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nlpodyssey / cybertron

Cybertron: the home planet of the Transformers in Go
BSD 2-Clause "Simplified" License
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Inference using cybertron's sentence-transformers/LaBSE model is inconsistent with Python #17

Open wxbool opened 1 year ago

wxbool commented 1 year ago

The results of inference with cybertron's sentence-transformers/LaBSE model are not consistent with Python, is it because I am missing some steps? It runs fine, but the output does not match the one run by Python, resulting in a text similarity result that is very different from Python and inaccurate.

golang code as follows:

key := "That is a happy person"

    zerolog.SetGlobalLevel(zerolog.DebugLevel)

    modelsDir := "."
    modelName := textencoding.DefaultModelMulti
    m, err := tasks.Load[textencoding.Interface](&tasks.Config{
        ModelsDir: modelsDir,
        ModelName: modelName,
    })
    if err != nil {
        panic(err)
    }
    defer tasks.Finalize(m)

    baseCtx := context.Background()
    r1, err := m.Encode(baseCtx, key, int(bert.MeanPooling))
    if err != nil {
        panic(err)
    }
    queryF64 := r1.Vector.Data().F64()
    fmt.Printf("key: %s\n", key)
    fmt.Println(queryF64)

Output results:

go run main.go
{"level":"debug","file":"sentence-transformers\\LaBSE\\config.json","time":"2023-05-24T19:03:49+08:00","message":"model file already exists, skipping download"}
{"level":"debug","file":"sentence-transformers\\LaBSE\\pytorch_model.bin","time":"2023-05-24T19:03:50+08:00","message":"model file already exists, skipping download"}
{"level":"debug","file":"sentence-transformers\\LaBSE\\vocab.txt","time":"2023-05-24T19:03:50+08:00","message":"model file already exists, skipping download"}
{"level":"debug","file":"sentence-transformers\\LaBSE\\tokenizer_config.json","time":"2023-05-24T19:03:50+08:00","message":"model file already exists, skipping download"}
{"level":"info","model":"sentence-transformers\\LaBSE\\spago_model.bin","time":"2023-05-24T19:03:50+08:00","message":"model file already exists, skipping conversion"}
key: That is a happy person
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Python code is as follows:

from sentence_transformers import SentenceTransformer
sentences = ["That is a happy person"]

model = SentenceTransformer('sentence-transformers/LaBSE')
embeddings = model.encode(sentences)
print(embeddings[0])

Output results:

python main.py
No sentence-transformers model found with name sentence-transformers/LaBSE. Creating a new one with MEAN pooling.
[ 1.03259623e+00  6.08770773e-02 -4.57188673e-02  3.07037681e-01
 -3.08238238e-01 -7.12364078e-01 -8.60971451e-01 -3.74696434e-01
  1.11134505e+00 -3.36232185e-01 -1.61597237e-01 -3.04585040e-01
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Awezome commented 1 year ago

+1