Improving README with links and quick start example

README.md

@@ -10,6 +10,39 @@ base_model:
 - Unbabel/XCOMET-XXL
 ---
 
-This is a distilled version of [`Unbabel/XCOMET-XXL`](https://huggingface.co/Unbabel/XCOMET-XXL) - a machine translation evaluation model trained to provide an overall quality score between 0 and 1, where 1 is perfect translation.
+# XCOMET-lite
 
-`XCOMET-lite` is based on [`microsoft/mdeberta-v3-base`](https://huggingface.co/microsoft/deberta-v3-base) and has 278 M parameters, which is ~38x smaller than 10.7 B `XCOMET-XXL`.
+**Links:** [EMNLP 2024](https://aclanthology.org/2024.emnlp-main.1223/) | [Arxiv](https://arxiv.org/abs/2406.14553) | [Github repository](https://github.com/NL2G/xCOMET-lite) 
+
+`XCOMET-lite` is a distilled version of [`Unbabel/XCOMET-XXL`](https://huggingface.co/Unbabel/XCOMET-XXL) — a machine translation evaluation model trained to provide an overall quality score between 0 and 1, where 1 represents a perfect translation.
+
+This model uses [`microsoft/mdeberta-v3-base`](https://huggingface.co/microsoft/deberta-v3-base) as its backbone and has 278 million parameters, making it approximately 38 times smaller than the 10.7 billion-parameter `XCOMET-XXL`.
+
+## Quick Start
+
+1. Clone the [GitHub repository](https://github.com/NL2G/xCOMET-lite).
+2. Create a conda environment as instructed in the README.
+
+Then, run the following code:
+
+```
+from xcomet.deberta_encoder import XCOMETLite
+
+model = XCOMETLite().from_pretrained("myyycroft/XCOMET-lite")
+data = [
+    {
+        "src": "Elon Musk has acquired Twitter and plans significant changes.",
+        "mt": "Илон Маск приобрел Twitter и планировал значительные искажения.",
+        "ref": "Илон Маск приобрел Twitter и планирует значительные изменения."
+    },
+    {
+        "src": "Elon Musk has acquired Twitter and plans significant changes.",
+        "mt": "Илон Маск приобрел Twitter.",
+        "ref": "Илон Маск приобрел Twitter и планирует значительные изменения."
+    }
+]
+
+model_output = model.predict(data, batch_size=2, gpus=1)
+
+print("Segment-level scores:", model_output.scores)
+```