ImportError: cannot import name 'score' from partially initialized module 'bert_score' (most likely due to a circular import) (/root/bert_score.py) 是因为我把一个文件名命名为了bert_s…
│ > 30 from torchmetrics.functional.text.bert import bert_score # noqa: F401 │ │ 31 from torchmetrics.functional.text.infolm import infolm # noqa: F401 │ │ │ │ D:\stablediff\automatic\venv\lib\site-packages\torchmetrics\functional\text\bert.py:24 in │ ...
bug描述 Describe the Bug Bug: ImportError: cannot import name 'MSRA' from 'paddle.fluid.initializer' (/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/initializer.py) 运行代码: import os import paddleslim.quant a...
# 需要导入模块: from pytorch_pretrained_bert import BertTokenizer [as 别名]# 或者: from pytorch_pretrained_bert.BertTokenizer importfrom_pretrained[as 别名]defeval_semantic_sim_score(instances: List[CFRInstance], bert_model_type="bert-base-uncased"):tokenizer = BertTokenizer.from_pretrained(b...
1 from transformers import pipeline Next, we need to initialize the pipeline for the Masked Language Modeling Task. 1 unmasker = pipeline(task='fill-mask', model='bert-base-uncased') In the above code block, pipeline accepts two arguments. task: Here, we need to provide the task that we...
Next, let’s get a handle on the pre-trained BERT tokenizer: from transformers import AutoTokenizer old_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") To compare the before and after, let’s see how the original BERT tokenizer would split the Greek intro of Wikipedia on...
开发者ID:yyht,项目名称:BERT,代码行数:6,代码来源:example.py 示例6: __init__ ▲点赞 5▼ # 需要导入模块: from tensorflow.contrib import predictor [as 别名]# 或者: from tensorflow.contrib.predictor importfrom_saved_model[as 别名]def__init__(self, conf, **kwargs):self.conf = confforat...
import "mle-js-oracledb"; export async function huggingfaceDemo(apiToken) { if (apiToken === undefined) { throw Error("must provide an API token"); } const payload = { inputs: "The answer to the universe is [MASK]." }; const mod...
Once the deployment completes, you can find the REST endpoint for the model in the endpoints page, which can be used to score the model. You find options to add more deployments, manage traffic and scaling the Endpoints hub. You also use the Test tab on the endpoint page to test the mo...
max(outputs.data, 1)[1].cpu()#选概率最大的作为预测值 train_acc = metrics.accuracy_score(true, predic)#metrics模块计算准确率 dev_acc, dev_loss = evaluate(config, model, dev_iter) if dev_loss < dev_best_loss:#当前验证集损失是否比之前的好 dev_best_loss = dev_loss#更新 torch.save(...