End of training

README.md

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+---
+language:
+- en
+license: mit
+base_model: microsoft/deberta-v3-xsmall
+tags:
+- nycu-112-2-datamining-hw2
+- generated_from_trainer
+datasets:
+- DandinPower/review_onlytitleandtext
+metrics:
+- accuracy
+model-index:
+- name: deberta-v3-xsmall-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: 0.6544285714285715
+---
+
+<!-- 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-xsmall-otat-recommened-hp
+
+This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the DandinPower/review_onlytitleandtext dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8557
+- Accuracy: 0.6544
+- Macro F1: 0.6530
+
+## 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: 4.5e-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: 1000
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
+| 0.9839        | 1.14  | 500  | 0.9456          | 0.6059   | 0.5947   |
+| 0.8351        | 2.29  | 1000 | 0.8711          | 0.6367   | 0.6268   |
+| 0.7364        | 3.43  | 1500 | 0.8376          | 0.6433   | 0.6463   |
+| 0.6687        | 4.57  | 2000 | 0.8557          | 0.6544   | 0.6530   |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.2.2+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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