Model save

README.md

@@ -1,20 +1,20 @@
 ---
-base_model: dccuchile/distilbert-base-spanish-uncased
+base_model: dccuchile/albert-base-spanish
 tags:
 - generated_from_trainer
 model-index:
-- name: distilbert-base-spanish-uncased-2023-11-13-18-01
+- name: albert-base-spanish-2023-11-13-19-24
   results: []
 ---
 
 <!-- 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. -->
 
-# distilbert-base-spanish-uncased-2023-11-13-18-01
+# albert-base-spanish-2023-11-13-19-24
 
-This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on the None dataset.
+This model is a fine-tuned version of [dccuchile/albert-base-spanish](https://huggingface.co/dccuchile/albert-base-spanish) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.3884
+- Loss: 1.4313
 
 ## Model description
 
@@ -45,22 +45,22 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| 2.5316        | 0.59  | 500  | 2.2591          |
-| 2.2197        | 1.19  | 1000 | 1.9800          |
-| 2.0108        | 1.78  | 1500 | 1.8304          |
-| 1.855         | 2.38  | 2000 | 1.7229          |
-| 1.7933        | 2.97  | 2500 | 1.6561          |
-| 1.7466        | 3.56  | 3000 | 1.6101          |
-| 1.6906        | 4.16  | 3500 | 1.5588          |
-| 1.6301        | 4.75  | 4000 | 1.5264          |
-| 1.6131        | 5.34  | 4500 | 1.4893          |
-| 1.5731        | 5.94  | 5000 | 1.4626          |
-| 1.5248        | 6.53  | 5500 | 1.4445          |
-| 1.509         | 7.13  | 6000 | 1.4274          |
-| 1.5083        | 7.72  | 6500 | 1.4109          |
-| 1.5277        | 8.31  | 7000 | 1.4051          |
-| 1.4884        | 8.91  | 7500 | 1.3872          |
-| 1.496         | 9.5   | 8000 | 1.3912          |
+| 1.9328        | 0.59  | 500  | 1.9145          |
+| 1.863         | 1.19  | 1000 | 1.7882          |
+| 1.7407        | 1.78  | 1500 | 1.7241          |
+| 1.648         | 2.38  | 2000 | 1.6651          |
+| 1.606         | 2.97  | 2500 | 1.6102          |
+| 1.5833        | 3.56  | 3000 | 1.5912          |
+| 1.5663        | 4.16  | 3500 | 1.5642          |
+| 1.5104        | 4.75  | 4000 | 1.5390          |
+| 1.5252        | 5.34  | 4500 | 1.5197          |
+| 1.4676        | 5.94  | 5000 | 1.4950          |
+| 1.4502        | 6.53  | 5500 | 1.4766          |
+| 1.4336        | 7.13  | 6000 | 1.4694          |
+| 1.4355        | 7.72  | 6500 | 1.4527          |
+| 1.457         | 8.31  | 7000 | 1.4403          |
+| 1.4219        | 8.91  | 7500 | 1.4380          |
+| 1.4503        | 9.5   | 8000 | 1.4313          |
 
 
 ### Framework versions