repo_id stringlengths 15 89 | file_path stringlengths 27 180 | content stringlengths 1 2.23M | __index_level_0__ int64 0 0 |
|---|---|---|---|
hf_public_repos/tokenizers/tokenizers/src/models | hf_public_repos/tokenizers/tokenizers/src/models/wordpiece/mod.rs | //! [WordPiece](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37842.pdf)
//! model.
use crate::models::bpe::BPE;
use crate::tokenizer::{Model, Result, Token};
use std::{
borrow::Cow,
collections::HashMap,
fs::File,
io::prelude::*,
io::{BufRead, BufReader},
path... | 0 |
hf_public_repos/tokenizers/tokenizers/src/models | hf_public_repos/tokenizers/tokenizers/src/models/wordpiece/trainer.rs | use super::WordPiece;
use crate::models::bpe::{BpeTrainer, BpeTrainerBuilder, BPE};
use crate::tokenizer::{AddedToken, Result, Trainer};
use serde::{Deserialize, Serialize};
use std::collections::HashSet;
/// A `WordPieceTrainerBuilder` can be used to create a `WordPieceTrainer` with a custom
/// configuration.
pub st... | 0 |
hf_public_repos/tokenizers/tokenizers/src/models | hf_public_repos/tokenizers/tokenizers/src/models/wordpiece/serialization.rs | use super::{super::OrderedVocabIter, WordPiece, WordPieceBuilder};
use serde::{
de::{MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
use std::collections::HashSet;
impl Serialize for WordPiece {
fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Er... | 0 |
hf_public_repos/tokenizers/tokenizers/src/models | hf_public_repos/tokenizers/tokenizers/src/models/bpe/mod.rs | //! [Byte Pair Encoding](https://www.aclweb.org/anthology/P16-1162/) model.
use std::{iter, mem};
mod model;
mod serialization;
pub mod trainer;
mod word;
type Pair = (u32, u32);
/// Errors that can be encountered while using or constructing a `BPE` model.
#[derive(thiserror::Error, Debug)]
pub enum Error {
/// ... | 0 |
hf_public_repos/tokenizers/tokenizers/src/models | hf_public_repos/tokenizers/tokenizers/src/models/bpe/word.rs | use super::Pair;
use rand::{thread_rng, Rng};
use std::cmp::Ordering;
use std::collections::{BinaryHeap, HashMap};
#[derive(Debug, Eq)]
struct Merge {
pos: usize,
rank: u32,
new_id: u32,
}
impl PartialEq for Merge {
fn eq(&self, other: &Self) -> bool {
self.rank == other.rank && self.pos == ot... | 0 |
hf_public_repos/tokenizers/tokenizers/src/models | hf_public_repos/tokenizers/tokenizers/src/models/bpe/trainer.rs | #![allow(clippy::map_entry)]
use super::{Pair, WithFirstLastIterator, Word, BPE};
use crate::parallelism::*;
use crate::tokenizer::{AddedToken, Result, Trainer};
use crate::utils::progress::{ProgressBar, ProgressStyle};
use serde::{Deserialize, Serialize};
use std::cmp::Ordering;
use std::collections::{BinaryHeap, Has... | 0 |
hf_public_repos/tokenizers/tokenizers/src/models | hf_public_repos/tokenizers/tokenizers/src/models/bpe/serialization.rs | use super::{super::OrderedVocabIter, convert_merges_to_hashmap, BpeBuilder, Pair, BPE};
use serde::{
de::{Error, MapAccess, Visitor},
ser::SerializeStruct,
Deserialize, Deserializer, Serialize, Serializer,
};
use std::collections::HashMap;
impl Serialize for BPE {
fn serialize<S>(&self, serializer: S) ... | 0 |
hf_public_repos/tokenizers/tokenizers/src/models | hf_public_repos/tokenizers/tokenizers/src/models/bpe/model.rs | use super::{super::OrderedVocabIter, trainer::BpeTrainer, Error, Pair, Word};
use crate::tokenizer::{Model, Result, Token};
use crate::utils::cache::{Cache, DEFAULT_CACHE_CAPACITY};
use crate::utils::iter::ResultShunt;
use serde_json::Value;
use std::borrow::Cow;
use std::{
collections::HashMap,
fs::File,
i... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/decoders/sequence.rs | use crate::decoders::DecoderWrapper;
use crate::tokenizer::{Decoder, Result};
use crate::utils::macro_rules_attribute;
use serde::{Deserialize, Serialize};
#[derive(Clone, Debug)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct Sequence {
decoders: Vec<DecoderWrapper>,
}
impl Sequence {
pub fn new(decod... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/decoders/fuse.rs | use crate::tokenizer::{Decoder, Result};
use monostate::MustBe;
use serde::{Deserialize, Serialize};
#[derive(Clone, Debug, Serialize, Deserialize, Default)]
/// Fuse simply fuses all tokens into one big string.
