Model save

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.0
+      value: 0.8447596532702916
     - name: Recall
       type: recall
-      value: 0.0
+      value: 0.8855844692275919
     - name: F1
       type: f1
-      value: 0.0
+      value: 0.8646904617866505
     - name: Accuracy
       type: accuracy
-      value: 0.7809647979139505
+      value: 0.9681587715486021
 ---
 
 <!-- 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 [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.9941
-- Precision: 0.0
-- Recall: 0.0
-- F1: 0.0
-- Accuracy: 0.7810
+- Loss: 0.1762
+- Precision: 0.8448
+- Recall: 0.8856
+- F1: 0.8647
+- Accuracy: 0.9682
 
 ## Model description
 
@@ -67,7 +67,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 0.005
+- learning_rate: 2e-05
 - train_batch_size: 1
 - eval_batch_size: 1
 - seed: 42
@@ -77,11 +77,11 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1  | Accuracy |
-|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:---:|:--------:|
-| 1.0478        | 1.0   | 7193  | 1.0002          | 0.0       | 0.0    | 0.0 | 0.7810   |
-| 0.9754        | 2.0   | 14386 | 0.9872          | 0.0       | 0.0    | 0.0 | 0.7810   |
-| 0.9377        | 3.0   | 21579 | 0.9941          | 0.0       | 0.0    | 0.0 | 0.7810   |
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2106        | 1.0   | 7193  | 0.2086          | 0.7859    | 0.8203 | 0.8027 | 0.9563   |
+| 0.1136        | 2.0   | 14386 | 0.1710          | 0.8391    | 0.8678 | 0.8532 | 0.9658   |
+| 0.0973        | 3.0   | 21579 | 0.1762          | 0.8448    | 0.8856 | 0.8647 | 0.9682   |
 
 
 ### Framework versions