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{ "name": "code-completion", "version": "0.0.0", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "code-completion", "version": "0.0.0", "dependencies": { "@monaco-editor/react": "^4.5.1", "@xenova/transformers": "^2.4.4", "react": "^18.2.0", ...
transformers.js/examples/code-completion/package-lock.json/0
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// This file (model.js) contains all the logic for loading the model and running predictions. class MyClassificationPipeline { // NOTE: Replace this with your own task and model static task = 'text-classification'; static model = 'Xenova/distilbert-base-uncased-finetuned-sst-2-english'; static instance...
transformers.js/examples/electron/src/model.js/0
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module.exports = { plugins: { tailwindcss: {}, autoprefixer: {}, }, }
transformers.js/examples/next-client/postcss.config.js/0
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{ "name": "esm", "version": "1.0.0", "description": "Server-side inference with Transformers.js (ESM)", "type": "module", "main": "app.js", "keywords": [], "author": "Xenova", "license": "ISC", "dependencies": { "@xenova/transformers": "^2.0.0" } }
transformers.js/examples/node/esm/package.json/0
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{ "name": "remove-background-client", "version": "0.0.0", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "remove-background-client", "version": "0.0.0", "dependencies": { "@xenova/transformers": "^2.15.0" }, "devDependencies": { "vite": "^...
transformers.js/examples/remove-background-client/package-lock.json/0
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@import url('https://fonts.googleapis.com/css2?family=Montserrat&display=swap'); * { box-sizing: border-box; padding: 0; margin: 0; font-family: 'Montserrat', sans-serif; } html { background: radial-gradient(ellipse at center, #1b2735 0%, #090a0f 100%); height: 100%; width: 100%; } body { overflow: h...
transformers.js/examples/semantic-audio-search/style.css/0
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module.exports = { plugins: { tailwindcss: {}, autoprefixer: {}, }, }
transformers.js/examples/semantic-image-search/postcss.config.js/0
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import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.6.0'; // Since we will download the model from the Hugging Face Hub, we can skip the local model check env.allowLocalModels = false; // Reference the elements that we will need const status = document.getElementById('status'); const fi...
transformers.js/examples/vanilla-js/index.js/0
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<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /> <title>Transformers.js | Real-time depth estimation</title> </head> <body> <h1> Real-time depth estimation w/ <a href="https://huggingface.co/onnx-community/depth-a...
transformers.js/examples/webgpu-video-depth-estimation/index.html/0
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function formatBytes(size) { const i = size == 0 ? 0 : Math.floor(Math.log(size) / Math.log(1024)); return +((size / Math.pow(1024, i)).toFixed(2)) * 1 + ['B', 'kB', 'MB', 'GB', 'TB'][i]; } export default function Progress({ text, percentage, total }) { percentage ??= 0; return ( <div className...
transformers.js/examples/webgpu-vlm/src/components/Progress.jsx/0
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<!doctype html> <html lang="en"> <head> <meta charset="UTF-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /> <title>Whisper Timestamped</title> </head> <body> <div id="root"></div> <script type="module" src="/src/main.jsx"></script> </body> </html>
transformers.js/examples/whisper-word-timestamps/index.html/0
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from transformers.convert_slow_tokenizer import Converter from tokenizers import Tokenizer, pre_tokenizers, processors from tokenizers.models import WordPiece class EsmConverter(Converter): def converted(self) -> Tokenizer: vocab = self.original_tokenizer.vocab tokenizer = Tokenizer(WordPiece(voca...
transformers.js/scripts/extra/esm.py/0
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/** * @module generation/configuration_utils */ import { pick } from "../utils/core.js"; /** * Class that holds a configuration for a generation task. */ export class GenerationConfig { // Parameters that control the length of the output /** * The maximum length the generated tokens can have. *...
transformers.js/src/generation/configuration_utils.js/0
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import { ImageProcessor, } from "../../base/image_processors_utils.js"; export class ConvNextImageProcessor extends ImageProcessor { constructor(config) { super(config); /** * Percentage of the image to crop. Only has an effect if this.size < 384. */ // @ts-expect-er...