/// It's usually the last decoding step anyway, but this
/// decoder exists incase some decoders need to ha... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/decoders/mod.rs | pub mod bpe;
pub mod byte_fallback;
pub mod ctc;
pub mod fuse;
pub mod sequence;
pub mod strip;
pub mod wordpiece;
// Re-export these as decoders
pub use super::pre_tokenizers::byte_level;
pub use super::pre_tokenizers::metaspace;
use serde::{Deserialize, Serialize};
use crate::decoders::bpe::BPEDecoder;
use crate::... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/decoders/ctc.rs | use crate::decoders::wordpiece;
use crate::tokenizer::{Decoder, Result};
use itertools::Itertools;
use serde::{Deserialize, Serialize};
#[derive(Debug, Clone, Serialize, Deserialize)]
/// The CTC (Connectionist Temporal Classification) decoder takes care
/// of sanitizing a list of inputs token.
/// Due to some align... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/decoders/byte_fallback.rs | use crate::tokenizer::{Decoder, Result};
use monostate::MustBe;
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Clone, Debug, Serialize, Default)]
/// ByteFallback is a simple trick which converts tokens looking like `<0x61>`
/// to pure bytes, and attempts to make them into a string. If the tokens
/// can... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/decoders/bpe.rs | use crate::tokenizer::{Decoder, Result};
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Clone, Debug, Serialize)]
/// Allows decoding Original BPE by joining all the tokens and then replacing
/// the suffix used to identify end-of-words by whitespaces
#[serde(tag = "type")]
#[non_exhaustive]
pub struct BP... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/decoders/wordpiece.rs | use crate::tokenizer::{Decoder, Result};
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Clone, Debug, Serialize)]
/// The WordPiece decoder takes care of decoding a list of wordpiece tokens
/// back into a readable string.
#[serde(tag = "type")]
#[non_exhaustive]
pub struct WordPiece {
/// The prefix ... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/decoders/strip.rs | use crate::tokenizer::{Decoder, Result};
use serde::{Deserialize, Serialize};
#[derive(Deserialize, Clone, Debug, Serialize, Default)]
/// Strip is a simple trick which converts tokens looking like `<0x61>`
/// to pure bytes, and attempts to make them into a string. If the tokens
/// cannot be decoded you will get � ... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/utils/truncation.rs | use crate::tokenizer::{Encoding, Result};
use serde::{Deserialize, Serialize};
use std::cmp;
use std::mem;
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize, Eq, Default)]
pub enum TruncationDirection {
Left,
#[default]
Right,
}
impl std::convert::AsRef<str> for TruncationDirection {
fn a... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/utils/from_pretrained.rs | use crate::Result;
use hf_hub::{api::sync::ApiBuilder, Repo, RepoType};
use std::collections::HashMap;
use std::path::PathBuf;
/// Defines the aditional parameters available for the `from_pretrained` function
#[derive(Debug, Clone)]
pub struct FromPretrainedParameters {
pub revision: String,
pub user_agent: Ha... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/utils/mod.rs | pub(crate) mod cache;
#[cfg(feature = "http")]
pub(crate) mod from_pretrained;
#[cfg(feature = "unstable_wasm")]
mod fancy;
#[cfg(feature = "unstable_wasm")]
pub use fancy::SysRegex;
#[cfg(not(feature = "unstable_wasm"))]
mod onig;
#[cfg(not(feature = "unstable_wasm"))]
pub use crate::utils::onig::SysRegex;
pub mod i... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/utils/cache.rs | use std::borrow::Borrow;
use std::collections::HashMap;
use std::hash::Hash;
use std::sync::RwLock;
/// The default capacity for a `BPE`'s internal cache.
pub static DEFAULT_CACHE_CAPACITY: usize = 10_000;
/// Provides a simple multithread cache to speed up BPE tokenization that will try to read values
/// concurrent... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/utils/onig.rs | 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)
}
... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/utils/iter.rs | //! 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... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/utils/progress.rs | #[cfg(feature = "progressbar")]
pub(crate) use indicatif::{ProgressBar, ProgressStyle};
#[cfg(not(feature = "progressbar"))]
mod progressbar {
use std::borrow::Cow;
pub struct ProgressBar;
impl ProgressBar {
pub fn new(_length: u64) -> Self {
Self {}
}
pub fn set_length... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/utils/parallelism.rs | //!
