End of training

README.md

@@ -4,8 +4,6 @@ license: apache-2.0
 base_model: Qwen/Qwen3-0.6B-Base
 tags:
 - generated_from_trainer
-metrics:
-- accuracy
 model-index:
 - name: MNLP_M3_mcqa_model_3
   results: []
@@ -18,8 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2762
-- Accuracy: 0.8986
+- Loss: 0.2545
 
 ## Model description
 
@@ -45,25 +42,19 @@ The following hyperparameters were used during training:
 - gradient_accumulation_steps: 4
 - total_train_batch_size: 8
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
+- lr_scheduler_type: cosine
 - lr_scheduler_warmup_ratio: 0.01
 - num_epochs: 4
 
 ### Training results
 
-| Training Loss | Epoch  | Step  | Validation Loss | Accuracy |
-|:-------------:|:------:|:-----:|:---------------:|:--------:|
-| 0.2684        | 0.2278 | 1000  | 0.2629          | 0.8904   |
-| 0.2572        | 0.4555 | 2000  | 0.2585          | 0.8927   |
-| 0.2475        | 0.6833 | 3000  | 0.2496          | 0.8963   |
-| 0.2508        | 0.9111 | 4000  | 0.2467          | 0.8970   |
-| 0.199         | 1.1387 | 5000  | 0.2594          | 0.8981   |
-| 0.1927        | 1.3665 | 6000  | 0.2618          | 0.8987   |
-| 0.2051        | 1.5942 | 7000  | 0.2683          | 0.8995   |
-| 0.1988        | 1.8220 | 8000  | 0.2651          | 0.8989   |
-| 0.1797        | 2.0497 | 9000  | 0.2833          | 0.9      |
-| 0.1738        | 2.2774 | 10000 | 0.2861          | 0.8995   |
-| 0.1781        | 2.5052 | 11000 | 0.2762          | 0.8986   |
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.2526        | 0.2597 | 1000 | 0.2546          |
+| 0.2401        | 0.5194 | 2000 | 0.2429          |
+| 0.237         | 0.7791 | 3000 | 0.2330          |
+| 0.2227        | 1.0387 | 4000 | 0.2550          |
+| 0.1778        | 1.2984 | 5000 | 0.2545          |
 
 
 ### Framework versions