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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: 0.8643440917174317 |
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- name: F1 |
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type: f1 |
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value: 0.8368032657773605 |
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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: 1.0026 |
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- Accuracy: 0.8643 |
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- F1: 0.8368 |
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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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| 0.5131 | 0.27 | 5000 | 0.6674 | 0.8368 | 0.7780 | |
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| 0.3715 | 0.53 | 10000 | 0.6554 | 0.8527 | 0.8145 | |
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| 0.3066 | 0.8 | 15000 | 0.6924 | 0.8471 | 0.8103 | |
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| 0.2194 | 1.07 | 20000 | 0.7348 | 0.8548 | 0.8238 | |
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| 0.2112 | 1.34 | 25000 | 0.7297 | 0.8581 | 0.8288 | |
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| 0.1907 | 1.6 | 30000 | 0.7308 | 0.8558 | 0.8288 | |
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| 0.1816 | 1.87 | 35000 | 0.7785 | 0.8565 | 0.8281 | |
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| 0.1297 | 2.14 | 40000 | 0.8493 | 0.8567 | 0.8278 | |
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| 0.127 | 2.41 | 45000 | 0.8757 | 0.8576 | 0.8310 | |
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| 0.1148 | 2.67 | 50000 | 0.8581 | 0.8577 | 0.8300 | |
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| 0.1287 | 2.94 | 55000 | 0.8479 | 0.8597 | 0.8341 | |
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| 0.0875 | 3.21 | 60000 | 0.8763 | 0.8656 | 0.8392 | |
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| 0.0832 | 3.47 | 65000 | 0.9379 | 0.8620 | 0.8341 | |
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| 0.0837 | 3.74 | 70000 | 0.9044 | 0.8625 | 0.8339 | |
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| 0.0617 | 4.01 | 75000 | 0.9840 | 0.8618 | 0.8352 | |
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| 0.0524 | 4.28 | 80000 | 0.9955 | 0.8639 | 0.8385 | |
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| 0.0496 | 4.54 | 85000 | 1.0026 | 0.8643 | 0.8368 | |
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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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