text stringlengths 96 319k | id stringlengths 14 178 | metadata dict |
|---|---|---|
import os
import time
import argparse
from datasets import load_dataset
from tiktoken.load import load_tiktoken_bpe
import tiktoken
from tokenizers import Tokenizer
from huggingface_hub import hf_hub_download
from typing import Tuple, List
from multiprocessing import Process
MODEL_ID = "meta-llama/Meta-Llama-3.1-8B"
D... | tokenizers/bindings/python/benches/test_tiktoken.py/0 | {
"file_path": "tokenizers/bindings/python/benches/test_tiktoken.py",
"repo_id": "tokenizers",
"token_count": 2288
} |
from typing import Dict, Iterator, List, Optional, Tuple, Union
from .. import AddedToken, Tokenizer, decoders, pre_tokenizers, trainers
from ..models import BPE
from ..normalizers import BertNormalizer, Lowercase, Sequence, unicode_normalizer_from_str
from .base_tokenizer import BaseTokenizer
class CharBPETokenizer... | tokenizers/bindings/python/py_src/tokenizers/implementations/char_level_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/implementations/char_level_bpe.py",
"repo_id": "tokenizers",
"token_count": 2509
} |
[project]
name = 'tokenizers'
requires-python = '>=3.9'
authors = [
{ name = 'Nicolas Patry', email = 'patry.nicolas@protonmail.com' },
{ name = 'Anthony Moi', email = 'anthony@huggingface.co' },
]
classifiers = [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Intended Audi... | tokenizers/bindings/python/pyproject.toml/0 | {
"file_path": "tokenizers/bindings/python/pyproject.toml",
"repo_id": "tokenizers",
"token_count": 671
} |
use std::sync::{Arc, RwLock};
use crate::models::PyModel;
use crate::tokenizer::PyAddedToken;
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use serde::{Deserialize, Serialize};
use tk::models::TrainerWrapper;
use tk::Trainer;
use tokenizers as tk;
/// Base class for all trainers
///
/// This class i... | tokenizers/bindings/python/src/trainers.rs/0 | {
"file_path": "tokenizers/bindings/python/src/trainers.rs",
"repo_id": "tokenizers",
"token_count": 17933
} |
import json
import pickle
import pytest
from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.pre_tokenizers import ByteLevel as ByteLevelPreTokenizer
from tokenizers.processors import (
BertProcessing,
ByteLevel,
PostProcessor,
RobertaProcessing,
Sequence,
Templat... | tokenizers/bindings/python/tests/bindings/test_processors.py/0 | {
"file_path": "tokenizers/bindings/python/tests/bindings/test_processors.py",
"repo_id": "tokenizers",
"token_count": 4406
} |
## Requirements
In order to generate the documentation, it is necessary to have a Python environment with the
following:
```python
pip install sphinx sphinx_rtd_theme setuptools_rust
```
It is also necessary to have the `tokenizers` library in this same environment, for Sphinx to
generate all the API Reference and li... | tokenizers/docs/README.md/0 | {
"file_path": "tokenizers/docs/README.md",
"repo_id": "tokenizers",
"token_count": 266
} |
DATA_DIR = data
BENCHMARK_DIR = benches
TESTS_DIR = tests
dir_guard=@mkdir -p $(@D)
SHARED_RESOURCES = $(DATA_DIR)/gpt2-vocab.json $(DATA_DIR)/gpt2-merges.txt $(DATA_DIR)/bert-base-uncased-vocab.txt $(DATA_DIR)/big.txt $(DATA_DIR)/small.txt $(DATA_DIR)/albert-base-v1-tokenizer.json $(DATA_DIR)/llama-3-tokenizer.json... | tokenizers/tokenizers/Makefile/0 | {
"file_path": "tokenizers/tokenizers/Makefile",
"repo_id": "tokenizers",
"token_count": 1019
} |
//! Test suite for the Web and headless browsers.
