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--- |
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license: mit |
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base_model: microsoft/deberta-v3-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: taskA-DeBERTa-large-conf-1.0.0 |
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results: [] |
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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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# taskA-DeBERTa-large-conf-1.0.0 |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0557 |
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- Accuracy: 0.8003 |
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- Precision: 0.6080 |
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- Recall: 0.5677 |
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- F1: 0.5872 |
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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: 4e-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.5219 | 0.39 | 500 | 0.5422 | 0.8147 | 0.725 | 0.4179 | 0.5302 | |
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| 0.5372 | 0.78 | 1000 | 0.6654 | 0.7873 | 0.9194 | 0.1643 | 0.2787 | |
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| 0.4255 | 1.16 | 1500 | 0.7670 | 0.8133 | 0.6774 | 0.4841 | 0.5647 | |
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| 0.3873 | 1.55 | 2000 | 1.2863 | 0.7592 | 0.5132 | 0.7262 | 0.6014 | |
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| 0.4026 | 1.94 | 2500 | 0.9782 | 0.7967 | 0.5964 | 0.5793 | 0.5877 | |
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| 0.2893 | 2.33 | 3000 | 1.0557 | 0.8003 | 0.6080 | 0.5677 | 0.5872 | |
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| 0.2513 | 2.71 | 3500 | 1.2664 | 0.7779 | 0.5470 | 0.6542 | 0.5958 | |
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| 0.231 | 3.1 | 4000 | 1.2628 | 0.7895 | 0.5745 | 0.6110 | 0.5922 | |
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| 0.1684 | 3.49 | 4500 | 1.1848 | 0.8061 | 0.6211 | 0.5764 | 0.5979 | |
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| 0.1647 | 3.88 | 5000 | 1.2085 | 0.8068 | 0.6262 | 0.5648 | 0.5939 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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