transformers.js/src/models/convnext/image_processing_convnext.js/0
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import { ImageProcessor, } from "../../base/image_processors_utils.js"; export class JinaCLIPImageProcessor extends ImageProcessor { constructor(config) { // JinaCLIPImageProcessor uses a custom preprocessor_config.json, so we configure it here const { resize_mode, fill_color, interpolation, si...
transformers.js/src/models/jina_clip/image_processing_jina_clip.js/0
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import { Processor } from "../../base/processing_utils.js"; import { AutoImageProcessor } from "../auto/image_processing_auto.js"; import { AutoTokenizer } from "../../tokenizers.js"; export class OwlViTProcessor extends Processor { static tokenizer_class = AutoTokenizer static image_processor_class = AutoImage...
transformers.js/src/models/owlvit/processing_owlvit.js/0
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import { ImageProcessor, } from "../../base/image_processors_utils.js"; export class SiglipImageProcessor extends ImageProcessor { }
transformers.js/src/models/siglip/image_processing_siglip.js/0
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import { createInferenceSession, isONNXProxy } from "../backends/onnx.js"; import { Tensor } from "../utils/tensor.js"; import { apis } from "../env.js"; const IS_WEB_ENV = apis.IS_BROWSER_ENV || apis.IS_WEBWORKER_ENV; /** * Asynchronously creates a wrapper function for running an ONNX inference session. * * @param...
transformers.js/src/ops/registry.js/0
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import { spawnSync } from "child_process"; const MODULE_NAME = "@huggingface/transformers"; const CODE_BODY = ` const model_id = "hf-internal-testing/tiny-random-LlamaForCausalLM"; const generator = await pipeline("text-generation", model_id, { dtype: "fp32" }); const result = await generator("hello", { max_new_token...
transformers.js/tests/bundles.test.js/0
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import { AutoImageProcessor, CLIPFeatureExtractor } from "../../../src/transformers.js"; import { load_cached_image } from "../../asset_cache.js"; import { MAX_PROCESSOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js"; export default () => { // CLIPFeatureExtractor // - tests center crop (do_center_cro...
transformers.js/tests/models/clip/test_image_processing_clip.js/0
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import { AutoImageProcessor, Idefics3ImageProcessor } from "../../../src/transformers.js"; import { load_cached_image } from "../../asset_cache.js"; import { MAX_PROCESSOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js"; export default () => { // Idefics3ImageProcessor // - custom image processing (patc...
transformers.js/tests/models/idefics3/test_image_processing_idefics3.js/0
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import { Wav2Vec2Processor, MoonshineForConditionalGeneration, full, ones } from "../../../src/transformers.js"; import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js"; export default () => { describe("MoonshineForConditionalGeneration", () => { ...
transformers.js/tests/models/moonshine/test_modeling_moonshine.js/0
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import { AutoProcessor, AutoModelForAudioFrameClassification } from "../../../src/transformers.js"; import { MAX_TEST_EXECUTION_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js"; import { compare } from "../../test_utils.js"; export default () => { const models_to_test = ["onnx-community/pyannote-segmentation-3.0"...
transformers.js/tests/models/pyannote/test_modeling_pyannote.js/0
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import { AutoImageProcessor, rand, Tensor, VitPoseImageProcessor } from "../../../src/transformers.js"; import { load_cached_image } from "../../asset_cache.js"; import { MAX_PROCESSOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js"; export default () => { describe("VitPoseImageProcessor", () => { con...
transformers.js/tests/models/vitpose/test_image_processing_vitpose.js/0
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import { pipeline, ImageClassificationPipeline } from "../../src/transformers.js"; import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js"; import { load_cached_image } from "../asset_cache.js"; const PIPELINE_ID = "image-classification"; export defaul...
transformers.js/tests/pipelines/test_pipelines_image_classification.js/0
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import { pipeline, ZeroShotImageClassificationPipeline } from "../../src/transformers.js"; import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js"; import { load_cached_image } from "../asset_cache.js"; const PIPELINE_ID = "zero-shot-image-classificatio...