//! This module defines helpers to allow optional Rayon usage.
//!
use rayon::iter::IterBridge;
use rayon::prelude::*;
use rayon_cond::CondIterator;
// Re-export rayon current_num_threads
pub use rayon::current_num_threads;
pub const ENV_VARIABLE: &str = "TOKENIZERS_PARALLELISM";
// Reading/Writing this variabl... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/utils/fancy.rs | use fancy_regex::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) -> Matches<'r, 't> {
Matches(self.regex.find_iter(inside))
}
pub fn new(regex_str: &str) -> Result<Self, Box<dyn Error + Se... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/utils/padding.rs | use crate::parallelism::*;
use crate::tokenizer::{Encoding, Result};
use serde::{Deserialize, Serialize};
/// The various possible padding directions.
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum PaddingDirection {
Left,
Right,
}
impl std::convert::AsRef<str> for PaddingDirection {
fn as... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/whitespace.rs | 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... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/metaspace.rs | use crate::tokenizer::{Decoder, PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use serde::{Deserialize, Deserializer, Serialize};
/// Enum representing options for the metaspace prepending scheme.
#[derive(Debug, Clone, PartialEq, Serialize, Eq, Deserialize, Copy)]
#[serde(rename_all = "snake_case"... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/sequence.rs | use crate::pre_tokenizers::PreTokenizerWrapper;
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result};
use crate::utils::macro_rules_attribute;
use serde::{Deserialize, Serialize};
#[derive(Clone, Debug, PartialEq)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct Sequence {
pretokenizers: Vec<PreT... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/mod.rs | pub mod bert;
pub mod byte_level;
pub mod delimiter;
pub mod digits;
pub mod metaspace;
pub mod punctuation;
pub mod sequence;
pub mod split;
pub mod unicode_scripts;
pub mod whitespace;
use serde::{Deserialize, Serialize};
use crate::pre_tokenizers::bert::BertPreTokenizer;
use crate::pre_tokenizers::byte_level::Byte... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/split.rs | use crate::utils::SysRegex;
use serde::{Deserialize, Deserializer, Serialize};
use crate::tokenizer::{
pattern::Invert, PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior,
};
/// Represents the different patterns that `Split` can use
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize, Eq)]
pub... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/digits.rs | use serde::{Deserialize, Serialize};
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
#[derive(Clone, Debug, PartialEq, Eq)]
/// Pre tokenizes the numbers into single tokens. If individual_digits is set
/// to true, then all digits are ... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/bert.rs | use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
use unicode_categories::UnicodeCategories;
fn is_bert_punc(x: char) -> bool {
char::is_ascii_punctuation(&x) || x.is_punctuation()
}
#[derive(Copy, Clone, Debug, PartialEq, Eq)]
#[mac... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/punctuation.rs | use serde::{Deserialize, Serialize};
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
use unicode_categories::UnicodeCategories;
fn is_punc(x: char) -> bool {
char::is_ascii_punctuation(&x) || x.is_punctuation()
}
#[derive(Copy, Cl... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/byte_level.rs | use std::collections::{HashMap, HashSet};
use crate::utils::SysRegex;
use serde::{Deserialize, Serialize};
use crate::tokenizer::{
Decoder, Encoding, PostProcessor, PreTokenizedString, PreTokenizer, Result,
SplitDelimiterBehavior,
};
use crate::utils::macro_rules_attribute;
/// Converts bytes to unicode char... | 0 |
hf_public_repos/tokenizers/tokenizers/src | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/delimiter.rs | use serde::{Deserialize, Serialize};
use crate::tokenizer::{PreTokenizedString, PreTokenizer, Result, SplitDelimiterBehavior};
use crate::utils::macro_rules_attribute;
#[derive(Copy, Clone, Debug, PartialEq, Eq)]
#[non_exhaustive]
#[macro_rules_attribute(impl_serde_type!)]
pub struct CharDelimiterSplit {
pub deli... | 0 |
hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/unicode_scripts/mod.rs | mod pre_tokenizer;
mod scripts;
// Re-export the PreTokenizer
pub use pre_tokenizer::UnicodeScripts;
| 0 |
hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/unicode_scripts/pre_tokenizer.rs | use crate::pre_tokenizers::unicode_scripts::scripts::{get_script, Script};
use crate::tokenizer::{normalizer::Range, PreTokenizedString, PreTokenizer, Result};
use crate::utils::macro_rules_attribute;
#[derive(Clone, Debug, PartialEq, Eq)]
#[macro_rules_attribute(impl_serde_type!)]
