Update README.md

README.md

@@ -21,7 +21,8 @@ A flax-based MonoBERT cross encoder trained on the [Baidu-ULTR](https://arxiv.or
 |-----------------|----------------|--------|--------|--------|--------|---------|--------|
 | Pointwise Naive | 0.2272         | 1.6836 | 3.5616 | 4.8822 | 7.4244 | 0.3640  | 0.6096 |
 | Pointwise IPS   | 0.2436         | 0.8842 | 2.0510 | 2.9535 | 4.8816 | 0.2363  | 0.4472 |
-| **Naive Listwise**  | **0.7535**         | **1.9738** | **4.1609** | **5.6861** | **8.5432** | **0.4091**  | **0.6436** |
+| **Listwise Naive**  | **0.7535**         | **1.9738** | **4.1609** | **5.6861** | **8.5432** | **0.4091**  | **0.6436** |
+| Listwise IPS    | 1.2193         | 1.7466 | 3.6378 | 4.9797 | 7.5790 | 0.3665  | 0.6112 |
 
 ## Usage
 Here is an example of downloading the model and calling it for inference on a mock batch of input data. For more details on how to use the model on the Baidu-ULTR dataset, take a look at our [training](https://github.com/philipphager/baidu-bert-model/blob/main/main.py) and [evaluation scripts](https://github.com/philipphager/baidu-bert-model/blob/main/eval.py) in our code repository.
@@ -47,7 +48,7 @@ batch = {
         [2, 21448, 21874, 21436, 1, 16794, 4522, 2082],
         [2, 21448, 21874, 21436, 1, 20206, 10082, 9773],
         [2, 21448, 21874, 21436, 1, 2618, 8520, 2860],
-    ]),
+  ]),
     # Specify if a token id belongs to the query (0) or document (1)
     "token_types": jnp.array([
         [0, 0, 0, 0, 1, 1, 1, 1],