BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity and Recognizing Textual Entailment
- BERT 101 State Of The Art NLP Model Explained - Hugging Face
What is BERT? BERT, short for Bidirectional Encoder Representations from Transformers, is a Machine Learning (ML) model for natural language processing
- BERT - Hugging Face
Hugging Face Transformers with Keras: Fine-tune a non-English BERT for Named Entity Recognition の使用方法に関するブログ投稿。 各単語の最初の単語部分のみを使用した 固有表現認識のための BERT の微調整 のノートブックトークン化中の単語ラベル内。
- BertJapanese - Hugging Face
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens A BERT sequence has the following format: single sequence: [CLS] X [SEP] pair of sequences: [CLS] A [SEP] B [SEP]
- ModernBERT - Hugging Face
ModernBERT is a modernized version of BERT trained on 2T tokens It brings many improvements to the original architecture such as rotary positional embeddings to support sequences of up to 8192 tokens, unpadding to avoid wasting compute on padding tokens, GeGLU layers, and alternating attention
- google-bert bert-base-multilingual-cased - Hugging Face
BERT is a transformers model pretrained on a large corpus of multilingual data in a self-supervised fashion This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts
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