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--- |
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license: mit |
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base_model: microsoft/deberta-v3-small |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: my_awesome_model |
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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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# my_awesome_model |
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This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0808 |
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- Accuracy: 0.8289 |
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- F1: 0.8595 |
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- Precision: 0.8864 |
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- Recall: 0.8342 |
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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: 2e-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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- 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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 47 | 0.1056 | 0.7059 | 0.7788 | 0.8684 | 0.7059 | |
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| No log | 2.0 | 94 | 0.0961 | 0.7219 | 0.7895 | 0.8710 | 0.7219 | |
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| No log | 3.0 | 141 | 0.1042 | 0.7594 | 0.8045 | 0.8554 | 0.7594 | |
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| No log | 4.0 | 188 | 0.0899 | 0.8021 | 0.8427 | 0.8876 | 0.8021 | |
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| No log | 5.0 | 235 | 0.0911 | 0.8182 | 0.8540 | 0.8807 | 0.8289 | |
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| No log | 6.0 | 282 | 0.0808 | 0.8289 | 0.8595 | 0.8864 | 0.8342 | |
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| No log | 7.0 | 329 | 0.0885 | 0.8503 | 0.8689 | 0.8883 | 0.8503 | |
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| No log | 8.0 | 376 | 0.0873 | 0.8396 | 0.8634 | 0.8827 | 0.8449 | |
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| No log | 9.0 | 423 | 0.0926 | 0.8342 | 0.8579 | 0.8771 | 0.8396 | |
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| No log | 10.0 | 470 | 0.0904 | 0.8342 | 0.8603 | 0.8820 | 0.8396 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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