text stringlengths 7 318k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 439 |
|---|---|---|---|
# (Gluon) ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residu... | pytorch-image-models/docs/models/.templates/models/gloun-resnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/gloun-resnet.md",
"repo_id": "pytorch-image-models",
"token_count": 6383
} | 170 |
# MobileNet v3
**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-block) modules in... | pytorch-image-models/docs/models/.templates/models/mobilenet-v3.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/mobilenet-v3.md",
"repo_id": "pytorch-image-models",
"token_count": 1755
} | 171 |
# SK-ResNet
**SK ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNet are replaced by the proposed [SK convo... | pytorch-image-models/docs/models/.templates/models/skresnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/skresnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1276
} | 172 |
# Xception
**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution layers](https://paperswithcode.com/method/depthwise-separable-convolution).
The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
{% include ... | pytorch-image-models/docs/models/.templates/models/xception.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/xception.md",
"repo_id": "pytorch-image-models",
"token_count": 1874
} | 173 |
# CSP-ResNet
**CSPResNet** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNet](https://paperswithcode.com/method/resnet). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a ... | pytorch-image-models/hfdocs/source/models/csp-resnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/csp-resnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 1706
} | 174 |
# RegNetX
**RegNetX** is a convolutional network design space with simple, regular models with parameters: depth \\( d \\), initial width \\( w\_{0} > 0 \\), and slope \\( w\_{a} > 0 \\), and generates a different block width \\( u\_{j} \\) for each block \\( j < d \\). The key restriction for the RegNet types of mode... | pytorch-image-models/hfdocs/source/models/regnetx.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/regnetx.mdx",
"repo_id": "pytorch-image-models",
"token_count": 6574
} | 175 |
# SWSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual ... | pytorch-image-models/hfdocs/source/models/swsl-resnet.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/swsl-resnet.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2442
} | 176 |
# Results
CSV files containing an ImageNet-1K and out-of-distribution (OOD) test set validation results for all models with pretrained weights is located in the repository [results folder](https://github.com/rwightman/pytorch-image-models/tree/master/results).
## Self-trained Weights
The table below includes ImageNe... | pytorch-image-models/hfdocs/source/results.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/results.mdx",
"repo_id": "pytorch-image-models",
"token_count": 2259
} | 177 |
import logging
from .constants import *
_logger = logging.getLogger(__name__)
def resolve_data_config(
args=None,
pretrained_cfg=None,
model=None,
use_test_size=False,
verbose=False
):
assert model or args or pretrained_cfg, "At least one of model, args, or pretrained_cfg... | pytorch-image-models/timm/data/config.py/0 | {
"file_path": "pytorch-image-models/timm/data/config.py",
"repo_id": "pytorch-image-models",
"token_count": 1927
} | 178 |
""" Dataset reader for HF IterableDataset
"""
import math
import os
from itertools import repeat, chain
from typing import Optional
import torch
import torch.distributed as dist
from PIL import Image
try:
import datasets
from datasets.distributed import split_dataset_by_node
from datasets.splits import Sp... | pytorch-image-models/timm/data/readers/reader_hfids.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_hfids.py",
"repo_id": "pytorch-image-models",
"token_count": 3722
} | 179 |
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import use_fused_attn
from .mlp import Mlp
from .weight_init import trunc_normal_tf_
class AttentionPoolLatent(nn.Module):
""" Attention pooling w/ latent query
"""
fused_attn: torch.jit.Final[boo... | pytorch-image-models/timm/layers/attention_pool.py/0 | {
"file_path": "pytorch-image-models/timm/layers/attention_pool.py",
"repo_id": "pytorch-image-models",
"token_count": 1758
} | 180 |
"""
ECA module from ECAnet
paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
https://arxiv.org/abs/1910.03151
Original ECA model borrowed from https://github.com/BangguWu/ECANet
Modified circular ECA implementation and adaption for use in timm package
by Chris Ha https://github.com/V... | pytorch-image-models/timm/layers/eca.py/0 | {
"file_path": "pytorch-image-models/timm/layers/eca.py",
"repo_id": "pytorch-image-models",
"token_count": 2411
} | 181 |
""" PyTorch Mixed Convolution
Paper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595)
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from .conv2d_same import create_conv2d_pad
def _split_channels(num_chan, num_groups):
split = [nu... | pytorch-image-models/timm/layers/mixed_conv2d.py/0 | {
"file_path": "pytorch-image-models/timm/layers/mixed_conv2d.py",
"repo_id": "pytorch-image-models",
"token_count": 834
} | 182 |
""" Split Attention Conv2d (for ResNeSt Models)
Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl at https://github.com/zhanghang1989/ResNeSt
Modified for torchscript compat, performance, and consistency with timm by Ross Wightman
"""
import torch
impor... | pytorch-image-models/timm/layers/split_attn.py/0 | {
"file_path": "pytorch-image-models/timm/layers/split_attn.py",
"repo_id": "pytorch-image-models",
"token_count": 1533
} | 183 |
""" EfficientNet, MobileNetV3, etc Builder
Assembles EfficieNet and related network feature blocks from string definitions.
