Colabng/twitter_bank_scam_classifier

README.md

@@ -20,9 +20,9 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6038
-- Accuracy: 0.73
-- Auc: 0.83
+- Loss: 0.7309
+- Accuracy: 0.55
+- Auc: 0.53
 
 ## Model description
 
@@ -53,8 +53,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc  |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|
-| 0.7583        | 1.0   | 11   | 0.5996          | 0.64     | 0.81 |
-| 0.6701        | 2.0   | 22   | 0.6038          | 0.73     | 0.83 |
+| 0.6939        | 1.0   | 11   | 0.7180          | 0.59     | 0.53 |
+| 0.6132        | 2.0   | 22   | 0.7309          | 0.55     | 0.53 |
 
 
 ### Framework versions

adapter_config.json

@@ -28,9 +28,9 @@
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   "task_type": "SEQ_CLS",
   "trainable_token_indices": null,

adapter_model.safetensors

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training_args.bin

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