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: 0.7974
-- Accuracy: 0.55
-- Auc: 0.54
+- Loss: 0.1238
+- Accuracy: 0.96
+- Auc: 1.0
 
 ## Model description
 
@@ -51,16 +51,16 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc  |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|
-| 0.6974        | 1.0   | 11   | 0.7115          | 0.59     | 0.53 |
-| 0.6953        | 2.0   | 22   | 0.7974          | 0.55     | 0.54 |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
+| 0.3922        | 1.0   | 27   | 0.0940          | 0.96     | 1.0 |
+| 0.1191        | 2.0   | 54   | 0.1238          | 0.96     | 1.0 |
 
 
 ### Framework versions
 
 - PEFT 0.16.0
 - Transformers 4.53.3
-- Pytorch 2.7.1+cu126
+- Pytorch 2.7.1
 - Datasets 4.0.0
 - Tokenizers 0.21.2

adapter_config.json

@@ -28,8 +28,8 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "value",
     "query",
+    "value",
     "key"
   ],
   "task_type": "SEQ_CLS",

adapter_model.safetensors

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runs/Sep06_13-55-18_Mac/events.out.tfevents.1757163318.Mac

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training_args.bin

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