Handles stride, dilation calculations, and selects feature extraction points.
Hacked together by / Copyright 2019, Ross Wightman
"""
import logging
import math
import re
from copy import deepco... | pytorch-image-models/timm/models/_efficientnet_builder.py/0 | {
"file_path": "pytorch-image-models/timm/models/_efficientnet_builder.py",
"repo_id": "pytorch-image-models",
"token_count": 9013
} | 184 |
""" Bring-Your-Own-Attention Network
A flexible network w/ dataclass based config for stacking NN blocks including
self-attention (or similar) layers.
Currently used to implement experimental variants of:
* Bottleneck Transformers
* Lambda ResNets
* HaloNets
Consider all of the models definitions here as exper... | pytorch-image-models/timm/models/byoanet.py/0 | {
"file_path": "pytorch-image-models/timm/models/byoanet.py",
"repo_id": "pytorch-image-models",
"token_count": 9703
} | 185 |
""" EfficientFormer-V2
@article{
li2022rethinking,
title={Rethinking Vision Transformers for MobileNet Size and Speed},
author={Li, Yanyu and Hu, Ju and Wen, Yang and Evangelidis, Georgios and Salahi, Kamyar and Wang, Yanzhi and Tulyakov, Sergey and Ren, Jian},
journal={arXiv preprint arXiv:2212.08059}... | pytorch-image-models/timm/models/efficientformer_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/efficientformer_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 12721
} | 186 |
"""
InceptionNeXt paper: https://arxiv.org/abs/2303.16900
Original implementation & weights from: https://github.com/sail-sg/inceptionnext
"""
from functools import partial
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import trunc_normal_, Drop... | pytorch-image-models/timm/models/inception_next.py/0 | {
"file_path": "pytorch-image-models/timm/models/inception_next.py",
"repo_id": "pytorch-image-models",
"token_count": 7709
} | 187 |
"""
pnasnet5large implementation grabbed from Cadene's pretrained models
Additional credit to https://github.com/creafz
https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
"""
from collections import OrderedDict
from functools import partial
import torch
import torch... | pytorch-image-models/timm/models/pnasnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/pnasnet.py",
"repo_id": "pytorch-image-models",
"token_count": 7653
} | 188 |
""" Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
- https://arxiv.org/abs/2111.09883
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2022, ... | pytorch-image-models/timm/models/swin_transformer_v2.py/0 | {
"file_path": "pytorch-image-models/timm/models/swin_transformer_v2.py",
"repo_id": "pytorch-image-models",
"token_count": 16762
} | 189 |
""" Cross-Covariance Image Transformer (XCiT) in PyTorch
Paper:
- https://arxiv.org/abs/2106.09681
Same as the official implementation, with some minor adaptations, original copyright below
- https://github.com/facebookresearch/xcit/blob/master/xcit.py
Modifications and additions for timm hacked together by ... | pytorch-image-models/timm/models/xcit.py/0 | {
"file_path": "pytorch-image-models/timm/models/xcit.py",
"repo_id": "pytorch-image-models",
"token_count": 18692
} | 190 |
""" Optimizer Factory w/ Custom Weight Decay
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
from itertools import islice
from typing import Optional, Callable, Tuple
import torch
import torch.nn as nn
import torch.optim as optim
from timm.models import group_parameters
from .adabelief import Ad... | pytorch-image-models/timm/optim/optim_factory.py/0 | {
"file_path": "pytorch-image-models/timm/optim/optim_factory.py",
"repo_id": "pytorch-image-models",
"token_count": 6927
} | 191 |
""" Checkpoint Saver
Track top-n training checkpoints and maintain recovery checkpoints on specified intervals.
