update model card README.md

README.md

@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.8379
-- Accuracy: {'accuracy': 0.638}
-- F1: {'f1': 0.6347028754401022}
-- Precision: {'precision': 0.6330789574164506}
-- Recall: {'recall': 0.638}
+- Loss: 0.7935
+- Accuracy: {'accuracy': 0.67}
+- F1: {'f1': 0.6539863523155215}
+- Precision: {'precision': 0.6655888523241464}
+- Recall: {'recall': 0.67}
 
 ## Model description
 
@@ -55,16 +55,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy            | F1                         | Precision                         | Recall            |
 |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:--------------------------:|:---------------------------------:|:-----------------:|
-| No log        | 1.0   | 125  | 0.9131          | {'accuracy': 0.575} | {'f1': 0.5238639982308713} | {'precision': 0.5763958598091251} | {'recall': 0.575} |
-| No log        | 2.0   | 250  | 0.8958          | {'accuracy': 0.594} | {'f1': 0.5872686350677672} | {'precision': 0.5839805403784284} | {'recall': 0.594} |
-| No log        | 3.0   | 375  | 1.0090          | {'accuracy': 0.579} | {'f1': 0.5805384614353013} | {'precision': 0.5937972757207018} | {'recall': 0.579} |
-| 0.6603        | 4.0   | 500  | 1.2606          | {'accuracy': 0.576} | {'f1': 0.5817117729696419} | {'precision': 0.5920616341004294} | {'recall': 0.576} |
-| 0.6603        | 5.0   | 625  | 1.4795          | {'accuracy': 0.577} | {'f1': 0.5838498885025925} | {'precision': 0.6094074383728622} | {'recall': 0.577} |
+| 0.7881        | 1.0   | 1923 | 0.8177          | {'accuracy': 0.638} | {'f1': 0.6219209356584174} | {'precision': 0.6325213408748697} | {'recall': 0.638} |
+| 0.649         | 2.0   | 3846 | 0.8257          | {'accuracy': 0.669} | {'f1': 0.6701535233107099} | {'precision': 0.672307962349643}  | {'recall': 0.669} |
+| 0.4771        | 3.0   | 5769 | 0.8922          | {'accuracy': 0.676} | {'f1': 0.6778795418743319} | {'precision': 0.6805694646691987} | {'recall': 0.676} |
+| 0.3403        | 4.0   | 7692 | 1.4285          | {'accuracy': 0.669} | {'f1': 0.666176554548987}  | {'precision': 0.6653390405441227} | {'recall': 0.669} |
+| 0.2088        | 5.0   | 9615 | 1.7417          | {'accuracy': 0.67}  | {'f1': 0.6716636513157895} | {'precision': 0.6752339933799478} | {'recall': 0.67}  |
 
 
 ### Framework versions
 
 - Transformers 4.31.0
 - Pytorch 2.0.1+cu118
-- Datasets 2.14.2
+- Datasets 2.14.3
 - Tokenizers 0.13.3