End of training

README.md

@@ -18,9 +18,9 @@ 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 an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0074
-- F1: {'f1': 0.9984006397441024}
-- Accuracy: {'accuracy': 0.9984}
+- Loss: 0.0264
+- F1: {'f1': 0.9936038376973816}
+- Accuracy: {'accuracy': 0.9936}
 
 ## Model description
 
@@ -39,45 +39,34 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2e-05
+- learning_rate: 1e-05
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 2
+- num_epochs: 1
 
 ### Training results
 
 | Training Loss | Epoch | Step  | Validation Loss | F1                         | Accuracy             |
 |:-------------:|:-----:|:-----:|:---------------:|:--------------------------:|:--------------------:|
-| 0.3638        | 0.09  | 1000  | 0.2191          | {'f1': 0.8698148698148697} | {'accuracy': 0.8685} |
-| 0.225         | 0.18  | 2000  | 0.2111          | {'f1': 0.877721574664614}  | {'accuracy': 0.8888} |
-| 0.1833        | 0.27  | 3000  | 0.1637          | {'f1': 0.9370261801273628} | {'accuracy': 0.9377} |
-| 0.1783        | 0.36  | 4000  | 0.1047          | {'f1': 0.9627846323603169} | {'accuracy': 0.9629} |
-| 0.1234        | 0.44  | 5000  | 0.0722          | {'f1': 0.9774569903104607} | {'accuracy': 0.9772} |
-| 0.1074        | 0.53  | 6000  | 0.1449          | {'f1': 0.9723613058281595} | {'accuracy': 0.9724} |
-| 0.1031        | 0.62  | 7000  | 0.0488          | {'f1': 0.9887371673477524} | {'accuracy': 0.9887} |
-| 0.0612        | 0.71  | 8000  | 0.0447          | {'f1': 0.9893138919404774} | {'accuracy': 0.9893} |
-| 0.0722        | 0.8   | 9000  | 0.0496          | {'f1': 0.990337683036159}  | {'accuracy': 0.9903} |
-| 0.0719        | 0.89  | 10000 | 0.0461          | {'f1': 0.9904210736379964} | {'accuracy': 0.9904} |
-| 0.0609        | 0.98  | 11000 | 0.0512          | {'f1': 0.989191353082466}  | {'accuracy': 0.9892} |
-| 0.0515        | 1.07  | 12000 | 0.0303          | {'f1': 0.9912245712006382} | {'accuracy': 0.9912} |
-| 0.0421        | 1.16  | 13000 | 0.0422          | {'f1': 0.991306085739982}  | {'accuracy': 0.9913} |
-| 0.0369        | 1.24  | 14000 | 0.0220          | {'f1': 0.9954055133839393} | {'accuracy': 0.9954} |
-| 0.0356        | 1.33  | 15000 | 0.0224          | {'f1': 0.9959036866819861} | {'accuracy': 0.9959} |
-| 0.0285        | 1.42  | 16000 | 0.0331          | {'f1': 0.99460647223332}   | {'accuracy': 0.9946} |
-| 0.0378        | 1.51  | 17000 | 0.0190          | {'f1': 0.995603517186251}  | {'accuracy': 0.9956} |
-| 0.0277        | 1.6   | 18000 | 0.0170          | {'f1': 0.9964035964035964} | {'accuracy': 0.9964} |
-| 0.0309        | 1.69  | 19000 | 0.0104          | {'f1': 0.997502247976821}  | {'accuracy': 0.9975} |
-| 0.0239        | 1.78  | 20000 | 0.0114          | {'f1': 0.997700689793062}  | {'accuracy': 0.9977} |
-| 0.0239        | 1.87  | 21000 | 0.0072          | {'f1': 0.9982017982017981} | {'accuracy': 0.9982} |
-| 0.0157        | 1.96  | 22000 | 0.0074          | {'f1': 0.9984006397441024} | {'accuracy': 0.9984} |
+| 0.4023        | 0.09  | 1000  | 0.5834          | {'f1': 0.7928479381443299} | {'accuracy': 0.7428} |
+| 0.269         | 0.18  | 2000  | 0.2556          | {'f1': 0.8676012461059189} | {'accuracy': 0.881}  |
+| 0.1879        | 0.27  | 3000  | 0.1296          | {'f1': 0.9648982848025529} | {'accuracy': 0.9648} |
+| 0.142         | 0.36  | 4000  | 0.1022          | {'f1': 0.9740272663946662} | {'accuracy': 0.9739} |
+| 0.1172        | 0.44  | 5000  | 0.0724          | {'f1': 0.979466322785438}  | {'accuracy': 0.9793} |
+| 0.1044        | 0.53  | 6000  | 0.1166          | {'f1': 0.9756195043964828} | {'accuracy': 0.9756} |
+| 0.0948        | 0.62  | 7000  | 0.0538          | {'f1': 0.98813441021039}   | {'accuracy': 0.9881} |
+| 0.075         | 0.71  | 8000  | 0.0444          | {'f1': 0.9892989298929893} | {'accuracy': 0.9893} |
+| 0.0667        | 0.8   | 9000  | 0.0427          | {'f1': 0.9911168779319294} | {'accuracy': 0.9911} |
+| 0.0667        | 0.89  | 10000 | 0.0448          | {'f1': 0.9908384783907588} | {'accuracy': 0.9908} |
+| 0.0668        | 0.98  | 11000 | 0.0264          | {'f1': 0.9936038376973816} | {'accuracy': 0.9936} |
 
 
 ### Framework versions
 
-- Transformers 4.35.1
+- Transformers 4.35.2
 - Pytorch 2.1.0+cu118
-- Datasets 2.14.6
-- Tokenizers 0.14.1
+- Datasets 2.15.0
+- Tokenizers 0.15.0

runs/Nov16_15-00-00_575f41c15980/events.out.tfevents.1700146811.575f41c15980.315.0

@@ -1,3 +1,3 @@
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