Hacked together by / Copyright 2020 Ross Wightman
"""
import glob
import operator
import os
import logging
import torch
from .model import unwrap_model, get_state_dict
_logger = logging.getLogger(__nam... | pytorch-image-models/timm/utils/checkpoint_saver.py/0 | {
"file_path": "pytorch-image-models/timm/utils/checkpoint_saver.py",
"repo_id": "pytorch-image-models",
"token_count": 2818
} | 192 |
#!/usr/bin/env python3
""" ImageNet Validation Script
This is intended to be a lean and easily modifiable ImageNet validation script for evaluating pretrained
models or training checkpoints against ImageNet or similarly organized image datasets. It prioritizes
canonical PyTorch, standard Python style, and good perform... | pytorch-image-models/validate.py/0 | {
"file_path": "pytorch-image-models/validate.py",
"repo_id": "pytorch-image-models",
"token_count": 9310
} | 193 |
<div align="center">
<a href="https://www.youtube.com/watch?v=jlMAX2Oaht0">
<img width=560 width=315 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png">
</a>
# Text Generation Inference
<a href="https://github.com/huggingface/text-generation-inf... | text-generation-inference/README.md/0 | {
"file_path": "text-generation-inference/README.md",
"repo_id": "text-generation-inference",
"token_count": 3286
} | 194 |
[tool.poetry]
name = "text-generation"
version = "0.6.1"
description = "Hugging Face Text Generation Python Client"
license = "Apache-2.0"
authors = ["Olivier Dehaene <olivier@huggingface.co>"]
maintainers = ["Olivier Dehaene <olivier@huggingface.co>"]
readme = "README.md"
homepage = "https://github.com/huggingface/tex... | text-generation-inference/clients/python/pyproject.toml/0 | {
"file_path": "text-generation-inference/clients/python/pyproject.toml",
"repo_id": "text-generation-inference",
"token_count": 336
} | 195 |
# Text-generation-launcher arguments
<!-- WRAP CODE BLOCKS -->
```shell
Text Generation Launcher
Usage: text-generation-launcher [OPTIONS]
Options:
```
## MODEL_ID
```shell
--model-id <MODEL_ID>
The name of the model to load. Can be a MODEL_ID as listed on <https://hf.co/models> like `gpt2` or `Open... | text-generation-inference/docs/source/basic_tutorials/launcher.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/launcher.md",
"repo_id": "text-generation-inference",
"token_count": 5833
} | 196 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 17934,
"logprob": null,
"text": "Pour"
},
{
"id": 49833,
"logprob": -10.5625,
"text": " dég"
},
{
"id"... | text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_bloom_560m/test_bloom_560m.json",
"repo_id": "text-generation-inference",
"token_count": 1544
} | 197 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -9.59375,
"text": "Test"
},
{
"id": 2009... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama_gptq/test_flash_llama_gptq.json",
"repo_id": "text-generation-inference",
"token_count": 1036
} | 198 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 563,
"logprob": null,
"text": "def"
},
{
"id": 942,
"logprob": -5.1367188,
"text": " print"
},
{
"id":... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_santacoder/test_flash_santacoder.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_santacoder/test_flash_santacoder.json",
"repo_id": "text-generation-inference",
"token_count": 1111
} | 199 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 50278,
"logprob": null,
"text": "<|USER|>"
},
{
"id": 1276,
"logprob": -4.5546875,
"text":... | text-generation-inference/integration-tests/models/__snapshots__/test_neox/test_neox_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_neox/test_neox_load.json",
"repo_id": "text-generation-inference",
"token_count": 6296
} | 200 |
import pytest
@pytest.fixture(scope="module")
def flash_phi_handle(launcher):
with launcher("microsoft/phi-2", num_shard=1) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_phi(flash_phi_handle):
await flash_phi_handle.health(300)
return flash_phi_handle.client
@pytest.m... | text-generation-inference/integration-tests/models/test_flash_phi.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_phi.py",
"repo_id": "text-generation-inference",
"token_count": 749
} | 201 |
use std::fmt;
use std::process::Command;
pub(crate) struct Env {
cargo_target: &'static str,
cargo_version: &'static str,
git_sha: &'static str,
docker_label: &'static str,
nvidia_env: String,
}
impl Env {
pub fn new() -> Self {
let nvidia_env = nvidia_smi();
Self {
... | text-generation-inference/launcher/src/env_runtime.rs/0 | {
"file_path": "text-generation-inference/launcher/src/env_runtime.rs",
"repo_id": "text-generation-inference",
"token_count": 650
} | 202 |
[package]
name = "grpc-metadata"
version = "0.1.0"
edition = "2021"
[dependencies]
opentelemetry = "^0.20"
