update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.7064853556485355
+      value: 0.7563938618925832
     - name: Recall
       type: recall
-      value: 0.8728353579736366
+      value: 0.9172912897389507
     - name: F1
       type: f1
-      value: 0.780899525956758
+      value: 0.8291087489779232
     - name: Accuracy
       type: accuracy
-      value: 0.9653054820924291
+      value: 0.9672628386152076
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,10 +44,10 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the lener_br dataset.
 It achieves the following results on the evaluation set:
 - Loss: nan
-- Precision: 0.7065
-- Recall: 0.8728
-- F1: 0.7809
-- Accuracy: 0.9653
+- Precision: 0.7564
+- Recall: 0.9173
+- F1: 0.8291
+- Accuracy: 0.9673
 
 ## Model description
 
@@ -72,13 +72,27 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 1
+- num_epochs: 15
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0784        | 1.0   | 1957 | nan             | 0.7065    | 0.8728 | 0.7809 | 0.9653   |
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0939        | 1.0   | 1957  | nan             | 0.6718    | 0.8545 | 0.7522 | 0.9609   |
+| 0.0462        | 2.0   | 3914  | nan             | 0.7637    | 0.8739 | 0.8151 | 0.9657   |
+| 0.0286        | 3.0   | 5871  | nan             | 0.7357    | 0.9077 | 0.8127 | 0.9691   |
+| 0.0253        | 4.0   | 7828  | nan             | 0.7497    | 0.8989 | 0.8176 | 0.9690   |
+| 0.0209        | 5.0   | 9785  | nan             | 0.7363    | 0.9196 | 0.8178 | 0.9624   |
+| 0.0149        | 6.0   | 11742 | nan             | 0.7209    | 0.9201 | 0.8084 | 0.9673   |
+| 0.0149        | 7.0   | 13699 | nan             | 0.7508    | 0.8987 | 0.8181 | 0.9682   |
+| 0.0099        | 8.0   | 15656 | nan             | 0.7837    | 0.8692 | 0.8243 | 0.9617   |
+| 0.0067        | 9.0   | 17613 | nan             | 0.8086    | 0.8638 | 0.8353 | 0.9703   |
+| 0.0046        | 10.0  | 19570 | nan             | 0.7518    | 0.9209 | 0.8278 | 0.9682   |
+| 0.0047        | 11.0  | 21527 | nan             | 0.7504    | 0.9101 | 0.8226 | 0.9681   |
+| 0.002         | 12.0  | 23484 | nan             | 0.7890    | 0.9082 | 0.8444 | 0.9646   |
+| 0.0033        | 13.0  | 25441 | nan             | 0.7629    | 0.9157 | 0.8324 | 0.9675   |
+| 0.0029        | 14.0  | 27398 | nan             | 0.7484    | 0.9155 | 0.8235 | 0.9658   |
+| 0.0009        | 15.0  | 29355 | nan             | 0.7564    | 0.9173 | 0.8291 | 0.9673   |
 
 
 ### Framework versions