Update README.md

README.md

@@ -134,18 +134,18 @@ Additionally, it features [LoRA](https://arxiv.org/abs/2106.09685) adapters to g
 ### Key Features:
 - **Extended Sequence Length:** Supports up to 8192 tokens with RoPE.
 - **Task-Specific Embedding:** Customize embeddings through the `task_type` argument with the following options:
-    - `retrieval.query`: Query encoding for asymmetric retrieval tasks
-    - `retrieval.passage`: Passage encoding for asymmetric retrieval tasks
-    - `separation`: For clustering and re-ranking applications
-    - `classification`: For classification tasks
-    - `text-matching`: For measuring textual similarity
+    - `retrieval.query`: Used for query embeddings in asymmetric retrieval tasks
+    - `retrieval.passage`: Used for passage embeddings in asymmetric retrieval tasks
+    - `separation`: Used for embeddings in clustering and re-ranking applications
+    - `classification`: Used for embeddings in classification tasks
+    - `text-matching`: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks
 - **Matryoshka Embeddings**: Supports flexible embedding sizes (`32, 64, 128, 256, 512, 768, 1024`), allowing for truncating embeddings to fit your application.
 
 ### Model Lineage:
 
 `jina-embeddings-v3` builds upon the [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) model, which was originally trained on 100 languages. 
 We extended its capabilities with an extra pretraining phase on the [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) dataset, 
-then contrastively fine-tuned it on 30 languages for enhanced performance in both monolingual and cross-lingual setups.
+then contrastively fine-tuned it on 30 languages for enhanced performance on embedding tasks in both monolingual and cross-lingual setups.
 
 ### Supported Languages:
 While the base model supports 100 languages, we've focused our tuning efforts on the following 30 languages to maximize performance: