Update README.md

README.md

@@ -60,7 +60,7 @@ The SWE-bench Extra dataset supports the development of software engineering age
 
 For a more detailed description of the data collection process, please refer to our blog post. [link]
 
-As an example use case of this dataset, we’ve used SWE-bench-extra instances to generate a dataset of 84,480 trajectories [`nebius/swe-agent-trajectories`](https://huggingface.co/datasets/nebius/swe-agent-trajectories). We’ve then trained an action generator model, that achieves a score of 19.2% on the subset of 50 random instances from the SWE-bench Verified benchmark, representing a 30% relative improvement over its parent model Qwen2.5-72B-Instruct, which scored 14.8%. Further augmenting the action generator with a guided search based on a critic model, also trained on this data, achieves 40.6% on the full SWE-bench Verified benchmark, which is state-of-the-art among agents using solely open-weight models. You can read more about this agent in our blog post, [“Leveraging Training and Search for Better Software Engineering Agents”](https://nebius.com/blog/posts/training-and-search-for-software-engineering-agents).
+As an example use case of this dataset, we’ve used SWE-bench-extra instances to generate a dataset of 80,036 trajectories [`nebius/swe-agent-trajectories`](https://huggingface.co/datasets/nebius/swe-agent-trajectories). We’ve then trained an action generator model, that achieves a score of 19.2% on the subset of 50 random instances from the SWE-bench Verified benchmark, representing a 30% relative improvement over its parent model Qwen2.5-72B-Instruct, which scored 14.8%. Further augmenting the action generator with a guided search based on a critic model, also trained on this data, achieves 40.6% on the full SWE-bench Verified benchmark, which is state-of-the-art among agents using solely open-weight models. You can read more about this agent in our blog post, [“Leveraging Training and Search for Better Software Engineering Agents”](https://nebius.com/blog/posts/training-and-search-for-software-engineering-agents).
 
 # How to Use
 
@@ -100,5 +100,6 @@ The dataset contains the following fields. It includes all fields from SWE-bench
 | `FAIL_TO_PASS`             | str    | A JSON list of strings that represent the set of tests resolved by the PR and tied to the issue resolution. |
 | `PASS_TO_PASS`             | str    | A JSON list of strings that represent tests that should pass before and after the PR application. |
 | `meta`                     | str    | A JSON dictionary indicating whether the instance is lite, along with a list of failed lite validators if it is not. |
+| `license`                  | str    | The type of license of the repository. |
 
 To execute instances within SWE-bench, you need to provide a default recipe for dependency installation. The constants required for running these instances are described in this [constants.py](https://huggingface.co/datasets/nebius/SWE-bench-extra/blob/main/constants.py).