Update README.md

README.md

@@ -5,13 +5,13 @@ datasets:
 license: cc-by-nc-4.0
 pipeline_tag: sentence-similarity
 ---
-These are LoRA adaption weights for [mT5](https://huggingface.co/google/mt5-xxl) encoder.
+These are LoRA adaption weights for the [mT5](https://huggingface.co/google/mt5-xxl) encoder.
 
-## Multilingual Sentence T5
+## Multilingual Sentence T5 (m-ST5)
 This model is a multilingual extension of Sentence T5 and was created using the [mT5](https://huggingface.co/google/mt5-xxl) encoder. It is proposed in this [paper](https://arxiv.org/abs/2403.17528).
-It is an encoder for sentence embedding, and its performance has been verified in cross-lingual STS and sentence retrieval.
+m-ST5 is an encoder for sentence embedding, and its performance has been verified in cross-lingual semantic textual similarity (STS) and sentence retrieval tasks.
 
-### Traning Data
+### Training Data
 The model was trained on the XNLI dataset.
 
 ### Framework versions
@@ -19,7 +19,7 @@ The model was trained on the XNLI dataset.
 
 - PEFT 0.4.0.dev0
 
-## Hot to use
+## How to use
 0. If you have not installed peft, please do so.
 ```
 pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git
@@ -34,7 +34,7 @@ model.enable_input_require_grads()
 model.gradient_checkpointing_enable()
 model: PeftModel = PeftModel.from_pretrained(model, "pkshatech/m-ST5")
 ```
-2. To obtain sentence embedding, use the mean pooling.
+2. To obtain sentence embedding, use mean pooling.
 ```
 tokenizer = AutoTokenizer.from_pretrained("google/mt5-xxl", use_fast=False)
 model.eval()