#![cfg(target_arch = "wasm32")]
extern crate wasm_bindgen_test;
use wasm_bindgen_test::*;
wasm_bindgen_test_configure!(run_in_browser);
#[wasm_bindgen_test]
fn pass() {
assert_eq!(1 + 1, 2);
}
| tokenizers/tokenizers/examples/unstable_wasm/tests/web.rs/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/tests/web.rs",
"repo_id": "tokenizers",
"token_count": 109
} |
use super::model::Unigram;
use serde::{
de::{Error, MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
impl Serialize for Unigram {
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
where
S: Serializer,
{
let mut model ... | tokenizers/tokenizers/src/models/unigram/serialization.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/unigram/serialization.rs",
"repo_id": "tokenizers",
"token_count": 1824
} |
use crate::tokenizer::{NormalizedString, Normalizer, Result};
use crate::utils::macro_rules_attribute;
#[derive(Default, Copy, Clone, Debug)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct NFD;
impl Normalizer for NFD {
fn normalize(&self, normalized: &mut NormalizedString) -> Result<()> {
normalize... | tokenizers/tokenizers/src/normalizers/unicode.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/unicode.rs",
"repo_id": "tokenizers",
"token_count": 1317
} |
pub mod bert;
pub mod roberta;
pub mod sequence;
pub mod template;
// Re-export these as processors
pub use super::pre_tokenizers::byte_level;
use serde::{Deserialize, Serialize};
use crate::pre_tokenizers::byte_level::ByteLevel;
use crate::processors::bert::BertProcessing;
use crate::processors::roberta::RobertaPro... | tokenizers/tokenizers/src/processors/mod.rs/0 | {
"file_path": "tokenizers/tokenizers/src/processors/mod.rs",
"repo_id": "tokenizers",
"token_count": 2147
} |
use crate::tokenizer::pattern::Pattern;
use crate::{Offsets, Result};
use onig::Regex;
use std::error::Error;
#[derive(Debug)]
pub struct SysRegex {
regex: Regex,
}
impl SysRegex {
pub fn find_iter<'r, 't>(&'r self, inside: &'t str) -> onig::FindMatches<'r, 't> {
self.regex.find_iter(inside)
}
... | tokenizers/tokenizers/src/utils/onig.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/onig.rs",
"repo_id": "tokenizers",
"token_count": 571
} |
# The `pipeline` API
Just like the [transformers Python library](https://github.com/huggingface/transformers), Transformers.js provides users with a simple way to leverage the power of transformers. The `pipeline()` function is the easiest and fastest way to use a pretrained model for inference.
<Tip>
For the full ... | transformers.js/docs/source/pipelines.md/0 | {
"file_path": "transformers.js/docs/source/pipelines.md",
"repo_id": "transformers.js",
"token_count": 2914
} |
import { pipeline, env } from '@xenova/transformers';
env.allowLocalModels = false;
/**
* This class uses the Singleton pattern to ensure that only one instance of the pipeline is loaded.
*/
class CodeCompletionPipeline {
static task = 'text-generation';
static model = null;
static instance = null;
... | transformers.js/examples/code-completion/src/worker.js/0 | {
"file_path": "transformers.js/examples/code-completion/src/worker.js",
"repo_id": "transformers.js",
"token_count": 817
} |
module.exports = {
packagerConfig: {},
rebuildConfig: {},
makers: [
{
name: '@electron-forge/maker-squirrel',
config: {},
},
{
name: '@electron-forge/maker-zip',
platforms: ['darwin'],
},
{
name: '@electron-forge/maker-deb',
config: {},
},
{
na... | transformers.js/examples/electron/forge.config.js/0 | {
"file_path": "transformers.js/examples/electron/forge.config.js",
"repo_id": "transformers.js",
"token_count": 192
} |
// content.js - the content scripts which is run in the context of web pages, and has access
// to the DOM and other web APIs.
