Spaces:
Running
Running
File size: 4,280 Bytes
ca97aa9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
import { pipeline, TokenClassificationPipeline } from "../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js";
const PIPELINE_ID = "token-classification";
export default () => {
describe("Token Classification", () => {
const model_id = "hf-internal-testing/tiny-random-BertForTokenClassification";
/** @type {TokenClassificationPipeline} */
let pipe;
beforeAll(async () => {
pipe = await pipeline(PIPELINE_ID, model_id, DEFAULT_MODEL_OPTIONS);
}, MAX_MODEL_LOAD_TIME);
it("should be an instance of TokenClassificationPipeline", () => {
expect(pipe).toBeInstanceOf(TokenClassificationPipeline);
});
describe("batch_size=1", () => {
it(
"default",
async () => {
const output = await pipe("1 2 3");
// TODO: Add start/end to target
const target = [
{
entity: "LABEL_0",
score: 0.5292708,
index: 1,
word: "1",
// 'start': 0, 'end': 1
},
{
entity: "LABEL_0",
score: 0.5353687,
index: 2,
word: "2",
// 'start': 2, 'end': 3
},
{
entity: "LABEL_1",
score: 0.51381934,
index: 3,
word: "3",
// 'start': 4, 'end': 5
},
];
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (ignore_labels set)",
async () => {
const output = await pipe("1 2 3", { ignore_labels: ["LABEL_0"] });
const target = [
{
entity: "LABEL_1",
score: 0.51381934,
index: 3,
word: "3",
// 'start': 4, 'end': 5
},
];
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
});
describe("batch_size>1", () => {
it(
"default",
async () => {
const output = await pipe(["1 2 3", "4 5"]);
const target = [
[
{
entity: "LABEL_0",
score: 0.5292708,
index: 1,
word: "1",
// 'start': 0, 'end': 1
},
{
entity: "LABEL_0",
score: 0.5353687,
index: 2,
word: "2",
// 'start': 2, 'end': 3
},
{
entity: "LABEL_1",
score: 0.51381934,
index: 3,
word: "3",
// 'start': 4, 'end': 5
},
],
[
{
entity: "LABEL_0",
score: 0.5432807,
index: 1,
word: "4",
// 'start': 0, 'end': 1
},
{
entity: "LABEL_1",
score: 0.5007693,
index: 2,
word: "5",
// 'start': 2, 'end': 3
},
],
];
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (ignore_labels set)",
async () => {
const output = await pipe(["1 2 3", "4 5"], { ignore_labels: ["LABEL_0"] });
const target = [
[
{
entity: "LABEL_1",
score: 0.51381934,
index: 3,
word: "3",
// 'start': 4, 'end': 5
},
],
[
{
entity: "LABEL_1",
score: 0.5007693,
index: 2,
word: "5",
// 'start': 2, 'end': 3
},
],
];
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
});
afterAll(async () => {
await pipe.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
};
|