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--- |
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language: |
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- en |
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license: mit |
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base_model: microsoft/deberta-v3-xsmall |
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tags: |
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- nycu-112-2-datamining-hw2 |
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- generated_from_trainer |
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datasets: |
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- DandinPower/review_cleanonlytitleandtext |
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metrics: |
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- accuracy |
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model-index: |
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- name: deberta-v3-xsmall-cotat-recommened-hp |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: DandinPower/review_cleanonlytitleandtext |
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type: DandinPower/review_cleanonlytitleandtext |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6262857142857143 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# deberta-v3-xsmall-cotat-recommened-hp |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8783 |
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- Accuracy: 0.6263 |
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- Macro F1: 0.6285 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 4.5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:| |
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| 1.61 | 0.4571 | 100 | 1.6076 | 0.22 | 0.1631 | |
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| 1.5063 | 0.9143 | 200 | 1.2854 | 0.4094 | 0.2942 | |
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| 1.2016 | 1.3714 | 300 | 1.0481 | 0.5529 | 0.5311 | |
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| 1.0219 | 1.8286 | 400 | 0.9338 | 0.6093 | 0.6020 | |
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| 0.9362 | 2.2857 | 500 | 0.8919 | 0.6261 | 0.6239 | |
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| 0.9097 | 2.7429 | 600 | 0.8783 | 0.6263 | 0.6285 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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