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https://github.com/quickwit-oss/whichlang
A blazingly fast and lightweight language detection library for Rust
https://github.com/quickwit-oss/whichlang
language-detection natural-language-processing rust-lang
Last synced: 4 days ago
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A blazingly fast and lightweight language detection library for Rust
- Host: GitHub
- URL: https://github.com/quickwit-oss/whichlang
- Owner: quickwit-oss
- License: mit
- Created: 2023-03-07T05:32:29.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-07-25T15:13:14.000Z (over 1 year ago)
- Last Synced: 2024-04-25T22:21:26.852Z (7 months ago)
- Topics: language-detection, natural-language-processing, rust-lang
- Language: Rust
- Homepage:
- Size: 474 KB
- Stars: 341
- Watchers: 17
- Forks: 12
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Whichlang
This is a language detection library, aiming for both precision and performance.
# Why build this?
While building [Quickwit](https://github.com/quickwit-oss/quickwit), a search engine tailored for log and tracing data, we found ourselves needing a light, fast, and precise language detection library in Rust that works well with our high throughput requirement. The full story and how it works are detailed in this [blog post](https://quickwit.io/blog/whichlang-language-detection-library).
# Features
- No dependency
- Throughput above 100 MB/s for short and long strings.
- Good accuracy (99.5% on my validation dataset, but it really depends on the size of your input.)
- Supported languages: Arabic, Dutch, English, French, German, Hindi, Italian, Japanese, Korean, Mandarin, Portuguese, Russian, Spanish, Swedish, Turkish, and Vietnamese.
# How does it work?
It uses a multiclass logistic regression model over:
- 2, 3, 4-grams of letters on ASCII
- codepoint / 128
- a slightly smarter projection of codepoints over a given class.
We use the hashing trick and project these features over a space of size `4_096`.
The logistic regression is trained in the python notebook attached,
and used to generate `weight.rs`.
# Comparison with [Whatlang](https://github.com/greyblake/whatlang-rs)
The following compares the throughput using the simple benchmark found in this repository and the accuracy using [whatlang-accuracy-benchmark](https://github.com/evanxg852000/whatlang-accuracy-benchmark) benchmark. Overall, Whichlang is about 10x faster and slightly more accurate than Whatlang.
### Throughput
To generate the throughput benchmark, we ported the benchmark available in [this repository](https://github.com/quickwit-oss/whichlang/blob/main/benches/bench.rs). Please, check this [repository](https://github.com/evanxg852000/whatlang-accuracy-benchmark) to see our changes.
| | Processing Time (µs) | Throughput (MiB/s) |
| ------------------------- | -------------------- | ------------------ |
| whatlang/short | 16.62 | 1.66 |
| whatlang/long | 62.00 | 9.42 |
| whichlang/short | 0.26 | 105.69 |
| whichlang/long | 5.21 | 112.31 |
### Accuracy
To generate the accuracy benchmark, we have changed the [whatlang-accuracy-benchmark](https://github.com/whatlang/whatlang-accuracy-benchmark) to add support for Whichlang. Given that Whatlang supports more languages, we have used its FilterList feature to restrict its analysis to only languages that are supported in Whichlang. We also use the `trigram` method in Whatlang. Please, check this [repository](https://github.com/evanxg852000/whatlang-accuracy-benchmark) to see our changes.
```
Crate: Whatlang
AVG: 91.69%
| LANG | AVG | <= 20 | 21-50 | 51-100 | > 100 |
|------------|--------|---------|--------|--------|---------|
| Arabic | 99.68% | 99.51% | 99.64% | 99.83% | 99.76% |
| Mandarin | 96.09% | 97.54% | 96.92% | 95.45% | 94.43% |
| German | 88.57% | 70.00% | 88.53% | 96.61% | 99.16% |
| English | 85.99% | 57.82% | 88.37% | 97.97% | 99.78% |
| French | 90.88% | 72.84% | 92.51% | 98.54% | 99.65% |
| Hindi | 99.80% | 100.00% | 99.83% | 99.78% | 99.61% |
| Italian | 87.75% | 66.67% | 87.74% | 97.04% | 99.54% |
| Japanese | 94.37% | 93.97% | 96.04% | 94.30% | 93.18% |
| Korean | 99.17% | 98.88% | 99.69% | 99.44% | 98.66% |
| Dutch | 89.68% | 72.13% | 89.78% | 97.40% | 99.40% |
| Portuguese | 88.08% | 72.90% | 85.76% | 95.22% | 98.44% |
| Russian | 99.98% | 100.00% | 99.96% | 99.98% | 100.00% |
| Spanish | 82.91% | 55.45% | 82.24% | 94.85% | 99.10% |
| Swedish | 84.16% | 58.33% | 83.78% | 96.35% | 98.18% |
| Turkish | 86.73% | 61.01% | 88.94% | 97.32% | 99.63% |
| Vietnamese | 93.23% | 82.84% | 92.96% | 97.88% | 99.24% |
| AVG | 91.69% | 78.74% | 92.04% | 97.37% | 98.61% |
```
```
Crate: Whichlang
AVG: 97.03%
| LANG | AVG | <= 20 | 21-50 | 51-100 | > 100 |
|------------|---------|---------|---------|---------|---------|
| Arabic | 100.00% | 100.00% | 100.00% | 100.00% | 100.00% |
| Mandarin | 98.65% | 98.69% | 98.48% | 98.55% | 98.87% |
| German | 94.20% | 80.00% | 97.47% | 99.49% | 99.84% |
| English | 97.15% | 91.84% | 97.25% | 99.57% | 99.93% |
| French | 97.59% | 93.83% | 97.61% | 99.20% | 99.71% |
| Hindi | 100.00% | 100.00% | 100.00% | 100.00% | 100.00% |
| Italian | 97.20% | 93.06% | 97.33% | 98.85% | 99.57% |
| Japanese | 94.92% | 88.95% | 95.14% | 97.74% | 97.85% |
| Korean | 99.83% | 99.44% | 99.98% | 99.97% | 99.94% |
| Dutch | 97.08% | 92.84% | 96.98% | 98.91% | 99.60% |
| Portuguese | 94.07% | 83.87% | 94.89% | 98.18% | 99.36% |
| Russian | 99.92% | 99.69% | 99.99% | 100.00% | 100.00% |
| Spanish | 92.12% | 76.36% | 93.78% | 98.65% | 99.70% |
| Swedish | 95.37% | 90.28% | 94.94% | 97.76% | 98.51% |
| Turkish | 95.51% | 88.24% | 98.11% | 98.38% | 97.33% |
| Vietnamese | 98.79% | 96.57% | 98.87% | 99.77% | 99.96% |
| AVG | 97.03% | 92.10% | 97.55% | 99.06% | 99.39% |
```