distilbert-base-uncased-distilled-squad Overview DistilBERT model was proposed in the blog post Smaller, faster, cheaper, lighter: Introducing DistilBERT, adistilled version of BERT, and the paper DistilBERT, adistilled version of BERT: smaller, faster, cheaper and lighter. DistilBERT is a small...
复制过来是可以用的, models--bert-base-uncased文件夹放在~/.cache/huggingface/hub下 6666665 commented Sep 25, 2023 • edited I have solved this problem. You need to start from https://huggingface.co/bert-base-uncased/tree/main Download files to stable differentiation-webui/bert-base-uncased ...
model_name = "bert-base-uncased"tokenizer = BertTokenizer.from_pretrained(model_name)model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2) Since we are using a pretrained model, we need to ensure that the input data is in the same form as what the pretrained model...
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. w...
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...
#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...
To improve the performance of our selected model, we could do a few different things including selecting a model trained on our domain data, movie reviews in this case, like DistilBERT base uncased finetuned SST-2. We could also shift our focus to another flavor of representation models, na...
“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 ...
config_space['model_name_or_path']=choice(['bert-base-cased','bert-base-uncased','distilbert-base-uncased','distilbert-base-cased','roberta-base','albert-base-v2','distilroberta-base','xlnet-base-cased','albert-base-v1']) Although the search space...
'bert-base-uncased' tokenizer = BertTokenizer.from_pretrained(pretrained_weights) for model_class in BERT_MODEL_CLASSES: # Load pretrained model/tokenizer model = model_class.from_pretrained(pretrained_weights) # Models can return full list of hidden-states & attentions weights at each layer model...