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DandinPower
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deberta-v2-xlarge-otat

Text Classification
Transformers
Safetensors
English
deberta-v2
nycu-112-2-datamining-hw2
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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README.md ADDED
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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-v2-xlarge
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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_onlytitleandtext
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: deberta-v2-xlarge-otat
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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_onlytitleandtext
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+ type: DandinPower/review_onlytitleandtext
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.20114285714285715
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+ ---
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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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+
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+ # deberta-v2-xlarge-otat
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+
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+ This model is a fine-tuned version of [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/deberta-v2-xlarge) on the DandinPower/review_onlytitleandtext dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.6316
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+ - Accuracy: 0.2011
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+ - Macro F1: 0.0670
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 8
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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: 1500
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
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+ | 1.1994 | 0.14 | 500 | 1.6893 | 0.4029 | 0.3240 |
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+ | 1.6344 | 0.29 | 1000 | 1.6403 | 0.2011 | 0.0670 |
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+ | 1.6413 | 0.43 | 1500 | 1.6270 | 0.2 | 0.0667 |
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+ | 1.6326 | 0.57 | 2000 | 1.6375 | 0.1971 | 0.0659 |
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+ | 1.6128 | 0.71 | 2500 | 1.6604 | 0.2011 | 0.0670 |
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+ | 1.6213 | 0.86 | 3000 | 1.6161 | 0.2 | 0.0667 |
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+ | 1.6199 | 1.0 | 3500 | 1.6132 | 0.2017 | 0.0671 |
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+ | 1.6177 | 1.14 | 4000 | 1.6142 | 0.2011 | 0.0670 |
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+ | 1.6183 | 1.29 | 4500 | 1.6213 | 0.2 | 0.0667 |
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+ | 1.6211 | 1.43 | 5000 | 1.6136 | 0.1971 | 0.0659 |
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+ | 1.6145 | 1.57 | 5500 | 1.6169 | 0.1971 | 0.0659 |
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+ | 1.6187 | 1.71 | 6000 | 1.6160 | 0.2011 | 0.0670 |
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+ | 1.6174 | 1.86 | 6500 | 1.6146 | 0.2 | 0.0667 |
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+ | 1.6164 | 2.0 | 7000 | 1.6181 | 0.2 | 0.0667 |
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+ | 1.6184 | 2.14 | 7500 | 1.6109 | 0.1971 | 0.0659 |
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+ | 1.6152 | 2.29 | 8000 | 1.6189 | 0.2 | 0.0667 |
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+ | 1.6175 | 2.43 | 8500 | 1.6146 | 0.1971 | 0.0659 |
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+ | 1.6134 | 2.57 | 9000 | 1.6160 | 0.1971 | 0.0659 |
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+ | 1.6144 | 2.71 | 9500 | 1.6167 | 0.2011 | 0.0670 |
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+ | 1.6141 | 2.86 | 10000 | 1.6106 | 0.2017 | 0.0671 |
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+ | 1.6128 | 3.0 | 10500 | 1.6139 | 0.1971 | 0.0659 |
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+ | 1.6179 | 3.14 | 11000 | 1.6112 | 0.2 | 0.0667 |
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+ | 1.6096 | 3.29 | 11500 | 1.6127 | 0.2 | 0.0667 |
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+ | 1.6132 | 3.43 | 12000 | 1.6135 | 0.2011 | 0.0670 |
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+ | 1.6053 | 3.57 | 12500 | 1.6186 | 0.2 | 0.0667 |
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+ | 1.6049 | 3.71 | 13000 | 1.6277 | 0.2011 | 0.0670 |
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+ | 1.6044 | 3.86 | 13500 | 1.6271 | 0.2011 | 0.0670 |
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+ | 1.6017 | 4.0 | 14000 | 1.6275 | 0.2011 | 0.0670 |
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+ | 1.608 | 4.14 | 14500 | 1.6192 | 0.2011 | 0.0670 |
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+ | 1.6075 | 4.29 | 15000 | 1.6259 | 0.2011 | 0.0670 |
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+ | 1.601 | 4.43 | 15500 | 1.6267 | 0.2011 | 0.0670 |
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+ | 1.6086 | 4.57 | 16000 | 1.6339 | 0.2011 | 0.0670 |
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+ | 1.5955 | 4.71 | 16500 | 1.6340 | 0.2011 | 0.0670 |
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+ | 1.6013 | 4.86 | 17000 | 1.6322 | 0.2011 | 0.0670 |
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+ | 1.5976 | 5.0 | 17500 | 1.6316 | 0.2011 | 0.0670 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - Pytorch 2.2.2+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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