tonic = "^0.10"
tracing = "^0.1"
tracing-opentelemetry = "^0.21"
| text-generation-inference/router/grpc-metadata/Cargo.toml/0 | {
"file_path": "text-generation-inference/router/grpc-metadata/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 83
} | 203 |
flash_att_v2_commit_cuda := 02ac572f3ffc4f402e4183aaa6824b45859d3ed3
flash_att_v2_commit_rocm := 8736558c287ff2ef28b24878e42828c595ac3e69
flash-attention-v2-cuda:
# Clone flash attention
pip install -U packaging ninja --no-cache-dir
git clone https://github.com/HazyResearch/flash-attention.git flash-attention-v2... | text-generation-inference/server/Makefile-flash-att-v2/0 | {
"file_path": "text-generation-inference/server/Makefile-flash-att-v2",
"repo_id": "text-generation-inference",
"token_count": 496
} | 204 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _hip_compat_cuh
#define _hip_compat_cuh
// Workaround for a bug in hipamd, backported from upstream, this is fixed in ROCm 5.6.
__device__ __forceinline__ __half __compat_hrcp(__half x) {
return __half_raw{
static_cast<_Float1... | text-generation-inference/server/exllama_kernels/exllama_kernels/hip_compat.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/hip_compat.cuh",
"repo_id": "text-generation-inference",
"token_count": 1707
} | 205 |
#ifndef _qdq_3_cuh
#define _qdq_3_cuh
#include "qdq_util.cuh"
#include "../../config.h"
#if QMODE_3BIT == 1
// Permutation:
//
// v9997775 55333111 u8886664 44222000 (u, v lsb)
// vjjjhhhf ffdddbbb uiiiggge eecccaaa
// vtttrrrp ppnnnlll usssqqqo oommmkkk
__forceinline__ __device__ void shuffle_3bit_32
(
uin... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_3.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/quant/qdq_3.cuh",
"repo_id": "text-generation-inference",
"token_count": 3335
} | 206 |
import pytest
import torch
from copy import copy
from transformers import AutoTokenizer
from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLM, CausalLMBatch
@pytest.fixture(scope="session")
def default_causal_lm():
return CausalLM("gpt2")
@pytest.fixtu... | text-generation-inference/server/tests/models/test_causal_lm.py/0 | {
"file_path": "text-generation-inference/server/tests/models/test_causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 5345
} | 207 |
import torch
import time
from dataclasses import dataclass
from opentelemetry import trace
from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.models import Model
from text_generation_server.utils.tokens... | text-generation-inference/server/text_generation_server/models/causal_lm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 14874
} | 208 |
"""A simple, flexible implementation of a GPT model.
Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
"""
import math
import os
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTraine... | text-generation-inference/server/text_generation_server/models/custom_modeling/mpt_modeling.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/mpt_modeling.py",
"repo_id": "text-generation-inference",
"token_count": 23558
} | 209 |
import torch
import time
from dataclasses import dataclass
from opentelemetry import trace
from transformers import (
AutoProcessor,
AutoTokenizer,
PreTrainedTokenizerBase,
ProcessorMixin,
)
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.models import Model
from text_... | text-generation-inference/server/text_generation_server/models/idefics_causal_lm.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/idefics_causal_lm.py",
"repo_id": "text-generation-inference",
"token_count": 16143
} | 210 |
import os
import torch
from datetime import timedelta
from loguru import logger
# Tensor Parallelism settings
RANK = int(os.getenv("RANK", "0"))
WORLD_SIZE = int(os.getenv("WORLD_SIZE", "1"))
# CUDA memory fraction
MEMORY_FRACTION = float(os.getenv("CUDA_MEMORY_FRACTION", "1.0"))
class FakeBarrier:
def wait(se... | text-generation-inference/server/text_generation_server/utils/dist.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/dist.py",
"repo_id": "text-generation-inference",
"token_count": 1042
} | 211 |
import re
from typing import Callable, List, Optional, Tuple
import torch
from text_generation_server.pb import generate_pb2
from text_generation_server.pb.generate_pb2 import FinishReason
from text_generation_server.utils.logits_process import (
HeterogeneousProcessorWrapper,
HeterogeneousRepetitionPenaltyLog... | text-generation-inference/server/text_generation_server/utils/tokens.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/tokens.py",
"repo_id": "text-generation-inference",
"token_count": 8706
} | 212 |
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: HuggingFace's Tokenizers
message: >-
Fast State-of-the-Art Tokenizers optimized for Research
and Production.