// Example usage:
// const message = {
// action: 'classify',
// text: 'text to classify',
// }
// chrome.runtime.sendMessage(message, (response) => {
// console.log('received user... | transformers.js/examples/extension/src/content.js/0 | {
"file_path": "transformers.js/examples/extension/src/content.js",
"repo_id": "transformers.js",
"token_count": 107
} |
import {
Florence2ForConditionalGeneration,
AutoProcessor,
AutoTokenizer,
RawImage,
full,
} from '@xenova/transformers';
async function hasFp16() {
try {
const adapter = await navigator.gpu.requestAdapter();
return adapter.features.has('shader-f16');
} catch (e) {
r... | transformers.js/examples/florence2-webgpu/src/worker.js/0 | {
"file_path": "transformers.js/examples/florence2-webgpu/src/worker.js",
"repo_id": "transformers.js",
"token_count": 1609
} |
'use client'
import { useState } from 'react'
export default function Home() {
// Keep track of the classification result and the model loading status.
const [result, setResult] = useState(null);
const [ready, setReady] = useState(null);
const classify = async (text) => {
if (!text) return;
if (read... | transformers.js/examples/next-server/src/app/page.js/0 | {
"file_path": "transformers.js/examples/next-server/src/app/page.js",
"repo_id": "transformers.js",
"token_count": 545
} |
:root {
font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif;
line-height: 1.5;
font-weight: 400;
color: #213547;
background-color: #ffffff;
font-synthesis: none;
text-rendering: optimizeLegibility;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
-webkit-text... | transformers.js/examples/react-translator/src/index.css/0 | {
"file_path": "transformers.js/examples/react-translator/src/index.css",
"repo_id": "transformers.js",
"token_count": 480
} |
import { defineConfig } from 'vite';
export default defineConfig(env => {
const config = {
build: {
target: 'esnext'
}
};
// TODO: Add this back when .wasm files are served locally
// if (env.mode === 'development') {
// // The .wasm files are not correctly served using Vite in development mo... | transformers.js/examples/segment-anything-client/vite.config.js/0 | {
"file_path": "transformers.js/examples/segment-anything-client/vite.config.js",
"repo_id": "transformers.js",
"token_count": 192
} |
@tailwind base;
@tailwind components;
@tailwind utilities;
:root {
font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif;
line-height: 1.5;
font-weight: 400;
color: #213547;
background-color: #ffffff;
font-synthesis: none;
text-rendering: optimizeLegibility;
-webkit-font-smoothing: antia... | transformers.js/examples/text-to-speech-client/src/index.css/0 | {
"file_path": "transformers.js/examples/text-to-speech-client/src/index.css",
"repo_id": "transformers.js",
"token_count": 181
} |
@tailwind base;
@tailwind components;
@tailwind utilities;
:root {
font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif;
line-height: 1.5;
font-weight: 400;
color-scheme: light dark;
color: rgba(255, 255, 255, 0.87);
background-color: #242424;
font-synthesis: none;
text-rendering: opti... | transformers.js/examples/tokenizer-playground/src/index.css/0 | {
"file_path": "transformers.js/examples/tokenizer-playground/src/index.css",
"repo_id": "transformers.js",
"token_count": 306
} |
import {
env,
AutoTokenizer,
Moondream1ForConditionalGeneration,
TextStreamer,
StoppingCriteria,
RawImage,
AutoProcessor,
Tensor,
full,
} from '@xenova/transformers';
const DEVICE = 'webgpu';
const MAX_NEW_TOKENS = 256;
env.backends.onnx.wasm.proxy = DEVICE !== 'webgpu';
async fu... | transformers.js/examples/webgpu-vlm/src/worker.js/0 | {
"file_path": "transformers.js/examples/webgpu-vlm/src/worker.js",
"repo_id": "transformers.js",
"token_count": 2356
} |
import { GITHUB_ISSUE_URL, IMAGE_PROCESSOR_NAME } from '../../utils/constants.js';
import { getModelJSON } from '../../utils/hub.js';
import { ImageProcessor } from '../../base/image_processors_utils.js';
import * as AllImageProcessors from '../image_processors.js';
export class AutoImageProcessor {
