update model card README.md

README.md

@@ -15,18 +15,26 @@ 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 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: 0.0152
-- F1: 0.9462
-- Roc Auc: 0.9698
-- Accuracy: 0.9297
-- Coverage Error: 3.6573
-- Label Ranking Average Precision Score: 0.9451
+- Loss: 0.0463
+- F1: 0.7931
+- Roc Auc: 0.8858
+- Accuracy: 0.7376
+- Coverage Error: 10.3626
+- Label Ranking Average Precision Score: 0.7968
+
+## Model description
+
+More information needed
 
 ## Intended uses & limitations
 
-This model only predicts the first two digits of the CPV codes.
+More information needed
+
+## Training and evaluation data
+
+More information needed
 
 ## Training procedure
 
@@ -43,18 +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 |
-|:-------------:|:-----:|:------:|:---------------:|:------:|:-------:|:--------:|:--------------:|:-------------------------------------:|
-| 0.0287        | 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                                |
+| Training Loss | Epoch | Step  | Validation Loss | F1     | Roc Auc | Accuracy | Coverage Error | Label Ranking Average Precision Score |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|:--------------:|:-------------------------------------:|
+| 0.0355        | 1.0   | 9054  | 0.0366          | 0.7550 | 0.8373  | 0.6950   | 14.1539        | 0.7347                                |
+| 0.0309        | 2.0   | 18108 | 0.0330          | 0.7773 | 0.8553  | 0.7204   | 12.6503        | 0.7647                                |
+| 0.0234        | 3.0   | 27162 | 0.0330          | 0.7836 | 0.8693  | 0.7293   | 11.6192        | 0.7799                                |
+| 0.0159        | 4.0   | 36216 | 0.0348          | 0.7830 | 0.8709  | 0.7291   | 11.5355        | 0.7810                                |
+| 0.0109        | 5.0   | 45270 | 0.0376          | 0.7789 | 0.8786  | 0.7201   | 10.9898        | 0.7812                                |
+| 0.0075        | 6.0   | 54324 | 0.0397          | 0.7838 | 0.8813  | 0.7241   | 10.7035        | 0.7861                                |
+| 0.0039        | 7.0   | 63378 | 0.0415          | 0.7888 | 0.8818  | 0.7309   | 10.6559        | 0.7898                                |
+| 0.0028        | 8.0   | 72432 | 0.0437          | 0.7906 | 0.8838  | 0.7326   | 10.5117        | 0.7924                                |
+| 0.0016        | 9.0   | 81486 | 0.0453          | 0.7908 | 0.8890  | 0.7308   | 10.0988        | 0.7957                                |
+| 0.001         | 10.0  | 90540 | 0.0463          | 0.7931 | 0.8858  | 0.7376   | 10.3626        | 0.7968                                |
 
 
 ### Framework versions