ner-api-3.go

package main

import (
    "bytes"
    "encoding/json"
    "fmt"
    "io"
    "io/ioutil"
    "reflect"

    "gopkg.in/jdkato/prose.v2"
)


// ProdigyOutput represents a single entry of Prodigy's JSON Lines output.
//
// `LabeledEntity` is a structure defined by prose that specifies where the
// entities are within the given `Text`.
type ProdigyOutput struct {
    Text   string
    Spans  []prose.LabeledEntity
    Answer string
}

// ReadProdigy reads our JSON Lines file line-by-line, populating a
// slice of `ProdigyOutput` structures.
func ReadProdigy(jsonLines []byte) []ProdigyOutput {
    dec := json.NewDecoder(bytes.NewReader(jsonLines))
    entries := []ProdigyOutput{}
    for {
        ent := ProdigyOutput{}
        err := dec.Decode(&ent)
        if err != nil {
            if err == io.EOF {
                break
            }
            panic(err)
        }
        entries = append(entries, ent)
    }
    return entries
}

// Split divides our human-annotated data set into two groups: one for training
// our model and one for testing it.
//
// We're using an 80-20 split here, although you may want to use a different
// split.
func Split(data []ProdigyOutput) ([]prose.EntityContext, []ProdigyOutput) {
    cutoff := int(float64(len(data)) * 0.8)

    train, test := []prose.EntityContext{}, []ProdigyOutput{}
    for i, entry := range data {
        if i < cutoff {
            train = append(train, prose.EntityContext{
                Text:   entry.Text,
                Spans:  entry.Spans,
                Accept: entry.Answer == "accept"})
        } else {
            test = append(test, entry)
        }
    }

    return train, test
}

func main() {
    data, err := ioutil.ReadFile("reddit_product.jsonl")
    if err != nil {
        panic(err)
    }
    train, test := Split(ReadProdigy(data))

    // Here, we're training a new model named PRODUCT with the training portion
    // of our annotated data.
    //
    // Depending on your hardware, this should take around 1 - 3 minutes.
    model := prose.ModelFromData("PRODUCT", prose.UsingEntities(train))

    // Now, let's test our model:
    correct := 0.0
    for _, entry := range test {
        // Create a document without segmentation, which isn't required for NER.
        doc, err := prose.NewDocument(
            entry.Text,
            prose.WithSegmentation(false),
            prose.UsingModel(model))

        if err != nil {
            panic(err)
        }
        ents := doc.Entities()

        if entry.Answer != "accept" && len(ents) == 0 {
            // If we rejected this entity during annotation, prose shouldn't
            // have labeled it.
            correct++
        } else {
            // Otherwise, we need to verify that we found the correct entities.
            expected := []string{}
            for _, span := range entry.Spans {
                expected = append(expected, entry.Text[span.Start:span.End])
            }
            if reflect.DeepEqual(expected, ents) {
                correct++
            }
        }
    }
    fmt.Printf("Correct (%%): %f\n", correct / float64(len(test)))
    model.Marshal("PRODUCT") // Save the model to disk.
}

Mr Joseph, where is file reddit_product.jsonl ?

I already found it at https://prodi.gy/assets/data/reddit_product.jsonl

Is there way to generate the jsonl file using a set of data,
OR how do we calculate rank,score,input_has etc for the jsonl file. Please suggest .