pub struct UnicodeScripts;
impl Uni... | 0 |
hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers | hf_public_repos/tokenizers/tokenizers/src/pre_tokenizers/unicode_scripts/scripts.rs | // Generated by modified Perl script at https://github.com/google/sentencepiece/blob/master/data/gen_unicode_scripts_code.pl
// Unicode scripts : https://gist.github.com/Narsil/07556f26dc84a6baeff4d499e68d3cd2
// Rust adaptation : https://gist.github.com/Narsil/1df9fbbf5296a8d4d62de55dcb2fe700
#[derive(PartialEq, Debu... | 0 |
hf_public_repos/tokenizers | hf_public_repos/tokenizers/docs/README.md | ## 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... | 0 |
hf_public_repos/tokenizers | hf_public_repos/tokenizers/docs/Makefile | # Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for those with `?=`
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
BUILDDIR ?= build
SOURCEDIR = source
# Put it first so that "make" without argument is like "make html_all".
h... | 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source/entities.inc | .. entities:: python
:global:
class
class
classmethod
class method
Tokenizer
:class:`~tokenizers.Tokenizer`
Tokenizer.train
:meth:`~tokenizers.Tokenizer.train`
Tokenizer.save
:meth:`~tokenizers.Tokenizer.save`
Tokenizer.from_file
:meth:`~toke... | 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source/quicktour.rst | Quicktour
====================================================================================================
Let's have a quick look at the 🤗 Tokenizers library features. The library provides an
implementation of today's most used tokenizers that is both easy to use and blazing fast.
.. only:: python
It can b... | 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source/pipeline.rst | The tokenization pipeline
====================================================================================================
When calling :entity:`Tokenizer.encode` or :entity:`Tokenizer.encode_batch`, the input text(s) go
through the following pipeline:
- :ref:`normalization`
- :ref:`pre-tokenization`
- :ref:`mode... | 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source/components.rst | Components
====================================================================================================
When building a Tokenizer, you can attach various types of components to this Tokenizer in order
to customize its behavior. This page lists most provided components.
.. _normalizers:
.. entities:: python
... | 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source/index.rst | Tokenizers
====================================================================================================
Fast State-of-the-art tokenizers, optimized for both research and production
`🤗 Tokenizers`_ provides an implementation of today's most used tokenizers, with
a focus on performance and versatility. These t... | 0 |
hf_public_repos/tokenizers/docs/source/_static | hf_public_repos/tokenizers/docs/source/_static/js/custom.js | // These three variables below need to be updated at each release for the selectors.
const languages = [ "rust", "python", "node" ];
// Last stable version for each language
const stableVersion = {
"rust": "master",
"python": "v0.10.0",
"node": "master"
}
// Dictionary doc folder to Label for each language... | 0 |
hf_public_repos/tokenizers/docs/source/_static | hf_public_repos/tokenizers/docs/source/_static/css/code-snippets.css |
.highlight .c1, .highlight .sd{
color: #999
}
.highlight .nn, .highlight .k, .highlight .s1, .highlight .nb, .highlight .bp, .highlight .kc, .highlight .kt {
color: #FB8D68;
}
.highlight .kn, .highlight .nv, .highlight .s2, .highlight .ow, .highlight .kd, .highlight .kr, .highlight .s {
color: #6670FF;
}... | 0 |
hf_public_repos/tokenizers/docs/source/_static | hf_public_repos/tokenizers/docs/source/_static/css/huggingface.css | /* 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:... | 0 |
hf_public_repos/tokenizers/docs/source | hf_public_repos/tokenizers/docs/source/api/python.inc | Input sequences
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
These types represent all the different kinds of sequence that can be used as input of a Tokenizer.
Globally, any sequence can be either a string or a list of strings, according to the operating
mode of... | 0 |
hf_public_repos/tokenizers/docs/source | hf_public_repos/tokenizers/docs/source/api/node.inc | Documentation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The node API has not been documented yet.
| 0 |
hf_public_repos/tokenizers/docs/source | hf_public_repos/tokenizers/docs/source/api/reference.rst | .. only:: python
.. include:: python.inc
.. only:: rust
.. include:: rust.inc
.. only:: node
.. include:: node.inc
| 0 |
hf_public_repos/tokenizers/docs/source | hf_public_repos/tokenizers/docs/source/api/rust.inc | Documentation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The Rust API Reference is available directly on the `Docs.rs <https://docs.rs/tokenizers>`__
website.
