End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.8094229751191107
+      value: 0.801254136909946
     - name: Recall
       type: recall
-      value: 0.840571742715778
+      value: 0.8429540040315191
     - name: F1
       type: f1
-      value: 0.8247033441208198
+      value: 0.821575281300232
     - name: Accuracy
       type: accuracy
-      value: 0.9697238736359447
+      value: 0.9685663231476382
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the lener_br dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1297
-- Precision: 0.8094
-- Recall: 0.8406
-- F1: 0.8247
-- Accuracy: 0.9697
+- Loss: 0.1550
+- Precision: 0.8013
+- Recall: 0.8430
+- F1: 0.8216
+- Accuracy: 0.9686
 
 ## Model description
 
@@ -73,18 +73,22 @@ 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: 100
+- num_epochs: 10
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 490  | 0.1863          | 0.6594    | 0.6441 | 0.6516 | 0.9411   |
-| 0.2956        | 2.0   | 980  | 0.1577          | 0.6933    | 0.7847 | 0.7362 | 0.9527   |
-| 0.0891        | 3.0   | 1470 | 0.1971          | 0.6681    | 0.8221 | 0.7371 | 0.9449   |
-| 0.0533        | 4.0   | 1960 | 0.1297          | 0.8094    | 0.8406 | 0.8247 | 0.9697   |
-| 0.0363        | 5.0   | 2450 | 0.1409          | 0.7951    | 0.8340 | 0.8141 | 0.9682   |
-| 0.026         | 6.0   | 2940 | 0.1791          | 0.7547    | 0.8277 | 0.7895 | 0.9600   |
+| No log        | 1.0   | 490  | 0.1750          | 0.7347    | 0.6581 | 0.6942 | 0.9465   |
+| 0.2808        | 2.0   | 980  | 0.1642          | 0.6954    | 0.7598 | 0.7262 | 0.9538   |
+| 0.093         | 3.0   | 1470 | 0.1849          | 0.6708    | 0.7992 | 0.7294 | 0.9510   |
+| 0.0557        | 4.0   | 1960 | 0.1403          | 0.7807    | 0.8345 | 0.8067 | 0.9668   |
+| 0.0366        | 5.0   | 2450 | 0.1560          | 0.7775    | 0.8466 | 0.8106 | 0.9626   |
+| 0.027         | 6.0   | 2940 | 0.1612          | 0.7342    | 0.8239 | 0.7764 | 0.9621   |
+| 0.0204        | 7.0   | 3430 | 0.1632          | 0.7625    | 0.8356 | 0.7974 | 0.9644   |
+| 0.015         | 8.0   | 3920 | 0.1748          | 0.7375    | 0.8442 | 0.7873 | 0.9615   |
+| 0.0135        | 9.0   | 4410 | 0.1547          | 0.7930    | 0.8446 | 0.8180 | 0.9685   |
+| 0.0101        | 10.0  | 4900 | 0.1550          | 0.8013    | 0.8430 | 0.8216 | 0.9686   |
 
 
 ### Framework versions

model.safetensors

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