End of training

README.md

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+---
+language:
+- en
+license: mit
+base_model: microsoft/deberta-v3-base
+tags:
+- nycu-112-2-datamining-hw2
+- generated_from_trainer
+datasets:
+- DandinPower/review_onlytitleandtext
+metrics:
+- accuracy
+model-index:
+- name: deberta-v3-base-otat-recommened-hp
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: DandinPower/review_onlytitleandtext
+      type: DandinPower/review_onlytitleandtext
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.6617142857142857
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# deberta-v3-base-otat-recommened-hp
+
+This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the DandinPower/review_onlytitleandtext dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8654
+- Accuracy: 0.6617
+- Macro F1: 0.6582
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
+| 0.8299        | 1.14  | 500  | 0.8488          | 0.6484   | 0.6448   |
+| 0.7147        | 2.29  | 1000 | 0.8250          | 0.6561   | 0.6480   |
+| 0.6487        | 3.43  | 1500 | 0.8193          | 0.6581   | 0.6596   |
+| 0.5704        | 4.57  | 2000 | 0.8654          | 0.6617   | 0.6582   |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.2.2+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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