/** @type {t... | transformers.js/src/models/auto/image_processing_auto.js/0 | {
"file_path": "transformers.js/src/models/auto/image_processing_auto.js",
"repo_id": "transformers.js",
"token_count": 465
} |
import {
ImageProcessor,
} from "../../base/image_processors_utils.js";
import { ones } from '../../utils/tensor.js';
/**
* @typedef {object} GroundingDinoFeatureExtractorResultProps
* @property {import('../../utils/tensor.js').Tensor} pixel_mask
* @typedef {import('../../base/image_processors_utils.js').Ima... | transformers.js/src/models/grounding_dino/image_processing_grounding_dino.js/0 | {
"file_path": "transformers.js/src/models/grounding_dino/image_processing_grounding_dino.js",
"repo_id": "transformers.js",
"token_count": 385
} |
import { Processor } from "../../base/processing_utils.js";
import { AutoImageProcessor } from "../auto/image_processing_auto.js";
import { AutoTokenizer } from "../../tokenizers.js";
import { RawImage } from "../../utils/image.js";
export class Qwen2VLProcessor extends Processor {
static image_processor_class = A... | transformers.js/src/models/qwen2_vl/processing_qwen2_vl.js/0 | {
"file_path": "transformers.js/src/models/qwen2_vl/processing_qwen2_vl.js",
"repo_id": "transformers.js",
"token_count": 819
} |
/// <reference types="@webgpu/types" />
import { apis } from "../env.js";
import { DEVICE_TYPES } from "./devices.js";
// TODO: Use the adapter from `env.backends.onnx.webgpu.adapter` to check for `shader-f16` support,
// when available in https://github.com/microsoft/onnxruntime/pull/19940.
// For more information,... | transformers.js/src/utils/dtypes.js/0 | {
"file_path": "transformers.js/src/utils/dtypes.js",
"repo_id": "transformers.js",
"token_count": 904
} |
import { BertTokenizer, BertModel, BertForMaskedLM, BertForSequenceClassification, BertForTokenClassification, BertForQuestionAnswering } 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 () ... | transformers.js/tests/models/bert/test_modeling_bert.js/0 | {
"file_path": "transformers.js/tests/models/bert/test_modeling_bert.js",
"repo_id": "transformers.js",
"token_count": 3074
} |
import { DistilBertTokenizer } from "../../../src/tokenizers.js";
import { BASE_TEST_STRINGS, BERT_TEST_STRINGS } from "../test_strings.js";
export const TOKENIZER_CLASS = DistilBertTokenizer;
export const TEST_CONFIG = {
"Xenova/distilbert-base-cased-distilled-squad": {
SIMPLE: {
text: BASE_TEST_STRINGS.S... | transformers.js/tests/models/distilbert/test_tokenization_distilbert.js/0 | {
"file_path": "transformers.js/tests/models/distilbert/test_tokenization_distilbert.js",
"repo_id": "transformers.js",
"token_count": 7676
} |
import { AutoImageProcessor, Swin2SRImageProcessor } 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 () => {
// Swin2SRImageProcessor
// - tests when padding is a number... | transformers.js/tests/models/swin2sr/test_image_processing_swin2sr.js/0 | {
"file_path": "transformers.js/tests/models/swin2sr/test_image_processing_swin2sr.js",
"repo_id": "transformers.js",
"token_count": 599
} |
import { pipeline, TextClassificationPipeline } 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 = "text-classification";
export default () => {
describe("Text Classification", () => {
c... | transformers.js/tests/pipelines/test_pipelines_text_classification.js/0 | {
"file_path": "transformers.js/tests/pipelines/test_pipelines_text_classification.js",
"repo_id": "transformers.js",
"token_count": 1699
} |
import {
// Pipelines
pipeline,
TextGenerationPipeline,
} from "../../src/transformers.js";
import { init } from "../init.js";
import { compare } from "../test_utils.js";
init();
const MAX_MODEL_LOAD_TIME = 10_000; // 10 seconds
const MAX_TEST_EXECUTION_TIME = 10_000; // 10 seconds
const MAX_MODEL_DISPOSE_TIME ... | transformers.js/tests/utils/logits_process.test.js/0 | {
"file_path": "transformers.js/tests/utils/logits_process.test.js",
"repo_id": "transformers.js",
"token_count": 1255
} |
# Benchmarks
You might want to add new benchmarks.