type: software
authors:
- given-names: Anthony
family-names: Moi
... | tokenizers/CITATION.cff/0 | {
"file_path": "tokenizers/CITATION.cff",
"repo_id": "tokenizers",
"token_count": 293
} | 213 |
<p align="center">
<br>
<img src="https://huggingface.co/landing/assets/tokenizers/tokenizers-logo.png" width="600"/>
<br>
<p>
<p align="center">
<a href="https://badge.fury.io/js/tokenizers">
<img alt="Build" src="https://badge.fury.io/js/tokenizers.svg">
</a>
<a href="https://github.com/huggingface/to... | tokenizers/bindings/node/README.md/0 | {
"file_path": "tokenizers/bindings/node/README.md",
"repo_id": "tokenizers",
"token_count": 651
} | 214 |
/* eslint-disable @typescript-eslint/no-explicit-any */
/* eslint-disable @typescript-eslint/no-empty-function */
import { TruncationStrategy, BPE, Encoding, AddedToken, Tokenizer } from '../../'
// jest.mock('../../bindings/tokenizer');
// jest.mock('../../bindings/models', () => ({
// __esModule: true,
// Model... | tokenizers/bindings/node/lib/bindings/tokenizer.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/tokenizer.test.ts",
"repo_id": "tokenizers",
"token_count": 5268
} | 215 |
# `tokenizers-linux-arm64-musl`
This is the **aarch64-unknown-linux-musl** binary for `tokenizers`
| tokenizers/bindings/node/npm/linux-arm64-musl/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-arm64-musl/README.md",
"repo_id": "tokenizers",
"token_count": 37
} | 216 |
use crate::tokenizer::PaddingOptions;
use napi::bindgen_prelude::*;
use napi_derive::napi;
use tokenizers::utils::truncation::TruncationDirection;
use tokenizers::Encoding;
#[napi(js_name = "Encoding")]
#[derive(Clone, Default)]
pub struct JsEncoding {
pub(crate) encoding: Option<Encoding>,
}
impl From<Encoding> fo... | tokenizers/bindings/node/src/encoding.rs/0 | {
"file_path": "tokenizers/bindings/node/src/encoding.rs",
"repo_id": "tokenizers",
"token_count": 3778
} | 217 |
# Generated content DO NOT EDIT
class Decoder:
"""
Base class for all decoders
This class is not supposed to be instantiated directly. Instead, any implementation of
a Decoder will return an instance of this class when instantiated.
