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image: archlinux
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packages:
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- rustup
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- cargo-audit
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- cargo-deny
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- cargo-msrv
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- cargo-nextest
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- cargo-outdated
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- cargo-tarpaulin
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- cargo-udeps
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secrets:
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- 88cce185-d2ec-4573-9546-e33f36f79bbf
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sources:
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- https://git.sr.ht/~swaits/bpe-tokenizer
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artifacts:
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- bpe-tokenizer/tarpaulin-report.html
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tasks:
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- setup: |
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rustup toolchain install nightly stable
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cd bpe-tokenizer/
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rustup run stable cargo fetch
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- format: |
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rustup default stable
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cd bpe-tokenizer/
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cargo fmt --verbose --check --all
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- lint: |
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rustup default stable
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cd bpe-tokenizer/
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cargo clippy --verbose --all-targets --all-features
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- outdated: |
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rustup default stable
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cd bpe-tokenizer/
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cargo outdated
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- deny: |
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rustup default stable
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cd bpe-tokenizer/
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cargo deny check
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- udeps: |
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rustup default nightly
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cd bpe-tokenizer/
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cargo +nightly udeps --all-features
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- audit: |
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rustup default stable
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cd bpe-tokenizer/
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cargo audit -d /tmp/advisory-db
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- msrv: |
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rustup default stable
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cd bpe-tokenizer/
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cargo msrv verify
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- coverage: |
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rustup default stable
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cd bpe-tokenizer/
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cargo tarpaulin --verbose --all-features --out html
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- stable: |
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rustup default stable
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cd bpe-tokenizer/
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cargo build --verbose --all-targets --all-features
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cargo nextest run --verbose --all-targets --all-features
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- nightly: |
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rustup default nightly
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cd bpe-tokenizer/
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cargo clippy --verbose --all-targets --all-features ||:
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cargo build --verbose --all-targets --all-features ||:
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cargo nextest --verbose --all-targets --all-features ||:
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- docs: |
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cd bpe-tokenizer/
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rustup run stable cargo doc --no-deps
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rustup run nightly cargo doc --no-deps ||:
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- mirror-to-github: |
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cd ~/bpe-tokenizer
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ssh-keyscan -t rsa github.com >> ~/.ssh/known_hosts
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git remote add github git@github.com:swaits/bpe-tokenizer.git
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git push --mirror github
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@@ -0,0 +1,2 @@
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/target
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/Cargo.lock
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+51
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[package]
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name = "bpe-tokenizer"
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version = "0.1.0"
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authors = ["Stephen Waits <steve@waits.net>"]
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edition = "2021"
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description = "A BPE Tokenizer library."
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keywords = ["byte", "pair", "encoding", "bpe", "tokenizer"]
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categories = ["algorithms", "text-processing"]
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documentation = "https://docs.rs/bpe-tokenizer/"
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readme = "README.md"
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homepage = "https://github.com/swaits/bpe-tokenizer/"
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repository = "https://github.com/swaits/bpe-tokenizer/"
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rust-version = "1.64"
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license-file = "LICENSE"
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[dependencies]
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thiserror = "1.0.64"
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unicode-segmentation = "1.12.0"
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# optional, only needed when defaul-{small,medium,large} are enabled
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bincode = { version = "1.3.3", optional = true }
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lz4_flex = { version = "0.11.3", optional = true }
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serde = { version = "1.0.210", optional = true }
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[features]
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default-small = ["bincode", "lz4_flex", "serde"]
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default-medium = ["bincode", "lz4_flex", "serde"]
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default-large = ["bincode", "lz4_flex", "serde"]
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[build-dependencies]
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bincode = { version = "1.3.3", optional = true }
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lz4_flex = { version = "0.11.3", optional = true }
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serde = { version = "1.0.210", optional = true }
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[package.metadata.docs.rs]
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default = []
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all-features = true
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[profile.dev]
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opt-level = 1
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debug = true
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overflow-checks = true
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[profile.release]
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opt-level = 3
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lto = "thin"
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codegen-units = 1
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debug = false
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panic = "abort"
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overflow-checks = false
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strip = "symbols"
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@@ -0,0 +1,21 @@
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MIT License
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Copyright (c) 2024 Stephen Waits <steve@waits.net>
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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||||
copies or substantial portions of the Software.