transformers.js/tests/pipelines/test_pipelines_zero_shot_image_classification.js/0
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# Troubleshooting This is a document explaining how to deal with various issues on Circle-CI. The entries may include actual solutions or pointers to Issues that cover those. ## Circle CI * pytest worker runs out of resident RAM and gets killed by `cgroups`: https://github.com/huggingface/transformers/issues/11408
transformers/.circleci/TROUBLESHOOT.md/0
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CREATE TABLE IF NOT EXISTS benchmarks ( benchmark_id SERIAL PRIMARY KEY, branch VARCHAR(255), commit_id VARCHAR(72), commit_message VARCHAR(70), metadata jsonb, created_at timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC') ); CREATE INDEX IF NOT EXISTS benchmarks_benchmark_...
transformers/benchmark/init_db.sql/0
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FROM python:3.9-slim ENV PYTHONDONTWRITEBYTECODE=1 ARG REF=main USER root RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git ENV UV_PYTHON=/usr/local/bin/python RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptoo...
transformers/docker/torch-jax-light.dockerfile/0
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FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04 LABEL maintainer="Hugging Face" ARG DEBIAN_FRONTEND=noninteractive # Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands) SHELL ["sh", "-lc"] # The following `ARG` are mainly used to specify the versions explicit...
transformers/docker/transformers-quantization-latest-gpu/Dockerfile/0
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# ุจู†ุงุก ู†ู…ุงุฐุฌ ู…ุฎุตุตุฉ ุชู… ุชุตู…ูŠู… ู…ูƒุชุจุฉ ๐Ÿค— Transformers ู„ุชูƒูˆู† ู‚ุงุจู„ุฉ ู„ู„ุชูˆุณูŠุน ุจุณู‡ูˆู„ุฉ. ูƒู„ ู†ู…ูˆุฐุฌ ู…ูุดูู‘ุฑ ุจุงู„ูƒุงู…ู„ ููŠ ู…ุฌู„ุฏ ูุฑุนูŠ ู…ุนูŠู† ุจุงู„ู…ุณุชูˆุฏุนุŒ ุฏูˆู† ุฃูŠ ุชุฌุฑูŠุฏุŒ ู„ุฐู„ูƒ ูŠู…ูƒู†ูƒ ุจุณู‡ูˆู„ุฉ ู†ุณุฎ ู…ู„ู ุงู„ู†ู…ุฐุฌุฉ ูˆุชุนุฏูŠู„ู‡ ูˆูู‚ู‹ุง ู„ุงุญุชูŠุงุฌุงุชูƒ. ุฅุฐุง ูƒู†ุช ุชูู†ุดุฆ ู†ู…ูˆุฐุฌู‹ุง ุฌุฏูŠุฏู‹ุง ุชู…ุงู…ู‹ุงุŒ ูู‚ุฏ ูŠูƒูˆู† ู…ู† ุงู„ุฃุณู‡ู„ ุงู„ุจุฏุก ู…ู† ุงู„ุตูุฑ. ููŠ ู‡ุฐุง ุงู„ุจุฑู†ุงู…ุฌ ุงู„ุชุนู„ูŠู…ูŠุŒ ุณู†ูุฑููŠูƒ ูƒูŠููŠุฉ ูƒ...
