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--- |
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license: mit |
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base_model: microsoft/mdeberta-v3-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- massive |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1 |
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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: massive |
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type: massive |
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config: all_1.1 |
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split: validation |
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args: all_1.1 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8577887926141738 |
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- name: F1 |
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type: f1 |
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value: 0.8335554213502777 |
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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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# scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1 |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9178 |
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- Accuracy: 0.8578 |
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- F1: 0.8336 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 64 |
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- seed: 66 |
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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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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| |
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| 0.5269 | 0.27 | 5000 | 0.6875 | 0.8358 | 0.7817 | |
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| 0.3683 | 0.53 | 10000 | 0.6940 | 0.8489 | 0.8131 | |
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| 0.3073 | 0.8 | 15000 | 0.6710 | 0.8545 | 0.8198 | |
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| 0.2189 | 1.07 | 20000 | 0.7507 | 0.8539 | 0.8299 | |
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| 0.2276 | 1.34 | 25000 | 0.7456 | 0.8582 | 0.8347 | |
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| 0.1939 | 1.6 | 30000 | 0.8157 | 0.8562 | 0.8342 | |
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| 0.1852 | 1.87 | 35000 | 0.7920 | 0.8548 | 0.8269 | |
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| 0.1302 | 2.14 | 40000 | 0.8574 | 0.8559 | 0.8329 | |
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| 0.1273 | 2.41 | 45000 | 0.8945 | 0.8594 | 0.8330 | |
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| 0.1163 | 2.67 | 50000 | 0.9178 | 0.8578 | 0.8336 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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