| 0 |
hf_public_repos/tokenizers/docs/source | hf_public_repos/tokenizers/docs/source/_ext/toctree_tags.py | import re
from sphinx.directives.other import TocTree
class TocTreeTags(TocTree):
hasPat = re.compile("^\s*:(.+):(.+)$")
def filter_entries(self, entries):
filtered = []
for e in entries:
m = self.hasPat.match(e)
if m != None:
if self.env.app.tags.has(m... | 0 |
hf_public_repos/tokenizers/docs/source | hf_public_repos/tokenizers/docs/source/_ext/rust_doc.py | from docutils import nodes
import sphinx
from sphinx.locale import _
from conf import rust_version
logger = sphinx.util.logging.getLogger(__name__)
class RustRef:
def __call__(self, name, rawtext, text, lineno, inliner, options={}, content=[]):
doctype = name.split("_")[1]
parts = text.split(":... | 0 |
hf_public_repos/tokenizers/docs/source | hf_public_repos/tokenizers/docs/source/_ext/entities.py | from collections import defaultdict, abc
from typing import cast
from docutils import nodes
from docutils.parsers.rst import Directive
import sphinx
from sphinx.locale import _
from sphinx.util.docutils import SphinxDirective
from sphinx.errors import ExtensionError
from conf import languages as LANGUAGES
logger = ... | 0 |
hf_public_repos/tokenizers/docs/source/tutorials | hf_public_repos/tokenizers/docs/source/tutorials/python/training_from_memory.rst | 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... | 0 |
hf_public_repos/tokenizers/docs/source | hf_public_repos/tokenizers/docs/source/installation/python.inc | 🤗 Tokenizers is tested on Python 3.5+.
You should install 🤗 Tokenizers in a
`virtual environment <https://docs.python.org/3/library/venv.html>`_. If you're unfamiliar with
Python virtual environments, check out the
`user guide <https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/>`__.
C... | 0 |
hf_public_repos/tokenizers/docs/source | hf_public_repos/tokenizers/docs/source/installation/node.inc | Installation with npm
----------------------------------------------------------------------------------------------------
You can simply install 🤗 Tokenizers with npm using::
npm install tokenizers
| 0 |
hf_public_repos/tokenizers/docs/source | hf_public_repos/tokenizers/docs/source/installation/rust.inc | Crates.io
----------------------------------------------------------------------------------------------------
🤗 Tokenizers is available on `crates.io <https://crates.io/crates/tokenizers>`__.
You just need to add it to your :obj:`Cargo.toml`::
tokenizers = "0.10"
| 0 |
hf_public_repos/tokenizers/docs/source | hf_public_repos/tokenizers/docs/source/installation/main.rst | Installation
====================================================================================================
.. only:: python
.. include:: python.inc
.. only:: rust
.. include:: rust.inc
.. only:: node
.. include:: node.inc
| 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source-doc-builder/quicktour.mdx | # Quicktour
Let's have a quick look at the 🤗 Tokenizers library features. The
library provides an implementation of today's most used tokenizers that
is both easy to use and blazing fast.
## Build a tokenizer from scratch
To illustrate how fast the 🤗 Tokenizers library is, let's train a new
tokenizer on [wikitext-... | 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source-doc-builder/training_from_memory.mdx | # 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 our
tokenizer.
For all the examples listed below, we'll use the same [`~tokenizers.Tokenizer`] and
[... | 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source-doc-builder/installation.mdx | # Installation
<tokenizerslangcontent>
<python>
🤗 Tokenizers is tested on Python 3.5+.
You should install 🤗 Tokenizers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're
unfamiliar with Python virtual environments, check out the [user
guide](https://packaging.python.org/guides/instal... | 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source-doc-builder/index.mdx | <!-- DISABLE-FRONTMATTER-SECTIONS -->
# Tokenizers
Fast State-of-the-art tokenizers, optimized for both research and
production
[🤗 Tokenizers](https://github.com/huggingface/tokenizers) provides an
implementation of today's most used tokenizers, with a focus on
performance and versatility. These tokenizers are also... | 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source-doc-builder/components.mdx | # Components
When building a Tokenizer, you can attach various types of components to
this Tokenizer in order to customize its behavior. This page lists most
provided components.
