| --- |
| license: mit |
| base_model: microsoft/deberta-v3-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: deberta-large-ReqORNot |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # deberta-large-ReqORNot |
|
|
| This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5297 |
| - Accuracy: 0.9135 |
| - Weighted precision: 0.9135 |
| - Weighted recall: 0.9135 |
| - Weighted f1: 0.9134 |
| - Macro precision: 0.9135 |
| - Macro recall: 0.9128 |
| - Macro f1: 0.9131 |
|
|
| ## 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 |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
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| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 3 |
| - eval_batch_size: 3 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 3 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted precision | Weighted recall | Weighted f1 | Macro precision | Macro recall | Macro f1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------:|:---------------:|:-----------:|:---------------:|:------------:|:--------:| |
| | 0.4826 | 1.0 | 1896 | 0.4286 | 0.9020 | 0.9020 | 0.9020 | 0.9019 | 0.9018 | 0.9014 | 0.9016 | |
| | 0.3429 | 2.0 | 3792 | 0.4274 | 0.9077 | 0.9091 | 0.9077 | 0.9078 | 0.9076 | 0.9089 | 0.9076 | |
| | 0.1299 | 3.0 | 5688 | 0.5297 | 0.9135 | 0.9135 | 0.9135 | 0.9134 | 0.9135 | 0.9128 | 0.9131 | |
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| ### Framework versions |
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|
| - Transformers 4.38.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
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|
