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_cleanonlytitleandtext
+metrics:
+- accuracy
+model-index:
+- name: deberta-v3-xsmall-cotat-recommened-hp
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: DandinPower/review_cleanonlytitleandtext
+      type: DandinPower/review_cleanonlytitleandtext
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.6262857142857143
+---
+
+<!-- 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-cotat-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_cleanonlytitleandtext dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8783
+- Accuracy: 0.6263
+- Macro F1: 0.6285
+
+## 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: 8
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Macro F1 |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
+| 1.61          | 0.4571 | 100  | 1.6076          | 0.22     | 0.1631   |
+| 1.5063        | 0.9143 | 200  | 1.2854          | 0.4094   | 0.2942   |
+| 1.2016        | 1.3714 | 300  | 1.0481          | 0.5529   | 0.5311   |
+| 1.0219        | 1.8286 | 400  | 0.9338          | 0.6093   | 0.6020   |
+| 0.9362        | 2.2857 | 500  | 0.8919          | 0.6261   | 0.6239   |
+| 0.9097        | 2.7429 | 600  | 0.8783          | 0.6263   | 0.6285   |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1