## Normalizers
A `Normalizer` is in charge of pre-processing the input string in order
to normalize it as relevant for a given use case. S... | 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source-doc-builder/_toctree.yml | - sections:
- local: index
title: 🤗 Tokenizers
- local: quicktour
title: Quicktour
- local: installation
title: Installation
- local: pipeline
title: The tokenization pipeline
- local: components
title: Components
- local: training_from_memory
title: Training from memory
title: G... | 0 |
hf_public_repos/tokenizers/docs | hf_public_repos/tokenizers/docs/source-doc-builder/pipeline.mdx | # The tokenization pipeline
When calling `Tokenizer.encode` or
`Tokenizer.encode_batch`, the input
text(s) go through the following pipeline:
- `normalization`
- `pre-tokenization`
- `model`
- `post-processing`
We'll see in details what happens during each of those steps in detail,
as well as when you want t... | 0 |
hf_public_repos/tokenizers/docs/source-doc-builder | hf_public_repos/tokenizers/docs/source-doc-builder/api/visualizer.mdx | # Visualizer
<tokenizerslangcontent>
<python>
## Annotation
[[autodoc]] tokenizers.tools.Annotation
## EncodingVisualizer
[[autodoc]] tokenizers.tools.EncodingVisualizer
- __call__
</python>
<rust>
The Rust API Reference is available directly on the [Docs.rs](https://docs.rs/tokenizers/latest/tokenizers/) webs... | 0 |
hf_public_repos/tokenizers/docs/source-doc-builder | hf_public_repos/tokenizers/docs/source-doc-builder/api/post-processors.mdx | # Post-processors
<tokenizerslangcontent>
<python>
## BertProcessing
[[autodoc]] tokenizers.processors.BertProcessing
## ByteLevel
[[autodoc]] tokenizers.processors.ByteLevel
## RobertaProcessing
[[autodoc]] tokenizers.processors.RobertaProcessing
## TemplateProcessing
[[autodoc]] tokenizers.processors.Template... | 0 |
hf_public_repos/tokenizers/docs/source-doc-builder | hf_public_repos/tokenizers/docs/source-doc-builder/api/added-tokens.mdx | # Added Tokens
<tokenizerslangcontent>
<python>
## AddedToken
[[autodoc]] tokenizers.AddedToken
- content
- lstrip
- normalized
- rstrip
- single_word
</python>
<rust>
The Rust API Reference is available directly on the [Docs.rs](https://docs.rs/tokenizers/latest/tokenizers/) website.
</rust>
<nod... | 0 |
hf_public_repos/tokenizers/docs/source-doc-builder | hf_public_repos/tokenizers/docs/source-doc-builder/api/tokenizer.mdx | # Tokenizer
<tokenizerslangcontent>
<python>
## Tokenizer
[[autodoc]] tokenizers.Tokenizer
- all
- decoder
- model
- normalizer
- padding
- post_processor
- pre_tokenizer
- truncation
</python>
<rust>
The Rust API Reference is available directly on the [Docs.rs](https://docs.rs/tokeniz... | 0 |
hf_public_repos/tokenizers/docs/source-doc-builder | hf_public_repos/tokenizers/docs/source-doc-builder/api/models.mdx | # Models
<tokenizerslangcontent>
<python>
## BPE
[[autodoc]] tokenizers.models.BPE
## Model
[[autodoc]] tokenizers.models.Model
## Unigram
[[autodoc]] tokenizers.models.Unigram
## WordLevel
[[autodoc]] tokenizers.models.WordLevel
## WordPiece
[[autodoc]] tokenizers.models.WordPiece
</python>
<rust>
The Rust A... | 0 |
hf_public_repos/tokenizers/docs/source-doc-builder | hf_public_repos/tokenizers/docs/source-doc-builder/api/normalizers.mdx | # Normalizers
<tokenizerslangcontent>
<python>
## BertNormalizer
[[autodoc]] tokenizers.normalizers.BertNormalizer
## Lowercase
[[autodoc]] tokenizers.normalizers.Lowercase
## NFC
[[autodoc]] tokenizers.normalizers.NFC
## NFD
[[autodoc]] tokenizers.normalizers.NFD
## NFKC
[[autodoc]] tokenizers.normalizers.NF... | 0 |
hf_public_repos/tokenizers/docs/source-doc-builder | hf_public_repos/tokenizers/docs/source-doc-builder/api/decoders.mdx | # Decoders
<tokenizerslangcontent>
<python>
## BPEDecoder
[[autodoc]] tokenizers.decoders.BPEDecoder
## ByteLevel
[[autodoc]] tokenizers.decoders.ByteLevel
## CTC
[[autodoc]] tokenizers.decoders.CTC
## Metaspace
[[autodoc]] tokenizers.decoders.Metaspace
## WordPiece
[[autodoc]] tokenizers.decoders.WordPiece
<... | 0 |
hf_public_repos/tokenizers/docs/source-doc-builder | hf_public_repos/tokenizers/docs/source-doc-builder/api/encode-inputs.mdx | # Encode Inputs
<tokenizerslangcontent>
<python>
These types represent all the different kinds of input that a [`~tokenizers.Tokenizer`] accepts
when using [`~tokenizers.Tokenizer.encode_batch`].
