Pringled commited on
Commit
e58e15f
Β·
verified Β·
1 Parent(s): da11bbe

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +5 -4
README.md CHANGED
@@ -9,18 +9,20 @@ pinned: false
9
 
10
  ## Hello, we're Minish!
11
 
12
- We're a two-person ([@pringled](https://github.com/Pringled) and [@stephantul](https://github.com/stephantul)) open-source lab, with a focus on Natural Language Processing.
 
 
13
 
14
  We believe that if you make models fast enough, you unlock new possibilities.
15
 
16
- Using our software, you can:
17
  * Embed the entire English Wikipedia in 5 minutes
18
  * Classify tens of thousands of documents per second on a CPU
19
  * Approximately deduplicate extremely large datasets in minutes
20
  * Build the fastest RAG application in the world
21
  * Easily evaluate which ANN algorithm works best for your data
22
 
23
- Our projects:
24
 
25
  * [model2vec](https://github.com/MinishLab/model2vec): tiny static embedding models with state-of-the-art performance.
26
  * [potion](https://huggingface.co/collections/minishlab/potion-6721e0abd4ea41881417f062): the best small models in the world. 100-500x faster than a sentence-transformer, and almost as good.
@@ -29,7 +31,6 @@ Our projects:
29
  * [model2vec-rs](https://github.com/MinishLab/model2vec-rs): a Rust port of model2vec.
30
 
31
 
32
-
33
  You can also find us on:
34
  πŸ”¬ [GitHub](https://github.com/MinishLab)
35
  πŸ‘½ [LinkedIn](https://www.linkedin.com/company/minish-lab/)
 
9
 
10
  ## Hello, we're Minish!
11
 
12
+ ### About us
13
+ We're an open-source lab, with a focus on Natural Language Processing. Minish is currently maintained by [@pringled](https://github.com/Pringled).
14
+ The lab was originally founded by [@pringled](https://github.com/Pringled) and [@stephantul](https://github.com/stephantul).
15
 
16
  We believe that if you make models fast enough, you unlock new possibilities.
17
 
18
+ Using our models and packages, you can:
19
  * Embed the entire English Wikipedia in 5 minutes
20
  * Classify tens of thousands of documents per second on a CPU
21
  * Approximately deduplicate extremely large datasets in minutes
22
  * Build the fastest RAG application in the world
23
  * Easily evaluate which ANN algorithm works best for your data
24
 
25
+ ### Our projects:
26
 
27
  * [model2vec](https://github.com/MinishLab/model2vec): tiny static embedding models with state-of-the-art performance.
28
  * [potion](https://huggingface.co/collections/minishlab/potion-6721e0abd4ea41881417f062): the best small models in the world. 100-500x faster than a sentence-transformer, and almost as good.
 
31
  * [model2vec-rs](https://github.com/MinishLab/model2vec-rs): a Rust port of model2vec.
32
 
33
 
 
34
  You can also find us on:
35
  πŸ”¬ [GitHub](https://github.com/MinishLab)
36
  πŸ‘½ [LinkedIn](https://www.linkedin.com/company/minish-lab/)