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||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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@@ -0,0 +1,143 @@
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# bpe-tokenizer
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A Rust implementation of Byte Pair Encoding (BPE) tokenization. This crate
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provides functionality to tokenize text into subword units using pre-trained
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vocabularies. BPE is widely used in natural language processing (NLP) tasks,
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where it breaks down words into subword tokens using a vocabulary of the most
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frequent token pairs.
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It supports **Unicode-aware** text segmentation for sentence and word splitting,
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making it suitable for processing a variety of languages and scripts.
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## Features
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- **Bring your own BPE token vocabularies**, or use ...
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- **Pre-trained multilingual vocabularies** sourced from the [BPEmb](https://github.com/bheinzerling/bpemb) project, with support for tokenizing text in **275 languages**.
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- **Unicode-aware sentence and word segmentation**: Leveraging the [`unicode-segmentation`](https://docs.rs/unicode-segmentation) crate for proper text splitting.
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## Installation
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To add this crate to your project, run:
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```bash
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cargo add bpe-tokenizer
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```
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Or manually include it in your `Cargo.toml`:
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```toml
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[dependencies]
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bpe-tokenizer = "<version>"
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```
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## Full Example
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Here is an example of how to create a `BytePairEncoder` from a string and use it
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to tokenize text:
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```rust
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use bpe_tokenizer::{BytePairEncoder, BytePairEncoderError};
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let vocab = BytePairEncoder::new_from_str("hello\t1\nworld\t2").unwrap();
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let tokenized = vocab.tokenize("Hello, world!");
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println!("{:?}", tokenized);
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```
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The output will be a vector of tokens:
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```text
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["<s>", "▁hello", "▁world", "</s>"]
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```
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Or load a vocabulary from a file:
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```rust
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use bpe_tokenizer::{BytePairEncoder, BytePairEncoderError};
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let vocab = BytePairEncoder::new_from_file("path/to/file.vocab").unwrap();
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```
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## Cargo Features
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The crate also includes several sizes of default pre-trained vocabularies, which
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are **optional** and can be enabled via Cargo features. They are sourced from
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Wikipedia data, pre-trained as part of the
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[BPEmb](https://github.com/bheinzerling/bpemb) project. These MIT-licensed
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vocabularies support 275 languages and provide different sizes depending on
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usage needs:
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### Available Optional Features
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- **`default-small` (100,000 tokens)**: Suitable for memory-constrained environments.
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- **`default-medium` (320,000 tokens)**: Balances between token coverage and memory efficiency.
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- **`default-large` (1,000,000 tokens)**: Provides the most detailed token representations for high granularity tasks.
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### Enabling Optional Features
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To use these default vocabularies, specify the feature in your `Cargo.toml`:
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```toml
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[dependencies]
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bpe-tokenizer = { version = "<version>", features = ["default-medium"] }
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```
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### Example with `default-medium` Vocabulary
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An example of using the **medium** vocabulary (320,000 tokens):
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```rust
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# #[cfg(feature = "default-medium")] {
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use bpe_tokenizer::{BytePairEncoder, BytePairEncoderError};
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let encoder = BytePairEncoder::new_default_medium().unwrap();
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let tokenized = encoder.tokenize("This is a test sentence.");
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println!("{:?}", tokenized);
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// Output: ["<s>", "▁this", "▁is", "▁a", "▁test", "▁sentence", "</s>"]
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# }
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```
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## Tokenization Functions
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The crate provides various ways to interact with the tokenizer:
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- **Tokenize into a flat `Vec<String>`**:
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- `BytePairEncoder::tokenize`
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Splits and flattens the text into tokens.