## TextEncodeInput[[[[tokenizers.TextEncodeInput]]]]
<code>tokenizers.TextEncodeInput</code>
Represents a textual input ... | 0 |
hf_public_repos/tokenizers/docs/source-doc-builder | hf_public_repos/tokenizers/docs/source-doc-builder/api/trainers.mdx | # Trainers
<tokenizerslangcontent>
<python>
## BpeTrainer
[[autodoc]] tokenizers.trainers.BpeTrainer
## UnigramTrainer
[[autodoc]] tokenizers.trainers.UnigramTrainer
## WordLevelTrainer
[[autodoc]] tokenizers.trainers.WordLevelTrainer
## WordPieceTrainer
[[autodoc]] tokenizers.trainers.WordPieceTrainer
</python... | 0 |
hf_public_repos/tokenizers/docs/source-doc-builder | hf_public_repos/tokenizers/docs/source-doc-builder/api/pre-tokenizers.mdx | # Pre-tokenizers
<tokenizerslangcontent>
<python>
## BertPreTokenizer
[[autodoc]] tokenizers.pre_tokenizers.BertPreTokenizer
## ByteLevel
[[autodoc]] tokenizers.pre_tokenizers.ByteLevel
## CharDelimiterSplit
[[autodoc]] tokenizers.pre_tokenizers.CharDelimiterSplit
## Digits
[[autodoc]] tokenizers.pre_tokenizers... | 0 |
hf_public_repos/tokenizers/docs/source-doc-builder | hf_public_repos/tokenizers/docs/source-doc-builder/api/encoding.mdx | # Encoding
<tokenizerslangcontent>
<python>
## Encoding
[[autodoc]] tokenizers.Encoding
- all
- attention_mask
- ids
- n_sequences
- offsets
- overflowing
- sequence_ids
- special_tokens_mask
- tokens
- type_ids
- word_ids
- words
</python>
<rust>
The Rust API Reference... | 0 |
hf_public_repos/tokenizers/docs/source-doc-builder | hf_public_repos/tokenizers/docs/source-doc-builder/api/input-sequences.mdx | # Input Sequences
<tokenizerslangcontent>
<python>
These types represent all the different kinds of sequence that can be used as input of a Tokenizer.
Globally, any sequence can be either a string or a list of strings, according to the operating
mode of the tokenizer: `raw text` vs `pre-tokenized`.
## TextInputSequen... | 0 |
hf_public_repos | hf_public_repos/text-generation-inference/.dockerignore | aml
target
server/transformers
server/flash-attention
| 0 |
hf_public_repos | hf_public_repos/text-generation-inference/update_doc.py | import subprocess
import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--check", action="store_true")
args = parser.parse_args()
output = subprocess.check_output(["text-generation-launcher", "--help"]).decode(
"utf-8"
)
wrap_code_blocks_flag = "<!-- WR... | 0 |
hf_public_repos | hf_public_repos/text-generation-inference/Dockerfile | # Rust builder
FROM lukemathwalker/cargo-chef:latest-rust-1.71 AS chef
WORKDIR /usr/src
ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
FROM chef as planner
COPY Cargo.toml Cargo.toml
COPY rust-toolchain.toml rust-toolchain.toml
COPY proto proto
COPY benchmark benchmark
COPY router router
COPY launcher launcher
RUN ca... | 0 |
hf_public_repos | hf_public_repos/text-generation-inference/README.md | <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... | 0 |
hf_public_repos | hf_public_repos/text-generation-inference/rust-toolchain.toml | [toolchain]
channel = "1.70.0"
components = ["rustfmt", "clippy"] | 0 |
hf_public_repos | hf_public_repos/text-generation-inference/Dockerfile_amd | # Rust builder
FROM lukemathwalker/cargo-chef:latest-rust-1.71 AS chef
WORKDIR /usr/src
ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
FROM chef as planner
COPY Cargo.toml Cargo.toml
COPY rust-toolchain.toml rust-toolchain.toml
COPY proto proto
COPY benchmark benchmark
COPY router router
COPY launcher launcher
RUN ca... | 0 |
hf_public_repos | hf_public_repos/text-generation-inference/sagemaker-entrypoint.sh | #!/bin/bash
if [[ -z "${HF_MODEL_ID}" ]]; then
echo "HF_MODEL_ID must be set"
exit 1
fi
export MODEL_ID="${HF_MODEL_ID}"
if [[ -n "${HF_MODEL_REVISION}" ]]; then
export REVISION="${HF_MODEL_REVISION}"
fi
if [[ -n "${SM_NUM_GPUS}" ]]; then
export NUM_SHARD="${SM_NUM_GPUS}"
fi
if [[ -n "${HF_MODEL_QUANTIZE}" ... | 0 |
hf_public_repos | hf_public_repos/text-generation-inference/Cargo.lock | # This file is automatically @generated by Cargo.
