Update README.md

README.md

@@ -1,17 +1,21 @@
 ## Typesense Public Embedding Models
-We store our current supported embedding models in this repo and you can also convert your own models to ONNX format and create a PR to add it to our supported models list.
+We maintain a repository of currently supported embedding models, and we welcome contributions from the community. If you have a model that you would like to add to our supported list, you can convert it to the ONNX format and create a Pull Request (PR) to include it.
 
 ### Convert a model to ONNX format
 
 #### Converting a Hugging Face Transformers Model
-You can follow instructions from [this link](https://huggingface.co/docs/transformers/serialization#export-to-onnx) to convert any model from Hugging Face to ONNX format using ```optimum-cli```.
+To convert any model from Hugging Face to ONNX format, you can follow the instructions in [this link](https://huggingface.co/docs/transformers/serialization#export-to-onnx) using the ```optimum-cli```.
 #### Converting a PyTorch Model
-You can use ```torch.onnx``` [APIs](https://pytorch.org/docs/stable/onnx.html) to convert PyTorch models to ONNX.
+If you have a PyTorch model, you can use the ```torch.onnx``` APIs to convert it to the ONNX format. More information on the conversion process can be found  [here](https://pytorch.org/docs/stable/onnx.html).
 #### Converting a Tensorflow Model
-You can use ```tf2onnx``` [tool](https://onnxruntime.ai/docs/tutorials/tf-get-started.html#getting-started-converting-tensorflow-to-onnx) to convert Tensorflow models to ONNX. 
+For Tensorflow models, you can utilize the tf2onnx tool to convert them to the ONNX format. Detailed guidance on this conversion can be found [here](https://onnxruntime.ai/docs/tutorials/tf-get-started.html#getting-started-converting-tensorflow-to-onnx). 
 
 ### Creating model config 
-Before creating a PR with your ONNX model, you should store model file, vocab file and model config file under a folder with model name. Your model config must be named as ```config.json``` and should contain those keys:
+Before submitting your ONNX model through a PR, you need to organize the necessary files under a folder with the model's name. Ensure that your model configuration adheres to the following structure:
+
+  - **Model File**: The ONNX model file.
+  - **Vocab File**: The vocabulary file required for the model.
+  - **Model Config File**: Named as config.json, this file should contain the following keys:
 | Key | Description | Optional |
 |-----|-------------|----------|
 |model_md5| MD5 checksum of model file as string| No |
@@ -20,3 +24,8 @@ Before creating a PR with your ONNX model, you should store model file, vocab fi
 |vocab_file_name| File name of vocab file| No |
 |indexing_prefix| Prefix to be added before embedding documents| Yes |
 |query_prefix| Prefix to be added before embedding queries | Yes |
+
+
+Please make sure that the information in the configuration file is accurate and complete before submitting your PR.
+
+We appreciate your contributions to expand our collection of supported embedding models!