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```rust
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let tokenized = vocab.tokenize("Example sentence.");
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// Output: ["<s>", "▁example", "▁sentence", "</s>"]
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```
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- **Tokenize into nested sentence vectors `Vec<Vec<String>>`**:
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- `BytePairEncoder::tokenize_sentences`
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Useful for processing multiple sentences separately.
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```rust
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let tokenized = vocab.tokenize_sentences("This is sentence one. And this is sentence two.");
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// Output: [["<s>", "▁this", "▁is", "▁sentence", "▁one", "</s>"], ["<s>", "▁and", "▁this", "▁is", "▁sentence", "▁two", "</s>"]]
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```
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- **Iterative tokenization**:
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- `BytePairEncoder::tokenize_iter` and `BytePairEncoder::tokenize_sentences_iter`
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Provides an iterator over generated tokens for better memory efficiency in
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large-scale text.
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```rust
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let tokens_iter: Vec<String> = vocab.tokenize_iter("Example sentence").collect();
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// Output: ["<s>", "▁example", "▁sentence", "</s>"]
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```
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## Licensing
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This crate is licensed under the [MIT License](LICENSE).
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## Contributing
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||||
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||||
Contributions are welcome! Please open an issue, submit a pull request, or reach
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||||
out if you'd like to contribute awesome new features or fixes to this crate.
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@@ -0,0 +1,75 @@
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#[cfg(any(
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feature = "default-small",
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feature = "default-medium",
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feature = "default-large"
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))]
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use lz4_flex::block::compress_prepend_size;
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#[cfg(any(
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feature = "default-small",
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feature = "default-medium",
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feature = "default-large"
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))]
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use std::{collections::HashMap, env, fs, path::PathBuf};
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fn main() {
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#[cfg(feature = "default-small")]
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process_vocab("multi.wiki.bpe.vs100000.vocab");
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#[cfg(feature = "default-medium")]
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process_vocab("multi.wiki.bpe.vs320000.vocab");
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#[cfg(feature = "default-large")]
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process_vocab("multi.wiki.bpe.vs1000000.vocab");
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}
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#[cfg(any(
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feature = "default-small",
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feature = "default-medium",
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feature = "default-large"
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))]
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fn process_vocab(name: &str) {
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// Path to the vocabulary file (ensure this path is correct)
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let vocab_path = PathBuf::from(format!("vocab/{}", name));
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// Load and parse the vocabulary into a HashMap.
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let tokens: HashMap<String, isize> = load_vocab_hashmap(&vocab_path);
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// Serialize the HashMap using bincode
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let serialized = bincode::serialize(&tokens).unwrap();
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// Compress the serialized data using zstd (with ultra compression level)
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let compressed = compress_prepend_size(&serialized);
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// Write the compressed data to a file in the build output directory
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let out_dir = PathBuf::from(env::var("OUT_DIR").unwrap());
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let output_path = out_dir.join(format!("{}.hashmap.bincode.lz4", name));
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fs::write(&output_path, compressed).unwrap();
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}
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#[cfg(any(
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feature = "default-small",
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feature = "default-medium",
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feature = "default-large"
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))]
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fn load_vocab_hashmap(path: &PathBuf) -> HashMap<String, isize> {
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// Read file's contents
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let content = fs::read_to_string(path).unwrap();
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let mut tokens = HashMap::new();
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// Process each line in the file, each being a token-score pair
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for line in content.lines() {
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let (token, score_str) = match line.split_once('\t') {
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Some(pair) => pair,
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None => panic!("Invalid line in vocabulary file: {}", line),
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||||
};
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let score = match score_str.parse::<isize>() {
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Ok(score) => score,
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Err(_) => panic!("Invalid score in vocabulary file: {}", line),
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||||
};
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tokens.insert(token.to_string(), score);
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}
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tokens
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}
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+1285
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Reference in new issue
Block a user