# It is not intended for manual editing.
version = 3
[[package]]
name = "addr2line"
version = "0.21.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "8a30b2e23b9e17a9f90641c7ab1549cd9b44f296d3ccbf309d2863cfe398a0cb"
dependencies = [
"giml... | 0 |
hf_public_repos | hf_public_repos/text-generation-inference/Makefile | install-server:
cd server && make install
install-custom-kernels:
if [ "$$BUILD_EXTENSIONS" = "True" ]; then cd server/custom_kernels && python setup.py install; else echo "Custom kernels are disabled, you need to set the BUILD_EXTENSIONS environment variable to 'True' in order to build them. (Please read the docs, ... | 0 |
hf_public_repos | hf_public_repos/text-generation-inference/Cargo.toml | [workspace]
members = [
"benchmark",
"router",
"router/client",
"router/grpc-metadata",
"launcher"
]
[workspace.package]
version = "1.3.4"
edition = "2021"
authors = ["Olivier Dehaene"]
homepage = "https://github.com/huggingface/text-generation-inference"
[profile.release]
debug = 1
incremental = ... | 0 |
hf_public_repos | hf_public_repos/text-generation-inference/LICENSE | Hugging Face Optimized Inference License 1.0 (HFOILv1.0)
This License Agreement governs the use of the Software and its Modifications. It is a
binding agreement between the Licensor and You.
This License Agreement shall be referred to as Hugging Face Optimized Inference License
1.0 or HFOILv1.0. We may publish revis... | 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/load_tests/vllm.js | import { get_options, run } from "./common.js";
const reference_latency_ms = 22;
const host = __ENV.HOST || '127.0.0.1:8000';
const max_new_tokens = 50;
function generate_payload(gpt){
const input = gpt["conversations"][0]["value"];
return {"prompt": input, "temperature": 0.5, "ignore_eos": true}
}
export ... | 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/load_tests/common.js | import { check, randomSeed } from 'k6';
import http from 'k6/http';
import { Trend, Counter } from 'k6/metrics';
import { randomItem } from 'https://jslib.k6.io/k6-utils/1.2.0/index.js';
const seed = 0;
const host = __ENV.HOST || '127.0.0.1:8000';
const timePerToken = new Trend('time_per_token', true);
const tokens =... | 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/load_tests/tgi.js | import { get_options, run } from "./common.js";
const reference_latency_ms = 70;
const host = __ENV.HOST || '127.0.0.1:8000';
const max_new_tokens = 50;
function generate_payload(gpt){
const input = gpt["conversations"][0]["value"];
return {"inputs": input, "parameters": {"max_new_tokens": max_new_tokens, "... | 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/load_tests/starcoder_load.js | import {check} from 'k6';
import http from 'k6/http';
import {Trend} from 'k6/metrics';
const host = __ENV.HOST || '127.0.0.1:3000';
const totalTime = new Trend('total_time', true);
const validationTime = new Trend('validation_time', true);
const queueTime = new Trend('queue_time', true);
const inferenceTime = new Tr... | 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/integration-tests/pyproject.toml | [tool.poetry]
name = "text-generation-integration-tests"
version = "1.3.4"
description = "Text Generation Inference integration tests"
authors = ["Nicolas Patry <nicolas@huggingface.co>"]
[tool.poetry.dependencies]
python = ">=3.9,<3.13"
syrupy = "4.0.1"
text-generation = "^0.6.0"
pytest = "^7.4.0"
pytest-asyncio = "^... | 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/integration-tests/poetry.lock | # This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
[[package]]
name = "aiohttp"
version = "3.8.5"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.6"
files = [
{file = "aiohttp-3.8.5-cp310-cp310-macosx_10_9_universal2.whl",... | 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/integration-tests/requirements.txt | aiohttp==3.8.5 ; python_version >= "3.9" and python_version < "3.13"
aiosignal==1.3.1 ; python_version >= "3.9" and python_version < "3.13"
async-timeout==4.0.3 ; python_version >= "3.9" and python_version < "3.13"
attrs==23.1.0 ; python_version >= "3.9" and python_version < "3.13"
certifi==2023.7.22 ; python_version >... | 0 |
hf_public_repos/text-generation-inference | hf_public_repos/text-generation-inference/integration-tests/pytest.ini | [pytest]
addopts = --snapshot-warn-unused
asyncio_mode = auto
markers =
private: marks tests as requiring an admin hf token (deselect with '-m "not private"') | 0 |
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