Golang对接Hugging Face模型 教你快速部署AI文本分类

package mainimport (    "fmt"    "log"    "path/filepath"    torch "github.com/wangkuiyi/gotorch"    "github.com/sugarme/tokenizer"    "github.com/sugarme/tokenizer/pretrained"    "github.com/sugarme/tokenizer/util")func main() {    // 1. 加载模型    modelPath := "sentiment_model.pt" // 替换为你的模型路径    module, err := torch.LoadModule(modelPath)    if err != nil {        log.Fatalf("Failed to load model: %v", err)    }    defer module.MustDestroy()    // 2. 加载Tokenizer    modelName := "distilbert-base-uncased-finetuned-sst-2-english" // 替换为你的模型名称    vocabPath, err := util.CachedPath(modelName, pretrained.VocabFile)    if err != nil {        log.Fatalf("Failed to get vocab path: %v", err)    }    mergesPath, err := util.CachedPath(modelName, pretrained.MergesFile)    if err != nil {        log.Fatalf("Failed to get merges path: %v", err)    }    tk, err := tokenizer.NewTokenizerFromFile(vocabPath, mergesPath, true)    if err != nil {        log.Fatalf("Failed to create tokenizer: %v", err)    }    // 3. 文本预处理    text := "This movie is terrible!"    encoded, err := tk.EncodeSingle(text, true)    if err != nil {        log.Fatalf("Failed to encode text: %v", err)    }    inputIds := encoded.Ids    attentionMask := encoded.AttentionMask    // 4. 转换为Tensor    inputTensor := torch.NewTensor(inputIds).MustTo(torch.Int64)    attentionMaskTensor := torch.NewTensor(attentionMask).MustTo(torch.Int64)    inputTensor = inputTensor.MustUnsqueeze(0) // 添加batch维度    attentionMaskTensor = attentionMaskTensor.MustUnsqueeze(0)    // 5. 模型推理    inputs := []torch.IValue{        torch.NewIValue(inputTensor),        torch.NewIValue(attentionMaskTensor),    }    outputs := module.MustForward(inputs)    outputTensor := outputs.ToTensor()    // 6. 后处理    outputTensor = outputTensor.MustSoftmax(1) // 应用Softmax    probabilities := outputTensor.MustData().([]float32)    fmt.Printf("Negative probability: %f\n", probabilities[0])    fmt.Printf("Positive probability: %f\n", probabilities[1])}