transformers/docs/source/ar/custom_models.md/0
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# ุชุญู…ูŠู„ ุงู„ู…ุญูˆู‘ู„ุงุช ุจุงุณุชุฎุฏุงู… ๐Ÿค— PEFT [[open-in-colab]] ุชู‚ู†ูŠุฉ "ุงู„ุชุฏุฑูŠุจ ุงู„ุฏู‚ูŠู‚ ุฐูˆ ุงู„ูƒูุงุกุฉ ุงู„ุจุงุฑุงู…ุชูŠุฑูŠุฉ" (PEFT)](https://huggingface.co/blog/peft) ุชู‚ูˆู… ุจุชุฌู…ูŠุฏ ู…ุนู„ู…ุงุช ุงู„ู†ู…ูˆุฐุฌ ุงู„ู…ูุฏุฑุจ ู…ุณุจู‚ู‹ุง ุฃุซู†ุงุก ุงู„ุถุจุท ุงู„ุฏู‚ูŠู‚ ูˆุชุถูŠู ุนุฏุฏ ุตุบูŠุฑ ู…ู† ุงู„ู…ุนู„ู…ุงุช ุงู„ู‚ุงุจู„ุฉ ู„ู„ุชุฏุฑูŠุจ (ุงู„ู…ุญูˆู„ุงุช) ููˆู‚ู‡. ูŠุชู… ุชุฏุฑูŠุจ ุงู„ู…ุญูˆู‘ู„ุงุช ู„ุชุนู„ู… ู…ุนู„ูˆู…ุงุช ุฎุงุตุฉ ุจุงู„ู…ู‡ุงู…. ูˆู‚ุฏ ุซุจุช...
transformers/docs/source/ar/peft.md/0
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<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
transformers/docs/source/de/autoclass_tutorial.md/0
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# Optimizing inference perf_infer_gpu_many: perf_infer_gpu_one transformers_agents: agents quantization: quantization/overview
transformers/docs/source/en/_redirects.yml/0
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<!--- Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or ...
transformers/docs/source/en/contributing.md/0
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<!--Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
transformers/docs/source/en/main_classes/data_collator.md/0
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<!--Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
transformers/docs/source/en/main_classes/trainer.md/0
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<!--Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
transformers/docs/source/en/model_doc/bert.md/0
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<!--Copyright 2021 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
transformers/docs/source/en/model_doc/canine.md/0
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<!--Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
transformers/docs/source/en/model_doc/cpm.md/0
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<!--Copyright 2024 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
transformers/docs/source/en/model_doc/depth_pro.md/0
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<!--Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
transformers/docs/source/en/model_doc/fsmt.md/0
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<!--Copyright 2023 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed...
transformers/docs/source/en/model_doc/gptsan-japanese.md/0
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transformers/docs/source/en/model_doc/llama.md/0
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transformers/docs/source/en/model_doc/mobilebert.md/0
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transformers/docs/source/es/converting_tensorflow_models.md/0
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<!--โš ๏ธ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer. --> # Uso de un flujo de trabajo para un servidor web <Tip> Crear un motor de inferencia es un tema complejo, y la "mejor" soluciรณn probablemente depend...
transformers/docs/source/es/pipeline_webserver.md/0
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transformers/docs/source/hi/accelerate.md/0
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transformers/docs/source/it/installation.md/0
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transformers/docs/source/ja/fast_tokenizers.md/0
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transformers/docs/source/ja/model_doc/canine.md/0
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transformers/docs/source/ja/model_doc/data2vec.md/0
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transformers/docs/source/ja/tasks/knowledge_distillation_for_image_classification.md/0
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# ๋ชจ๋“ˆ์‹ ํŠธ๋žœ์Šคํฌ๋จธ [[modular-transformers]] `transformers`๋Š” opinionated(์ž๊ธฐ ์˜๊ฒฌ์ด ๊ฐ•ํ•œ) ํ”„๋ ˆ์ž„์›Œํฌ์ด๋ฉฐ, ์šฐ๋ฆฌ์˜ ์ฒ ํ•™์€ ๋‹ค์Œ์˜ [๊ฐœ๋… ๊ฐ€์ด๋“œ](./philosophy)์— ์ •์˜๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ์ฒ ํ•™์˜ ํ•ต์‹ฌ์€ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์˜ [๋‹จ์ผ ๋ชจ๋ธ, ๋‹จ์ผ ํŒŒ์ผ](https://huggingface.co/blog/transformers-design-philosophy) ์ธก๋ฉด์—์„œ ์ž˜ ๋‚˜ํƒ€๋‚ฉ๋‹ˆ๋‹ค. ์ด ๊ตฌ์„ฑ ์š”์†Œ์˜ ๋‹จ์ ์€ ํŒŒ์ผ ๊ฐ„์— ๊ตฌ์„ฑ ์š”์†Œ์˜ ์ƒ์†๊ณผ ์ž„ํฌํŠธ ๊ฐ€๋Šฅ์„ฑ์„ ์ œํ•œํ•œ๋‹ค๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ๋ชจ๋ธ ๊ตฌ์„ฑ ์š”์†Œ๊ฐ€ ์—ฌ๋Ÿฌ ํŒŒ์ผ์— ๊ฑธ์ณ ๋ฐ˜๋ณต...
