model.save_pretrained("./models/bert-base-uncased") tokenizer.save_pretrained("./models/bert-base-uncased") ``` ### 步骤 3:构建 API 服务 使用FastAPI 创建一个简单的 API 服务来调用模型。下面是一个示例代码: ```python from fastapi import FastAPI from pydantic import BaseModel from transformers...
选择预训练模型:从Hugging Face的模型库中选择一个适合您需求的预训练模型,例如BERT、GPT-2等。 下载模型:使用Hugging Face的transformers库下载预训练模型。 from transformers import BertModel model = BertModel.from_pretrained('bert-base-uncased') 1. 2. 3. 三、配置LM Studio LM Studio部署模型需要创建一...
使用pipeline载入模型,分类 classifier = pipeline('text-classification', model='distilbert-base-uncased-finetuned-sst-2-english') 对一段文本进行分类 result = classifier("Hugging Face is a great platform for NLP models.") print(result) 四、处理响应数据 调用API后,会得到处理过的数据,正确处理这些响...
model: name: my-bert-model path: /path/to/bert-base-uncased type: transformers environment: CUDA_VISIBLE_DEVICES: "0" 四、修改模型代码以适应LM Studio 确保您的模型代码可以与LM Studio兼容。以下是一个简单的模型加载和推理示例: from transformers import BertTokenizer, BertForSequenceClassification ...
("bert-base-uncased")# huggingface的API中,使用torchscript=True参数可以直接加载TorchScript modelscript_model=BertModel.from_pretrained("bert-base-uncased",torchscript=True)script_tokenizer=BertTokenizer.from_pretrained('bert-base-uncased',torchscript=True)# Tokenizing input texttext="[CLS] Who was ...
在myapp目录中,创建一个名为models的文件夹,用于存储训练好的模型。然后,使用Transformers库来下载一个预训练的模型。例如,我们可以下载BERT模型: from transformers import BertTokenizer, BertForSequenceClassification import torch tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertFor...
embedding = HuggingFaceEmbeddings(model_name="distilbert-base-uncased") 1. 2. 3. SentenceTransformersEmbeddings 简介: 使用SentenceTransformers库中的模型生成文本嵌入,这些模型经过特定任务的微调,如语义相似度和聚类。 适用场景: 文本聚类、信息检索、语义搜索等。
Sentiment and emotion analysis:determine sentiments and emotions from a text (positive, negative, fear, joy...), in many languages. We also have an AI for financial sentiment analysis.We use DistilBERT Base Uncased Finetuned SST-2, DistilBERT Base Uncased Emotion, and Finbert by Prosus AI....
Model namedistilbert-base-uncased-finetuned-sst-2-englishon top. Framework (the red box is ours):transformers Task (the blue box is ours):Text Classification Supported underlying frameworks: PyTorch + TensorFlow Files and Versions tab, which contains model file sizes etc. ...
try await hf.fillMask( inputs: "[MASK] world!", model: "bert-base-uncased" )SummarizationSummarizes longer text into shorter text. Be careful, some models have a maximum length of input.try await hf.summarization( inputs: "The tower is 324 metres (1,063 ft) tall, about the same ...