| --- |
| license: mit |
| base_model: microsoft/deberta-v3-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: checkpoints_26_9_microsoft_deberta_21_9 |
| 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. --> |
|
|
| # checkpoints_26_9_microsoft_deberta_21_9 |
|
|
| 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: 0.6029 |
| - Map@3: 0.865 |
| - Accuracy: 0.79 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| 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: 2 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - gradient_accumulation_steps: 32 |
| - total_train_batch_size: 64 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 1 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Map@3 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| |
| | 1.0387 | 0.11 | 100 | 0.8482 | 0.8242 | 0.725 | |
| | 0.8243 | 0.21 | 200 | 0.7331 | 0.8667 | 0.79 | |
| | 0.8484 | 0.32 | 300 | 0.6765 | 0.8858 | 0.82 | |
| | 0.7295 | 0.43 | 400 | 0.6490 | 0.8575 | 0.775 | |
| | 0.7206 | 0.53 | 500 | 0.6752 | 0.8625 | 0.79 | |
| | 0.6933 | 0.64 | 600 | 0.6329 | 0.8742 | 0.815 | |
| | 0.6817 | 0.75 | 700 | 0.6131 | 0.8675 | 0.8 | |
| | 0.6738 | 0.85 | 800 | 0.6029 | 0.865 | 0.79 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.32.1 |
| - Pytorch 2.0.0 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.3 |
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|
