Model save

README.md

@@ -11,13 +11,13 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/amazingvince/huggingface/runs/213lyzv9)
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/amazingvince/huggingface/runs/atvo6d3z)
 # jina_embeddings_v2_base_code_multi_regression-simple
 
-This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-code](https://huggingface.co/jinaai/jina-embeddings-v2-base-code) on the amazingvince/the-stack-smol-xs-scored-and-annotated-all dataset.
+This model is a fine-tuned version of [jinaai/jina-embeddings-v2-base-code](https://huggingface.co/jinaai/jina-embeddings-v2-base-code) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6117
-- Mse: 0.6117
+- Loss: 0.6125
+- Mse: 0.6125
 
 ## Model description
 
@@ -41,22 +41,20 @@ The following hyperparameters were used during training:
 - eval_batch_size: 2
 - seed: 90085
 - distributed_type: multi-GPU
-- gradient_accumulation_steps: 16
-- total_train_batch_size: 32
+- gradient_accumulation_steps: 32
+- total_train_batch_size: 64
 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-09
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.05
-- num_epochs: 2.0
+- num_epochs: 3.0
 
 ### Training results
 
 | Training Loss | Epoch  | Step | Validation Loss | Mse    |
 |:-------------:|:------:|:----:|:---------------:|:------:|
-| 0.5055        | 0.3871 | 100  | 0.6117          | 0.6117 |
-| 0.5288        | 0.7743 | 200  | 0.6113          | 0.6113 |
-| 0.5981        | 1.1614 | 300  | 0.6117          | 0.6117 |
-| 0.6077        | 1.5485 | 400  | 0.6125          | 0.6125 |
-| 0.6943        | 1.9356 | 500  | 0.6117          | 0.6117 |
+| 0.5611        | 0.7743 | 100  | 0.6137          | 0.6137 |
+| 0.6542        | 1.5485 | 200  | 0.6139          | 0.6139 |
+| 0.5106        | 2.3228 | 300  | 0.6125          | 0.6125 |
 
 
 ### Framework versions