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README.md
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---
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license: mit
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base_model: microsoft/deberta-v3-base
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: deberta-v3-base-DIALOCONAN-WIKI-CLS
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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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# deberta-v3-base-DIALOCONAN-WIKI-CLS
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3828
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- Precision: 0.7060
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- Recall: 0.7086
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- F1: 0.7072
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- Accuracy: 0.9422
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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: 3e-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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- 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 | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.4295 | 1.0 | 2500 | 0.5694 | 0.6816 | 0.6816 | 0.6793 | 0.9040 |
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| 0.3525 | 2.0 | 5000 | 0.4852 | 0.6923 | 0.6938 | 0.6928 | 0.9225 |
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| 0.2604 | 3.0 | 7500 | 0.4372 | 0.6993 | 0.7005 | 0.6995 | 0.9314 |
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| 0.1979 | 4.0 | 10000 | 0.4076 | 0.7056 | 0.7077 | 0.7065 | 0.9410 |
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| 0.1295 | 5.0 | 12500 | 0.3828 | 0.7060 | 0.7086 | 0.7072 | 0.9422 |
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### Framework versions
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- Transformers 4.42.4
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- Pytorch 2.3.1+cu121
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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