Update README.md

README.md

@@ -2,18 +2,33 @@
 license: apache-2.0
 ---
 
-# Deita-Quality-Scorer
+# Model Card for Deita Quality Scorer
 
-Deita-Quality-Scorer is a tool for automatically annotating the Instruction Quality of SFT data.
+Deita is an open-sourced project designed to facilitate Automatic Data Selection for instruction tuning in Large Language Models (LLMs).
 
+Deita Quality Scorer is a tool for automatically annotating the Instruction Quality of SFT data.
 
-## Uses
+## Model description
+
+- **Model type:** Model fine tuned to automatically annotate the Instruction-Response Pair Quality
+- **Language(s) (NLP):** Primarily English
+- **Finetuned from model:** Llama-1-13b-hf
+
+### Model Sources
+
+- **Repository:** https://github.com/hkust-nlp/deita
+- **Model Family:** Other models and the dataset are found in the [Deita collection](https://huggingface.co/collections/hkust-nlp/deita-6569c198c174808d94cf5bd4).
+
+
+## Usage
+
+Please use the following format
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import numpy as np
 from scipy.special import softmax
-model_name = "hkust-nlp/Deita-Quality-Scorer"
+model_name = "hkust-nlp/deita-quality-scorer"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
@@ -44,8 +59,8 @@ def infer_Quality(model, tokenizer, input_text, resp_text):
 	score_npy = np.sum(score_npy, axis=0)
 	return score_npy
 
-input_text = "word to describe UI with helpful tooltips"
-output_text = "User-friendly or intuitive UI"
+input_text = "word to describe UI with helpful tooltips" # Example Input
+output_text = "User-friendly or intuitive UI" # Example Output
 quality_score = infer_quality(model, tokenizer, input_text)
 
 print(quality_score)