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[MINOR:TYPO] Update README.md
cakiki Jun 15, 2023
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[MINOR:TYPO] Update README.md
cakiki Jun 15, 2023
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -51,7 +51,7 @@ We have precompiled some popular models and listed with the source code referenc
| roberta.bin | Byte-BPE tokenization model for Roberta model | byte BPE | [src](https://github.com/microsoft/BlingFire/tree/master/ldbsrc/roberta) |
| syllab.bin | Multi lingual model to identify allowed hyphenation points inside a word. | W2H | [src](https://github.com/microsoft/BlingFire/tree/master/ldbsrc/syllab) |

Oh yes, it is also the fastest! We did a comparison of Bling Fire with tokenizers from Hugging Face, [Bling Fire runs 4-5 times faster than Hugging Face Tokenizers](https://github.com/Microsoft/BlingFire/wiki/Comparing-performance-of-Bling-Fire-and-Hugging-Face-Tokenizers), see also [Bing Blog Post](https://blogs.bing.com/Developers-Blog/march-2020/Bling-FIRE-Tokenizer-for-BERT). We did comparison of Bling Fire Unigram LM and BPE implementaion to the same one in [SentencePiece](https://github.com/google/sentencepiece) library and our implementation is ~2x faster, see [XLNET benchmark](https://github.com/microsoft/BlingFire/blob/master/ldbsrc/xlnet/README.TXT) and [BPE benchmark](https://github.com/microsoft/BlingFire/blob/master/ldbsrc/bpe_example/README.TXT). Not to mention our default models are 10x faster than the same functionality from [SpaCy](https://github.com/explosion/spaCy), see [benchmark wiki](https://github.com/Microsoft/BlingFire/wiki/Benchmark-Guide) and this [Bing Blog Post](https://blogs.bing.com/Developers-Blog/2019-04/bling-fire-tokenizer-released-to-open-source).
Oh yes, it is also the fastest! We did a comparison of Bling Fire with tokenizers from Hugging Face, [Bling Fire runs 4-5 times faster than Hugging Face Tokenizers](https://github.com/Microsoft/BlingFire/wiki/Comparing-performance-of-Bling-Fire-and-Hugging-Face-Tokenizers), see also [Bing Blog Post](https://blogs.bing.com/Developers-Blog/march-2020/Bling-FIRE-Tokenizer-for-BERT). We did comparison of Bling Fire Unigram LM and BPE implementation to the same one in [SentencePiece](https://github.com/google/sentencepiece) library and our implementation is ~2x faster, see [XLNET benchmark](https://github.com/microsoft/BlingFire/blob/master/ldbsrc/xlnet/README.TXT) and [BPE benchmark](https://github.com/microsoft/BlingFire/blob/master/ldbsrc/bpe_example/README.TXT). Not to mention our default models are 10x faster than the same functionality from [SpaCy](https://github.com/explosion/spaCy), see [benchmark wiki](https://github.com/Microsoft/BlingFire/wiki/Benchmark-Guide) and this [Bing Blog Post](https://blogs.bing.com/Developers-Blog/2019-04/bling-fire-tokenizer-released-to-open-source).

So if low latency inference is what you need then you have to try Bling Fire!

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2 changes: 1 addition & 1 deletion dist-pypi/README.md
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Expand Up @@ -45,7 +45,7 @@ We have precompiled some popular models and listed with the source code referenc
| roberta.bin | Byte-BPE tokenization model for Roberta model | byte BPE | [src](https://github.com/microsoft/BlingFire/tree/master/ldbsrc/roberta) |
| syllab.bin | Multi lingual model to identify allowed hyphenation points inside a word. | W2H | [src](https://github.com/microsoft/BlingFire/tree/master/ldbsrc/syllab) |

Oh yes, it is also the fastest! We did a comparison of Bling Fire with tokenizers from Hugging Face, [Bling Fire runs 4-5 times faster than Hugging Face Tokenizers](https://github.com/Microsoft/BlingFire/wiki/Comparing-performance-of-Bling-Fire-and-Hugging-Face-Tokenizers), see also [Bing Blog Post](https://blogs.bing.com/Developers-Blog/march-2020/Bling-FIRE-Tokenizer-for-BERT). We did comparison of Bling Fire Unigram LM and BPE implementaion to the same one in [SentencePiece](https://github.com/google/sentencepiece) library and our implementation is ~2x faster, see [XLNET benchmark](https://github.com/microsoft/BlingFire/blob/master/ldbsrc/xlnet/README.TXT) and [BPE benchmark](https://github.com/microsoft/BlingFire/blob/master/ldbsrc/bpe_example/README.TXT). Not to mention our default models are 10x faster than the same functionality from [SpaCy](https://github.com/explosion/spaCy), see [benchmark wiki](https://github.com/Microsoft/BlingFire/wiki/Benchmark-Guide) and this [Bing Blog Post](https://blogs.bing.com/Developers-Blog/2019-04/bling-fire-tokenizer-released-to-open-source).
Oh yes, it is also the fastest! We did a comparison of Bling Fire with tokenizers from Hugging Face, [Bling Fire runs 4-5 times faster than Hugging Face Tokenizers](https://github.com/Microsoft/BlingFire/wiki/Comparing-performance-of-Bling-Fire-and-Hugging-Face-Tokenizers), see also [Bing Blog Post](https://blogs.bing.com/Developers-Blog/march-2020/Bling-FIRE-Tokenizer-for-BERT). We did comparison of Bling Fire Unigram LM and BPE implementation to the same one in [SentencePiece](https://github.com/google/sentencepiece) library and our implementation is ~2x faster, see [XLNET benchmark](https://github.com/microsoft/BlingFire/blob/master/ldbsrc/xlnet/README.TXT) and [BPE benchmark](https://github.com/microsoft/BlingFire/blob/master/ldbsrc/bpe_example/README.TXT). Not to mention our default models are 10x faster than the same functionality from [SpaCy](https://github.com/explosion/spaCy), see [benchmark wiki](https://github.com/Microsoft/BlingFire/wiki/Benchmark-Guide) and this [Bing Blog Post](https://blogs.bing.com/Developers-Blog/2019-04/bling-fire-tokenizer-released-to-open-source).

So if low latency inference is what you need then you have to try Bling Fire!

Expand Down