update model card README.md

README.md

@@ -10,16 +10,19 @@ model-index:
   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. -->
+
 # roberta-finetuned-CPV_Spanish
 
-This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the a dataset derived from Spanish Public Procurement documents from 2019. The whole fine-tuning process is available in the following [Kaggle notebook](https://www.kaggle.com/code/marianavasloro/fine-tuned-roberta-for-spanish-cpv-codes).
+This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0417
-- F1: 0.7757
-- Roc Auc: 0.8684
-- Accuracy: 0.7223
-- Coverage Error: 11.7873
-- Label Ranking Average Precision Score: 0.7728
+- Loss: 0.0152
+- F1: 0.9462
+- Roc Auc: 0.9698
+- Accuracy: 0.9297
+- Coverage Error: 3.6573
+- Label Ranking Average Precision Score: 0.9451
 
 ## Model description
 
@@ -48,18 +51,18 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | F1     | Roc Auc | Accuracy | Coverage Error | Label Ranking Average Precision Score |
-|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|:--------------:|:-------------------------------------:|
-| 0.0582        | 1.0   | 2039  | 0.0554          | 0.6291 | 0.7463  | 0.5235   | 21.9642        | 0.5547                                |
-| 0.0413        | 2.0   | 4078  | 0.0437          | 0.7054 | 0.7959  | 0.6239   | 17.5374        | 0.6589                                |
-| 0.0295        | 3.0   | 6117  | 0.0403          | 0.7391 | 0.8285  | 0.6788   | 14.7700        | 0.7197                                |
-| 0.022         | 4.0   | 8156  | 0.0390          | 0.7562 | 0.8414  | 0.6987   | 13.8217        | 0.7425                                |
-| 0.0168        | 5.0   | 10195 | 0.0393          | 0.7600 | 0.8547  | 0.7007   | 12.8532        | 0.7542                                |
-| 0.0127        | 6.0   | 12234 | 0.0396          | 0.7645 | 0.8606  | 0.7099   | 12.3890        | 0.7622                                |
-| 0.0094        | 7.0   | 14273 | 0.0406          | 0.7642 | 0.8675  | 0.7027   | 11.8679        | 0.7628                                |
-| 0.0066        | 8.0   | 16312 | 0.0404          | 0.7706 | 0.8641  | 0.7173   | 12.0876        | 0.7681                                |
-| 0.0052        | 9.0   | 18351 | 0.0411          | 0.7748 | 0.8679  | 0.7182   | 11.8149        | 0.7705                                |
-| 0.0042        | 10.0  | 20390 | 0.0417          | 0.7757 | 0.8684  | 0.7223   | 11.7873        | 0.7728                                |
+| Training Loss | Epoch | Step   | Validation Loss | F1     | Roc Auc | Accuracy | Coverage Error | Label Ranking Average Precision Score |
+|:-------------:|:-----:|:------:|:---------------:|:------:|:-------:|:--------:|:--------------:|:-------------------------------------:|
+| 0.0287        | 1.0   | 20385  | 0.0270          | 0.8235 | 0.8815  | 0.7695   | 10.4603        | 0.8167                                |
+| 0.0172        | 2.0   | 40770  | 0.0199          | 0.8773 | 0.9210  | 0.8306   | 7.5943         | 0.8768                                |
+| 0.01          | 3.0   | 61155  | 0.0168          | 0.9028 | 0.9364  | 0.8639   | 6.2111         | 0.9045                                |
+| 0.0062        | 4.0   | 81540  | 0.0152          | 0.9207 | 0.9520  | 0.8871   | 5.1353         | 0.9213                                |
+| 0.0037        | 5.0   | 101925 | 0.0151          | 0.9300 | 0.9569  | 0.9026   | 4.7350         | 0.9295                                |
+| 0.0021        | 6.0   | 122310 | 0.0147          | 0.9365 | 0.9625  | 0.9123   | 4.2946         | 0.9355                                |
+| 0.0013        | 7.0   | 142695 | 0.0148          | 0.9396 | 0.9659  | 0.9184   | 3.9912         | 0.9387                                |
+| 0.001         | 8.0   | 163080 | 0.0150          | 0.9426 | 0.9680  | 0.9243   | 3.8065         | 0.9422                                |
+| 0.0006        | 9.0   | 183465 | 0.0152          | 0.9445 | 0.9693  | 0.9274   | 3.7064         | 0.9438                                |
+| 0.0003        | 10.0  | 203850 | 0.0152          | 0.9462 | 0.9698  | 0.9297   | 3.6573         | 0.9451                                |
 
 
 ### Framework versions