| | --- |
| | license: mit |
| | base_model: microsoft/deberta-v3-large |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: checkpoints_28_9_microsoft_deberta_V2 |
| | 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_28_9_microsoft_deberta_V2 |
| | |
| | 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: 0.5675 |
| | - Map@3: 0.8842 |
| | - Accuracy: 0.815 |
| | |
| | ## Model description |
| | |
| | More information needed |
| | |
| | ## Intended uses & limitations |
| | |
| | More information needed |
| | |
| | ## Training and evaluation data |
| | |
| | More information needed |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| |
| | | 1.0011 | 0.11 | 100 | 0.8842 | 0.8258 | 0.74 | |
| | | 0.8398 | 0.21 | 200 | 0.6978 | 0.8667 | 0.79 | |
| | | 0.8414 | 0.32 | 300 | 0.6337 | 0.8625 | 0.795 | |
| | | 0.7461 | 0.43 | 400 | 0.6609 | 0.8600 | 0.775 | |
| | | 0.7131 | 0.53 | 500 | 0.6329 | 0.8758 | 0.805 | |
| | | 0.6891 | 0.64 | 600 | 0.6157 | 0.8892 | 0.83 | |
| | | 0.6969 | 0.75 | 700 | 0.5917 | 0.8808 | 0.805 | |
| | | 0.6775 | 0.85 | 800 | 0.5698 | 0.8817 | 0.81 | |
| | | 0.6534 | 0.96 | 900 | 0.5675 | 0.8842 | 0.815 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.32.1 |
| | - Pytorch 2.0.0 |
| | - Datasets 2.9.0 |
| | - Tokenizers 0.13.3 |
| | |
