总参数量: 31782912+24*12596224+1049600 = 335141888 实验采用的huggingface的Transformers实现 Bert-Large模型的结构: BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(30522, 1024, padding_idx=0) (position_embeddings): Embedding(512, 1024) (token_type_embeddings): Embedding(2, 102...
具体而言,此模型是一个bert-large-cased模型,在标准CoNLL-2003命名实体识别(https://www.aclweb.org/anthology/W03-0419.pdf)数据集的英文版上进行了微调。 如果要在同一数据集上使用较小的 BERT 模型进行微调,也可以使用基于 NER 的 BERT(https://huggingface.co/dslim/bert-base-NER/) 版本。 本文介绍了如...
sparseml.transformers.train.question_answering \ --output_dir bert_large_uncased-squad \ --model_name_or_path zoo:bert-large-wikipedia_bookcorpus-pruned80.4block_quantized \ --distill_teacher zoo:nlp/question_answering/bert-large/pytorch/huggingface/squad/base-none \ --recipe zoo:nlp/question_ans...
allow flax d923823 Patrick von Platen 3 年前 correct weights a25a4b0 Patrick von Platen 3 年前 add flax model 49121ce Patrick von Platen 3 年前 track msgpcak 0a93963 Patrick von Platen 3 年前 Update code samples & dimensions f16a3c9 huggingface-web 3 年前 Migrate model card from tran...
Usage (HuggingFace Transformers) Fine-tune Configuration library_nametagspipeline_tagwidget transformersfill-mask text shop làm ăn như cái <mask> text hag từ Quảng <mask> kực nét text Set xinh quá, <mask> bèo nhèo
For details about the original model, check out BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, HuggingFace's Transformers: State-of-the-art Natural Language Processing. Tokenization occurs using the BERT tokenizer (see the demo code for impl...
Before fine-tuning, we first converted the pretrained model to the PyTorch version, using the HuggingFace package (version 2.3)62. For fine-tuning, we utilized our established codebase https://github.com/ZhiGroup/pytorch_ehr for the implementation of BERT_only, GRU, bi-GRU, and RETAIN models...
No need to download the entire parameter weight of the model to the local, just by the model name can test any open source large model on the huggingface platform. fromcalflopsimportcalculate_flops_hfbatch_size,max_seq_length=1,128model_name="baichuan-inc/Baichuan-13B-Chat"flops,macs,params...
如果要在同一数据集上使用较小的 BERT 模型进行微调,也可以使用基于 NER 的 BERT(https://huggingface.co/dslim/bert-base-NER/) 版本。 本文介绍了如何使用MindStudio将hugging face上开源的bert_large_NER模型部署到Ascend平台上,并进行数据预处理、推理脚本的开发,在CoNLL-2003命名实体识别数据集上完成推理任务...
如果要在同一数据集上使用较小的 BERT 模型进行微调,也可以使用基于 NER 的 BERT(https://huggingface.co/dslim/bert-base-NER/) 版本。 本文介绍了如何使用MindStudio将hugging face上开源的bert_large_NER模型部署到Ascend平台上,并进行数据预处理、推理脚本的开发,在CoNLL-2003命名实体识别数据集上完成推理任务...