You will need to define a python function named `run_benchmark` in your python file and the file must be located in this `benchmark/` directory.
The expected function signature is the following:
```py
def run_benchmark(logger: Logger, branch: str, commit_id: str, c... | transformers/benchmark/README.md/0 | {
"file_path": "transformers/benchmark/README.md",
"repo_id": "transformers",
"token_count": 936
} |
FROM python:3.9-slim
ENV PYTHONDONTWRITEBYTECODE=1
USER root
RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git
RUN apt-get install -y g++ cmake
ENV UV_PYTHON=/usr/local/bin/python
RUN pip --no-cache-dir install uv && uv venv
RUN uv pip install --no-cache-dir -U pip setuptools albumentations se... | transformers/docker/examples-tf.dockerfile/0 | {
"file_path": "transformers/docker/examples-tf.dockerfile",
"repo_id": "transformers",
"token_count": 222
} |
FROM rocm/dev-ubuntu-22.04:6.2.4
LABEL maintainer="Hugging Face"
ARG DEBIAN_FRONTEND=noninteractive
ARG PYTORCH='2.5.1'
ARG TORCH_VISION='0.20.0'
ARG TORCH_AUDIO='2.5.0'
ARG ROCM='6.2'
RUN apt update && \
apt install -y --no-install-recommends \
libaio-dev \
git \
# These are required to build deepspe... | transformers/docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile",
"repo_id": "transformers",
"token_count": 689
} |
# الوكلاء والأدوات
[[open-in-colab]]
### ما هو الوكيل؟
يمكن للنظم اللغوية الكبيرة (LLMs) التي تم تدريبها على أداء [نمذجة اللغة السببية](./tasks/language_modeling.) التعامل مع مجموعة واسعة من المهام، ولكنها غالبًا ما تواجه صعوبات في المهام الأساسية مثل المنطق والحساب والبحث. وعندما يتم استدعاؤها في مجالات لا تؤدي فيه... | transformers/docs/source/ar/agents.md/0 | {
"file_path": "transformers/docs/source/ar/agents.md",
"repo_id": "transformers",
"token_count": 14313
} |
# تحسين نماذج اللغة الكبيرة من حيث السرعة والذاكرة
[[open-in-colab]]
تحقق نماذج اللغة الكبيرة (LLMs) مثل GPT3/4، [Falcon](https://huggingface.co/tiiuae/falcon-40b)، و [Llama](https://huggingface.co/meta-llama/Llama-2-70b-hf) تقدمًا سريعًا في قدرتها على معالجة المهام التي تركز على الإنسان، مما يجعلها أدوات أساسية في ... | transformers/docs/source/ar/llm_tutorial_optimization.md/0 | {
"file_path": "transformers/docs/source/ar/llm_tutorial_optimization.md",
"repo_id": "transformers",
"token_count": 37741
} |
# Trainer
تُتيح وحدة [`Trainer`] حلقة تدريب وتقييم متكاملة لنماذج PyTorch المطبقة في مكتبة Transformers. تحتاج فقط إلى تمرير المكونات الضرورية للتدريب (النموذج، والمجزىء النصى، ومجموعة البيانات، دالة التقييم، معلمات التدريب الفائقة، إلخ)، وستتولى فئة [`Trainer`] الباقي. هذا يُسهّل بدء التدريب بشكل أسرع دون كتابة حلقة ... | transformers/docs/source/ar/trainer.md/0 | {
"file_path": "transformers/docs/source/ar/trainer.md",
"repo_id": "transformers",
"token_count": 17735
} |
<!---
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/de/pr_checks.md/0 | {
"file_path": "transformers/docs/source/de/pr_checks.md",
"repo_id": "transformers",
"token_count": 4986
} |
<!--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/en/autoclass_tutorial.md/0 | {
"file_path": "transformers/docs/source/en/autoclass_tutorial.md",
"repo_id": "transformers",
"token_count": 2654
} |
<!--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 to... | transformers/docs/source/en/llm_tutorial_optimization.md/0 | {
"file_path": "transformers/docs/source/en/llm_tutorial_optimization.md",
"repo_id": "transformers",
"token_count": 14805
} |
<!--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/pipelines.md/0 | {
"file_path": "transformers/docs/source/en/main_classes/pipelines.md",
"repo_id": "transformers",
"token_count": 4683
} |
<!--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/barthez.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/barthez.md",
"repo_id": "transformers",
"token_count": 818
} |
<!--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/bort.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/bort.md",
"repo_id": "transformers",
"token_count": 867
} |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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<!---
Copyright 2020 The HuggingFace Team. All rights reserved.
