Some tweaks at the wording (#1)

- Some tweaks at the wording (b386fca18bdc910c447391f8a7bc42ee16712401)


Co-authored-by: Gaël Varoquaux <GaelVaroquaux@users.noreply.huggingface.co>

README.md

@@ -10,12 +10,14 @@ tags:
 ## PEARL-small
 [Learning High-Quality and General-Purpose Phrase Representations](https://arxiv.org/pdf/2401.10407.pdf). <br>
 [Lihu Chen](https://chenlihu.com), [Gaël Varoquaux](https://gael-varoquaux.info/), [Fabian M. Suchanek](https://suchanek.name/). 
-<br> Accepted by EACL Findings 2024 <br>
+Accepted by EACL Findings 2024 <br>
 
-PEARL-small is a variant of [E5-small](https://huggingface.co/intfloat/e5-small-v2) finetuned on our constructed context-free [dataset](https://zenodo.org/records/10676475) to yield better representations 
+PEARL-small is a lightweight string embedding model. It is the tool of choice for semantic similarity computation for strings,
+creating excellent embeddings for string matching, entity retrieval, entity clustering, fuzzy join...
+<br>
+It differents from typically sentence embedders because it adds a character-level representation giving a good support for open vocabulary.
+The model is a variant of [E5-small](https://huggingface.co/intfloat/e5-small-v2) finetuned on our constructed context-free [dataset](https://zenodo.org/records/10676475) to yield better representations 
 for phrases and strings. <br>
-If you require semantic similarity computation for strings, our PEARL model might be a helpful tool.<br> 
-It offers powerful embeddings suitable for tasks like string matching, entity retrieval, entity clustering, and fuzzy join.
 
 🤗 [PEARL-small](https://huggingface.co/Lihuchen/pearl_small) 🤗 [PEARL-base](https://huggingface.co/Lihuchen/pearl_base)
 <br>