"""
def decode(self, tokens):
"""
Decode the ... | tokenizers/bindings/python/py_src/tokenizers/decoders/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/decoders/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 3115
} | 218 |
from .visualizer import Annotation, EncodingVisualizer
| tokenizers/bindings/python/py_src/tokenizers/tools/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/tools/__init__.py",
"repo_id": "tokenizers",
"token_count": 13
} | 219 |
use std::sync::{Arc, RwLock};
use pyo3::exceptions;
use pyo3::prelude::*;
use pyo3::types::*;
use crate::error::ToPyResult;
use crate::utils::{PyNormalizedString, PyNormalizedStringRefMut, PyPattern};
use serde::ser::SerializeStruct;
use serde::{Deserialize, Deserializer, Serialize, Serializer};
use tk::normalizers::... | tokenizers/bindings/python/src/normalizers.rs/0 | {
"file_path": "tokenizers/bindings/python/src/normalizers.rs",
"repo_id": "tokenizers",
"token_count": 11191
} | 220 |
import pytest
from tokenizers import BertWordPieceTokenizer
from ..utils import bert_files, data_dir
class TestEncoding:
@pytest.fixture(scope="class")
def encodings(self, bert_files):
tokenizer = BertWordPieceTokenizer.from_file(bert_files["vocab"])
single_encoding = tokenizer.encode("I lov... | tokenizers/bindings/python/tests/bindings/test_encoding.py/0 | {
"file_path": "tokenizers/bindings/python/tests/bindings/test_encoding.py",
"repo_id": "tokenizers",
"token_count": 1991
} | 221 |
import os
import pytest
from tokenizers import SentencePieceBPETokenizer, SentencePieceUnigramTokenizer
class TestSentencePieceBPE:
def test_train_from_iterator(self):
text = ["A first sentence", "Another sentence", "And a last one"]
tokenizer = SentencePieceBPETokenizer()
tokenizer.trai... | tokenizers/bindings/python/tests/implementations/test_sentencepiece.py/0 | {
"file_path": "tokenizers/bindings/python/tests/implementations/test_sentencepiece.py",
"repo_id": "tokenizers",
"token_count": 1122
} | 222 |
# Trainers
<tokenizerslangcontent>
<python>
## BpeTrainer
[[autodoc]] tokenizers.trainers.BpeTrainer
## UnigramTrainer
[[autodoc]] tokenizers.trainers.UnigramTrainer
## WordLevelTrainer
[[autodoc]] tokenizers.trainers.WordLevelTrainer
## WordPieceTrainer
[[autodoc]] tokenizers.trainers.WordPieceTrainer
</python... | tokenizers/docs/source-doc-builder/api/trainers.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/trainers.mdx",
"repo_id": "tokenizers",
"token_count": 183
} | 223 |
/* Our DOM objects */
/* Version control */
.selectors {
margin-bottom: 10px;
}
.dropdown-button {
display: inline-block;
width: 50%;
background-color: #6670FF;
color: white;
border: none;
padding: 5px;
font-size: 15px;
cursor: pointer;
}
.dropdown-button:hover, .dropdown-button:... | tokenizers/docs/source/_static/css/huggingface.css/0 | {
"file_path": "tokenizers/docs/source/_static/css/huggingface.css",
"repo_id": "tokenizers",
"token_count": 2708
} | 224 |
Training from memory
----------------------------------------------------------------------------------------------------
In the `Quicktour <quicktour>`__, we saw how to build and train a tokenizer using text files,
but we can actually use any Python Iterator. In this section we'll see a few different ways of
training... | tokenizers/docs/source/tutorials/python/training_from_memory.rst/0 | {
"file_path": "tokenizers/docs/source/tutorials/python/training_from_memory.rst",
"repo_id": "tokenizers",
"token_count": 1149
} | 225 |
mod utils;
use tokenizers::models::bpe::{Vocab, BPE};
use tokenizers::Tokenizer;
use wasm_bindgen::prelude::*;
// When the `wee_alloc` feature is enabled, use `wee_alloc` as the global
// allocator.
#[cfg(feature = "wee_alloc")]
#[global_allocator]
static ALLOC: wee_alloc::WeeAlloc = wee_alloc::WeeAlloc::INIT;
#[was... | tokenizers/tokenizers/examples/unstable_wasm/src/lib.rs/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/src/lib.rs",
"repo_id": "tokenizers",
"token_count": 543
} | 226 |
//!
//! This is the CLI binary for the Tokenizers project
//!
