Update README.md

README.md

@@ -97,7 +97,7 @@ If you use this dataset in your research, please cite **all** the following work
 
 ## Task Overview
 
-This benchmark includes **eight primary tasks** for evaluating CPP models in crystal property prediction, divided into regression and classification missions.
+This benchmark includes **6 primary tasks** for evaluating CPP models in crystal property prediction, divided into regression and classification missions.
 
 ### **Mission Settings**
 
@@ -108,12 +108,12 @@ This benchmark includes **eight primary tasks** for evaluating CPP models in cry
 3. **T3**: Bulk Modulus Prediction (`float`) | `Gpa`
 4. **T4**: Shear Modulus Prediction (`float`) | `Gpa`
 5. **T5**: Young’s Modulus Prediction (`float`) | `Gpa`
-6. **T6**: Poisson’s Ratio Prediction (`float`) | `NAN`
+
 
 
 #### 🧮 Classification Task
 
-7. **T7**: Metal/Non-metal Classification (`int`, binary classification)
+7. **T6**: Metal/Non-metal Classification (`int`, binary classification)
 
 ---
 
@@ -135,7 +135,7 @@ The datasets are hosted on Hugging Face and stored in ASE database (`.db`) forma
 * **Train:** `MP_modulus_train.db` (6,631 crystals)
 * **Val:** `MP_modulus_val.db` (947 crystals)
 * **Test:** `MP_modulus_test.db` (1,895 crystals)
-* **Tasks:** T3–T6
+* **Tasks:** T3–T5
 
   * Keys: `bulk_modulus`, `shear_modulus`, `youngs_modulus`, `poissons_ratio`, 
 
@@ -144,7 +144,7 @@ The datasets are hosted on Hugging Face and stored in ASE database (`.db`) forma
 * **Train:** `MP_100_metal_train.db` (86,071 crystals)
 * **Val:** `MP_100_metal_val.db` (12,295 crystals)
 * **Test:** `MP_100_metal_test.db` (24,593 crystals)
-* **Task:** T7 (label: `0` or `1`)
+* **Task:** T6 (label: `0` or `1`)
   
   * Keys: `metal`
 ---