transformers/docs/source/ko/modular_transformers.md/0
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<!--โš ๏ธ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer. --> # ์›น ์„œ๋ฒ„๋ฅผ ์œ„ํ•œ ํŒŒ์ดํ”„๋ผ์ธ ์‚ฌ์šฉํ•˜๊ธฐ[[using_pipelines_for_a_webserver]] <Tip> ์ถ”๋ก  ์—”์ง„์„ ๋งŒ๋“œ๋Š” ๊ฒƒ์€ ๋ณต์žกํ•œ ์ฃผ์ œ์ด๋ฉฐ, "์ตœ์„ ์˜" ์†”๋ฃจ์…˜์€ ๋ฌธ์ œ ๊ณต๊ฐ„์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์งˆ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์Šต๋‹ˆ๋‹ค. CPU ๋˜๋Š” GPU๋ฅผ ์‚ฌ์šฉํ•˜๋Š”์ง€์— ๋”ฐ๋ผ ๋‹ค๋ฅด๊ณ ...
transformers/docs/source/ko/pipeline_webserver.md/0
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transformers/docs/source/ko/tasks/idefics.md/0
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transformers/docs/source/ko/troubleshooting.md/0
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transformers/docs/source/pt/serialization.md/0
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<!--็‰ˆๆƒ2020ๅนดHuggingFaceๅ›ข้˜Ÿไฟ็•™ๆ‰€ๆœ‰ๆƒๅˆฉใ€‚ ๆ นๆฎApache่ฎธๅฏ่ฏ็ฌฌ2.0็‰ˆ๏ผˆโ€œ่ฎธๅฏ่ฏโ€๏ผ‰่ฎธๅฏ๏ผ›้™ค้ž็ฌฆๅˆ่ฎธๅฏ่ฏ๏ผŒๅฆๅˆ™ๆ‚จไธๅพ—ไฝฟ็”จๆญคๆ–‡ไปถใ€‚ๆ‚จๅฏไปฅๅœจไปฅไธ‹็ฝ‘ๅ€่Žทๅ–่ฎธๅฏ่ฏ็š„ๅ‰ฏๆœฌ๏ผš http://www.apache.org/licenses/LICENSE-2.0 ้™ค้ž้€‚็”จๆณ•ๅพ‹่ฆๆฑ‚ๆˆ–ไนฆ้ขๅŒๆ„๏ผŒๅฆๅˆ™ๆŒ‰โ€œๆŒ‰ๅŽŸๆ ทโ€ๅˆ†ๅ‘็š„่ฝฏไปถ๏ผŒๆ— ่ฎบๆ˜ฏๆ˜Ž็คบ่ฟ˜ๆ˜ฏๆš—็คบ็š„๏ผŒ้ƒฝๆฒกๆœ‰ไปปไฝ•ๆ‹…ไฟๆˆ–ๆกไปถใ€‚่ฏทๅ‚้˜…่ฎธๅฏ่ฏไปฅไบ†่งฃ็‰นๅฎš่ฏญ่จ€ไธ‹็š„ๆƒ้™ๅ’Œ้™ๅˆถใ€‚ โš ๏ธ ่ฏทๆณจๆ„๏ผŒๆœฌๆ–‡ไปถ่™ฝ็„ถไฝฟ็”จMarkdown็ผ–ๅ†™๏ผŒไฝ†ๅŒ…ๅซไบ†็‰นๅฎš็š„่ฏญๆณ•๏ผŒ้€‚็”จไบŽๆˆ‘ไปฌ็š„doc-builder๏ผˆ็ฑปไผผไบŽMDX๏ผ‰๏ผŒๅฏ่ƒฝๆ— ๆณ•ๅœจๆ‚จ็š„MarkdownๆŸฅ็œ‹ๅ™จไธญๆญฃๅธธๆธฒๆŸ“ใ€‚ --> # ๅŸบไบŽBERT่ฟ›่กŒ็š„็›ธๅ…ณ็ ”...