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<!---
Copyright 2020 The HuggingFace Team. Tutti i diritti riservati.
Concesso in licenza in base alla Licenza Apache, Versione 2.0 (la "Licenza");
non è possibile utilizzare questo file se non in conformità con la Licenza.
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<!---
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<!---
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- sections:
- local: index
title: 🤗 Transformers
- local: quicktour
title: Lawatan cepat
- local: installation
title: Pemasangan
title: Mulakan
- sections:
- local: pipeline_tutorial
title: Jalankan inferens dengan saluran paip
- local: autoclass_tutorial
title: Tuli... | transformers/docs/source/ms/_toctree.yml/0 | {
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# 模型
基类 [`PreTrainedModel`]、[`TFPreTrainedModel`] 和 [`FlaxPreTrainedModel`] 实现了从本地文件或目录加载... | transformers/docs/source/zh/main_classes/model.md/0 | {
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<!---
Copyright 2021 The Google Flax Team Authors and 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 r... | transformers/examples/flax/question-answering/README.md/0 | {
"file_path": "transformers/examples/flax/question-answering/README.md",
"repo_id": "transformers",
"token_count": 1053
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#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 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/flax/token-classification/run_flax_ner.py/0 | {
"file_path": "transformers/examples/flax/token-classification/run_flax_ner.py",
"repo_id": "transformers",
"token_count": 14961
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#!/usr/bin/env bash
# for seqeval metrics import
pip install -r ../requirements.txt
## The relevant files are currently on a shared Google
## drive at https://drive.google.com/drive/folders/1kC0I2UGl2ltrluI9NqDjaQJGw5iliw_J
## Monitor for changes and eventually migrate to use the `datasets` library
curl -L 'https://d... | transformers/examples/legacy/pytorch-lightning/run_ner.sh/0 | {
"file_path": "transformers/examples/legacy/pytorch-lightning/run_ner.sh",
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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 applicabl... | transformers/examples/legacy/seq2seq/finetune_tpu.sh/0 | {
"file_path": "transformers/examples/legacy/seq2seq/finetune_tpu.sh",
"repo_id": "transformers",
"token_count": 322
} |
#!/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/save_len_file.py/0 | {
"file_path": "transformers/examples/legacy/seq2seq/save_len_file.py",
"repo_id": "transformers",
"token_count": 869
} |
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from examples/modular-transformers/modular_my_new_model2.py.
# Do NOT edit this file manually as any edits will be overwritten by the generati... | transformers/examples/modular-transformers/configuration_my_new_model2.py/0 | {
"file_path": "transformers/examples/modular-transformers/configuration_my_new_model2.py",
"repo_id": "transformers",
"token_count": 2258
} |
# Note that zamba does not have the `apply_rotary_pos_emb` function!
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
from transformers.models.zamba.modeling_zamba import ZambaAttention
# When following ZambaAttention dependencies, the function `apply_rotary_pos_emb` is not present
# by defau... | transformers/examples/modular-transformers/modular_add_function.py/0 | {
"file_path": "transformers/examples/modular-transformers/modular_add_function.py",
"repo_id": "transformers",
"token_count": 222
} |
<!---
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 ... | transformers/examples/pytorch/audio-classification/README.md/0 | {
"file_path": "transformers/examples/pytorch/audio-classification/README.md",
"repo_id": "transformers",
"token_count": 2210
} |
<!---
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 ... | transformers/examples/pytorch/instance-segmentation/README.md/0 | {
"file_path": "transformers/examples/pytorch/instance-segmentation/README.md",
"repo_id": "transformers",
"token_count": 3502
} |
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