use clap::{Parser, Subcommand};
use std::io::{self, BufRead, Write};
use tokenizers::models::bpe::BPE;
use tokenizers::pre_tokenizers::byte_level::ByteLevel;
use tokenizers::tokenizer::{AddedToken, Result};
use tokenizers::Tokenizer;
/// Generate custom To... | tokenizers/tokenizers/src/cli.rs/0 | {
"file_path": "tokenizers/tokenizers/src/cli.rs",
"repo_id": "tokenizers",
"token_count": 900
} | 227 |
use rand::distributions::WeightedIndex;
use rand::prelude::*;
use std::cell::RefCell;
use std::cmp::{min, Ordering};
use std::collections::BinaryHeap;
use std::rc::Rc;
type NodeRef = Rc<RefCell<Node>>;
type HypothesisRef = Rc<RefCell<Hypothesis>>;
type Agenda = BinaryHeap<Hypothesis>;
struct Hypothesis {
node_ref... | tokenizers/tokenizers/src/models/unigram/lattice.rs/0 | {
"file_path": "tokenizers/tokenizers/src/models/unigram/lattice.rs",
"repo_id": "tokenizers",
"token_count": 12682
} | 228 |
use crate::tokenizer::pattern::Pattern;
use crate::tokenizer::Decoder;
use crate::tokenizer::{NormalizedString, Normalizer, Result};
use crate::utils::SysRegex;
use serde::{Deserialize, Serialize};
/// Represents the different patterns that `Replace` can use
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize, Eq... | tokenizers/tokenizers/src/normalizers/replace.rs/0 | {
"file_path": "tokenizers/tokenizers/src/normalizers/replace.rs",
"repo_id": "tokenizers",
"token_count": 2048
} | 229 |
use regex::Regex;
use crate::tokenizer::{
pattern::Invert, PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior,
};
use crate::utils::macro_rules_attribute;
#[derive(Clone, Debug, PartialEq, Eq)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct Whitespace;
impl Default for Whitespace {
fn de... | tokenizers/tokenizers/src/pre_tokenizers/whitespace.rs/0 | {
"file_path": "tokenizers/tokenizers/src/pre_tokenizers/whitespace.rs",
"repo_id": "tokenizers",
"token_count": 1660
} | 230 |
//! This comes from the Rust libcore and is duplicated here because it is not exported
//! (cf <https://github.com/rust-lang/rust/blob/25091ed9b7739e12466fb2490baa1e8a2815121c/src/libcore/iter/adapters/mod.rs#L2664>)
//! We are now using the version from <https://stackoverflow.com/questions/44544323/how-to-unzip-a-sequ... | tokenizers/tokenizers/src/utils/iter.rs/0 | {
"file_path": "tokenizers/tokenizers/src/utils/iter.rs",
"repo_id": "tokenizers",
"token_count": 1339
} | 231 |
version: 2.1
setup: true
orbs:
continuation: circleci/continuation@0.1.0
parameters:
nightly:
type: boolean
default: false
jobs:
# Ensure running with CircleCI/huggingface
check_circleci_user:
docker:
- image: cimg/python:3.8.12
parallelism: 1
steps:... | transformers/.circleci/config.yml/0 | {
"file_path": "transformers/.circleci/config.yml",
"repo_id": "transformers",
"token_count": 5200
} | 232 |
FROM google/cloud-sdk:slim
# Build args.
ARG GITHUB_REF=refs/heads/main
# TODO: This Dockerfile installs pytorch/xla 3.6 wheels. There are also 3.7
# wheels available; see below.
ENV PYTHON_VERSION=3.6
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
cmake \
... | transformers/docker/transformers-pytorch-tpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-pytorch-tpu/Dockerfile",
"repo_id": "transformers",
"token_count": 1235
} | 233 |
<!---
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 ... | transformers/docs/source/de/installation.md/0 | {
"file_path": "transformers/docs/source/de/installation.md",
"repo_id": "transformers",
"token_count": 3991
} | 234 |
<!--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/add_new_model.md/0 | {
"file_path": "transformers/docs/source/en/add_new_model.md",
"repo_id": "transformers",
"token_count": 14076
} | 235 |
<!--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/fast_tokenizers.md/0 | {
"file_path": "transformers/docs/source/en/fast_tokenizers.md",
"repo_id": "transformers",
"token_count": 792
} | 236 |
<!--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/bartpho.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/bartpho.md",
"repo_id": "transformers",
"token_count": 1166
} | 237 |
<!--Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and 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... | transformers/docs/source/en/model_doc/bridgetower.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/bridgetower.md",
"repo_id": "transformers",
"token_count": 2392
} | 238 |
<!--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 | {
"file_path": "transformers/docs/source/en/model_doc/cpm.md",
"repo_id": "transformers",
"token_count": 735
} | 239 |
<!--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/model_doc/flan-t5.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/flan-t5.md",
"repo_id": "transformers",
"token_count": 781
} | 240 |
<!--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/model_doc/gpt_neox_japanese.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/gpt_neox_japanese.md",
"repo_id": "transformers",
"token_count": 1075
} | 241 |
<!--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/model_doc/mask2former.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/mask2former.md",
"repo_id": "transformers",
"token_count": 1219
} | 242 |