transformers/docs/source/zh/bertology.md/0
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transformers/docs/source/zh/internal/generation_utils.md/0
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transformers/docs/source/zh/main_classes/logging.md/0
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transformers/docs/source/zh/perf_torch_compile.md/0
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transformers/docs/source/zh/torchscript.md/0
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#!/usr/bin/env python3 import json from typing import Iterator, List, Union from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers from tokenizers.implementations.base_tokenizer import BaseTokenizer from tokenizers.models import Unigram from tokenizers.processors import Te...
transformers/examples/flax/language-modeling/t5_tokenizer_model.py/0
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import argparse import glob import logging import os from argparse import Namespace from importlib import import_module import numpy as np import torch from lightning_base import BaseTransformer, add_generic_args, generic_train from seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score from to...
transformers/examples/legacy/pytorch-lightning/run_ner.py/0
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#!/usr/bin/env python # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless...
transformers/examples/legacy/seq2seq/run_eval_search.py/0
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# ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ # This file was automatically generated from examples/modular-transformers/modular_my_new_model.py. # Do NOT edit this file manually as any edits will be overwritten by the generatio...
transformers/examples/modular-transformers/configuration_my_new_model.py/0
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# ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ๐Ÿšจ # This file was automatically generated from examples/modular-transformers/modular_switch_function.py. # Do NOT edit this file manually as any edits will be overwritten by the genera...
transformers/examples/modular-transformers/modeling_switch_function.py/0
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#!/usr/bin/env python # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LI...
transformers/examples/pytorch/image-pretraining/run_mim_no_trainer.py/0
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# coding=utf-8 # Copyright 2021 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless require...
transformers/examples/pytorch/question-answering/trainer_seq2seq_qa.py/0
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#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE...
transformers/examples/pytorch/summarization/run_summarization.py/0
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# Patience-based Early Exit Patience-based Early Exit (PABEE) is a plug-and-play inference method for pretrained language models. We have already implemented it on BERT and ALBERT. Basically, you can make your LM faster and more robust with PABEE. It can even improve the performance of ALBERT on GLUE. The only sacrifi...
transformers/examples/research_projects/bert-loses-patience/README.md/0
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# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Un...
transformers/examples/research_projects/distillation/distiller.py/0
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<p align="center"> <img src="http://sayef.tech:8082/uploads/FSNER-LOGO-2.png" alt="FSNER LOGO"> </p> <p align="center"> Implemented by <a href="https://huggingface.co/sayef"> sayef </a>. </p> ## Overview The FSNER model was proposed in [Example-Based Named Entity Recognition](https://arxiv.org/abs/2008.10570) by ...
transformers/examples/research_projects/fsner/README.md/0
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import json import os from dataclasses import dataclass from functools import partial from typing import Callable import flax.linen as nn import jax import jax.numpy as jnp import joblib import optax import wandb from flax import jax_utils, struct, traverse_util from flax.serialization import from_bytes, to_bytes from...
transformers/examples/research_projects/jax-projects/big_bird/bigbird_flax.py/0
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# Wav2Vec2 Contrastive Loss PreTraining examples The following example showcases how to pretrain a wav2vec2 model using the JAX/Flax backend. Pretraining Wav2Vec2 is rather complex, so it is highly recommended to read the [official paper](https://arxiv.org/abs/2006.11477). JAX/Flax allows you to trace pure functions...
transformers/examples/research_projects/jax-projects/wav2vec2/README.md/0
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# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a cop...
transformers/examples/research_projects/movement-pruning/emmental/configuration_bert_masked.py/0
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