charabia 0.5.0

A simple library to detect the language, tokenize the text and normalize the tokens
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# Charabia
Library used by Meilisearch to tokenize queries and documents

## Role

The tokenizer’s role is to take a sentence or phrase and split it into smaller units of language, called tokens. It finds and retrieves all the words in a string based on the language’s particularities.

## Details

Charabia provides a simple API to segment, normalize, or tokenize (segment + normalize) a text of a specific language by detecting its Script/Language and choosing the specialized pipeline for it.

## Supported languages

**Charabia is multilingual**, featuring optimized support for:


|  Script - Language  |                           specialized segmentation                            | specialized normalization | Segmentation Performance level | Tokenization Performance level |
|---------------------|-------------------------------------------------------------------------------|---------------------------|-------------------|---|
| **Latin** - **Any** | ✅ [unicode-segmentation](https://github.com/unicode-rs/unicode-segmentation) | ✅ lowercase + deunicode            | 🟨 ~12MiB/sec    | 🟧 ~5MiB/sec    |
| **Chinese** - **CMN** 🇨🇳 | ✅ [jieba](https://github.com/messense/jieba-rs) | ✅ traditional-to-simplified conversion | 🟨 ~9MiB/sec    | 🟧 ~4MiB/sec    |
| **Hebrew** 🇮🇱 | ✅ [unicode-segmentation](https://github.com/messense/jieba-rs) | ✅ diacritics removal  | 🟩 ~21MiB/sec    | 🟨 ~9MiB/sec    |
| **Japanese** 🇯🇵 | ✅ [lindera](https://github.com/lindera-morphology/lindera) | ❌ | 🟧 ~3MiB/sec    | 🟧 ~3MiB/sec    |

We aim to provide global language support, and your feedback helps us [move closer to that goal](https://docs.meilisearch.com/learn/advanced/language.html#improving-our-language-support). If you notice inconsistencies in your search results or the way your documents are processed, please open an issue on our [GitHub repository](https://github.com/meilisearch/charabia/issues/new/choose).

If you have a particular need that charabia does not support, please share it in the product repository by creating a [dedicated discussion](https://github.com/meilisearch/product/discussions?discussions_q=label%3Aproduct%3Acore%3Atokenizer).

### About Performance level

Performances are based on the throughput (MiB/sec) of the tokenizer (computed on a [scaleway Elastic Metal server EM-A410X-SSD](https://www.scaleway.com/en/pricing/) - CPU: Intel Xeon E5 1650 - RAM: 64 Go) using jemalloc:
- 0️⃣⬛️:  0  ->  1  MiB/sec
- 1️⃣🟥:  1  ->  3  MiB/sec
- 2️⃣🟧:  3  ->  8  MiB/sec
- 3️⃣🟨:  8  -> 20  MiB/sec
- 4️⃣🟩: 20  -> 50  MiB/sec
- 5️⃣🟪: 50 MiB/sec or more

## Examples

#### Tokenization

```rust
use charabia::Tokenize;

let orig = "Thé quick (\"brown\") fox can't jump 32.3 feet, right? Brr, it's 29.3°F!";

// tokenize the text.
let mut tokens = orig.tokenize();

let token = tokens.next().unwrap();
// the lemma into the token is normalized: `Thé` became `the`.
assert_eq!(token.lemma(), "the");
// token is classfied as a word
assert!(token.is_word());

let token = tokens.next().unwrap();
assert_eq!(token.lemma(), " ");
// token is classfied as a separator
assert!(token.is_separator());
```

#### Segmentation

```rust
use charabia::Segment;

let orig = "The quick (\"brown\") fox can't jump 32.3 feet, right? Brr, it's 29.3°F!";

// segment the text.
let mut segments = orig.segment_str();

assert_eq!(segments.next(), Some("The"));
assert_eq!(segments.next(), Some(" "));
assert_eq!(segments.next(), Some("quick"));
```