<!--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/openai-gpt.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/openai-gpt.md",
"repo_id": "transformers",
"token_count": 2422
} | 243 |
<!--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/prophetnet.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/prophetnet.md",
"repo_id": "transformers",
"token_count": 1170
} | 244 |
<!--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/sam.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/sam.md",
"repo_id": "transformers",
"token_count": 1871
} | 245 |
<!--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/unispeech-sat.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/unispeech-sat.md",
"repo_id": "transformers",
"token_count": 1045
} | 246 |
<!--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/xlnet.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/xlnet.md",
"repo_id": "transformers",
"token_count": 2042
} | 247 |
<!--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/perf_torch_compile.md/0 | {
"file_path": "transformers/docs/source/en/perf_torch_compile.md",
"repo_id": "transformers",
"token_count": 5859
} | 248 |
<!--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/run_scripts.md/0 | {
"file_path": "transformers/docs/source/en/run_scripts.md",
"repo_id": "transformers",
"token_count": 5851
} | 249 |
<!--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/tasks/object_detection.md/0 | {
"file_path": "transformers/docs/source/en/tasks/object_detection.md",
"repo_id": "transformers",
"token_count": 9638
} | 250 |
<!--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/tflite.md/0 | {
"file_path": "transformers/docs/source/en/tflite.md",
"repo_id": "transformers",
"token_count": 871
} | 251 |
<!--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/es/custom_models.md/0 | {
"file_path": "transformers/docs/source/es/custom_models.md",
"repo_id": "transformers",
"token_count": 5983
} | 252 |
<!--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/es/run_scripts.md/0 | {
"file_path": "transformers/docs/source/es/run_scripts.md",
"repo_id": "transformers",
"token_count": 6952
} | 253 |
<!--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/fr/quicktour.md/0 | {
"file_path": "transformers/docs/source/fr/quicktour.md",
"repo_id": "transformers",
"token_count": 10715
} | 254 |
<!---
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 ... | transformers/docs/source/it/installation.md/0 | {
"file_path": "transformers/docs/source/it/installation.md",
"repo_id": "transformers",
"token_count": 3575
} | 255 |
<!--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/it/quicktour.md/0 | {
"file_path": "transformers/docs/source/it/quicktour.md",
"repo_id": "transformers",
"token_count": 6490
} | 256 |
<!--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/ja/custom_models.md/0 | {
"file_path": "transformers/docs/source/ja/custom_models.md",
"repo_id": "transformers",
"token_count": 7501
} | 257 |
<!--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/ja/main_classes/processors.md/0 | {
"file_path": "transformers/docs/source/ja/main_classes/processors.md",
"repo_id": "transformers",
"token_count": 3103
} | 258 |
<!--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/ja/model_doc/bert-generation.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/bert-generation.md",
"repo_id": "transformers",
"token_count": 1962
} | 259 |
<!--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/ja/model_doc/byt5.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/byt5.md",
"repo_id": "transformers",
"token_count": 3268
} | 260 |
<!--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
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"token_count": 2118
} | 261 |
<!--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
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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
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"repo_id": "transformers",
"token_count": 10073
} | 263 |
<!--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
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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
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} | 265 |
<!--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
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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
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- sections:
- local: index
title: 🤗 Transformers
- local: quicktour
title: Tour rápido
- local: installation
title: Instalação
title: Início
- sections:
- local: pipeline_tutorial
title: Pipelines para inferência
- local: training
title: Fine-tuning de um modelo pré-treinado
- local: ... | transformers/docs/source/pt/_toctree.yml/0 | {
"file_path": "transformers/docs/source/pt/_toctree.yml",
"repo_id": "transformers",
"token_count": 424
} | 268 |
- sections:
- local: index
title: 🤗 Transformers
- local: quicktour
title: త్వరిత పర్యటన
title: ప్రారంభించడానికి
| transformers/docs/source/te/_toctree.yml/0 | {
"file_path": "transformers/docs/source/te/_toctree.yml",
"repo_id": "transformers",
"token_count": 125
} | 269 |
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