Model 模型选择:BERT-Base uncased,参数量为110M。评测脚本参照SQuAD2.0官方的评测脚本,把验证集当作测试集来验证结果。 超参数的设置如下: 本文的Baseine结果与已公布的结果的比较: NQ数据集的处理中,去除了long answer的验证,只关注SQuAD格式的short answer task。 QuAC中,去除了上下文相关的。ignored all context-...
distilbert-base-uncased bert-base-uncased roberta-large-pytorchmodel.bin 1.3GB roberta-large-openai-detector-pytorchmodel.bin 1.3GB roberta-large-mnli-pytorchmodel.bin 1.3GB roberta-base-pytorchmodel.bin 478.0MB roberta-base-openai-detector-pytorchmodel.bin 477.8MB gpt2-xl-pytorchmodel.zip 1.9GB gp...
BERT is known to be a very good general-purpose model that works well for most language tasks. In our case, we used BERT first to see if generic models could perform well for our task before resorting to domain-specific adaptations. For our experiments, we used the “Bert-base-uncased”...
faster, cheaper and lighter. DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters thanbert-base-uncased, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language understanding bench...
#create endpointendpoint_name="hf-ep-"$(date +%s) model_name="bert-base-uncased" az ml online-endpoint create --name $endpoint_name#create deployment file.cat <<EOF > ./deploy.yml name: demo model: azureml://registries/HuggingFace/models/$model_name/labels/latest endpoint_name: $endpoint...
复制过来是可以用的, models--bert-base-uncased文件夹放在~/.cache/huggingface/hub下 I have solved this problem. You need to start fromhttps://huggingface.co/bert-base-uncased/tree/mainDownload files tostable differentiation-webui/bert-base-uncased(create this folder if it does not exist) ...
The dataset, hyperparameters, and evaluation and software libraries for fine-tuning other LLMs were the same as when fine-tuning NYUTron. The pretrained LLMs were constructed as follows: random-init is a BERT-base uncased model with reset parameters. web-wiki is a BERT-base uncased model. ...
State-of-the-art Transformer architectures scale up the core self-attention mechanism described above in two ways. First, multiple attention heads are assembled in parallel within a given layer (“multi-headed attention”). For example, BERT-base-uncased33, used in most of our analyses, contains...
MNLI, QNLI, RTE natural language inference bert-base-uncased, roberta-base (2) SFT delta 参数的冗余 Decoder-based LMs Encoder-based LMs Decoder-based 和 Encoder-based 的 LM 的 SFT 增量参数都是高度冗余的。 DARE 可以有效去除 90% 的增量参数,而不会显著降低性能。在某些情况下,这个冗余甚至能达到...
“DistilBERT-Base-Uncased-Emotion”, which is “BERTMini”: DistilBERT is constructed during the pre-training phase via knowledge distillation, which decreases the size of a BERT model by 40% while keeping 97% of its language understanding. It is faster and smaller than any other BERT-based ...