End of training

README.md

@@ -1,10 +1,11 @@
 ---
 license: apache-2.0
-base_model: distilbert/distilbert-base-uncased-finetuned-sst-2-english
+base_model: pradanaadn/sucidal-text-classification-distillbert
 tags:
 - generated_from_trainer
 metrics:
 - accuracy
+- f1
 model-index:
 - name: sucidal-text-classification-distillbert
   results: []
@@ -15,11 +16,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 # sucidal-text-classification-distillbert
 
-This model is a fine-tuned version of [distilbert/distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english) on an unknown dataset.
+This model is a fine-tuned version of [pradanaadn/sucidal-text-classification-distillbert](https://huggingface.co/pradanaadn/sucidal-text-classification-distillbert) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4590
-- Accuracy: 0.8198
-- F1 Score: 0.8198
+- Loss: 0.5138
+- Accuracy: 0.8106
+- F1: 0.7801
 
 ## Model description
 
@@ -39,8 +40,8 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 16
-- eval_batch_size: 16
+- train_batch_size: 64
+- eval_batch_size: 64
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
@@ -49,28 +50,15 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1 Score |
-|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
-| 0.3602        | 0.1885 | 500  | 0.7867          | 0.7670   | 0.7670   |
-| 0.6285        | 0.3769 | 1000 | 0.5574          | 0.7795   | 0.7795   |
-| 0.5624        | 0.5654 | 1500 | 0.5011          | 0.7988   | 0.7988   |
-| 0.5413        | 0.7539 | 2000 | 0.4968          | 0.8017   | 0.8017   |
-| 0.5084        | 0.9423 | 2500 | 0.4712          | 0.8085   | 0.8085   |
-| 0.4253        | 1.1308 | 3000 | 0.4938          | 0.8053   | 0.8053   |
-| 0.3915        | 1.3193 | 3500 | 0.4781          | 0.8136   | 0.8136   |
-| 0.3739        | 1.5077 | 4000 | 0.5195          | 0.8043   | 0.8043   |
-| 0.3638        | 1.6962 | 4500 | 0.4790          | 0.8201   | 0.8201   |
-| 0.3667        | 1.8847 | 5000 | 0.4590          | 0.8198   | 0.8198   |
-| 0.3182        | 2.0731 | 5500 | 0.5129          | 0.8218   | 0.8218   |
-| 0.2325        | 2.2616 | 6000 | 0.5279          | 0.8198   | 0.8198   |
-| 0.2318        | 2.4501 | 6500 | 0.5368          | 0.8197   | 0.8197   |
-| 0.2219        | 2.6385 | 7000 | 0.5606          | 0.8221   | 0.8221   |
-| 0.2261        | 2.8270 | 7500 | 0.5406          | 0.8229   | 0.8229   |
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
+| 0.2549        | 0.7530 | 500  | 0.5138          | 0.8106   | 0.7801 |
+| 0.2054        | 1.5060 | 1000 | 0.6016          | 0.8142   | 0.7877 |
+| 0.1666        | 2.2590 | 1500 | 0.6464          | 0.8164   | 0.7896 |
 
 
 ### Framework versions
 
 - Transformers 4.41.2
 - Pytorch 2.3.0+cu121
-- Datasets 2.20.0
 - Tokenizers 0.19.1

model.safetensors

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