| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/deberta-v3-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| - accuracy |
| model-index: |
| - name: out |
| 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. --> |
|
|
| ### Model checkpoint for Assignment3 |
| ### The full code for training procedure, configuration and the training log for the checkpoint model are documented in the IPython notebook accessible in the files |
| ### Comparable results of the checkpoint used in assignment3 can be reproduced in Colab using training pipeline in the IPython notebook. |
| This model is a fine-tuned version of microsoft/deberta-v3-base on climate claim verification training dataset(using gold evidence provided by the training set). It achieves the following results on the development set: |
|
|
| Model evalutaion performance on the development set |
| - F1: 0.7196 |
| - Accuracy: 0.7922 |
|
|
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 1.5e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 16 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 0.1 |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| |
| | 5.4135 | 1.0 | 77 | 1.3468 | 0.1532 | 0.4416 | |
| | 4.6607 | 2.0 | 154 | 1.1471 | 0.3819 | 0.6364 | |
| | 4.2591 | 3.0 | 231 | 1.1545 | 0.3801 | 0.6234 | |
| | 3.9299 | 4.0 | 308 | 0.9857 | 0.6322 | 0.7013 | |
| | 3.2692 | 5.0 | 385 | 0.8877 | 0.6500 | 0.7273 | |
| | 2.7183 | 6.0 | 462 | 1.0321 | 0.6360 | 0.7403 | |
| | 2.3779 | 7.0 | 539 | 0.9220 | 0.7017 | 0.7727 | |
| | 2.1893 | 8.0 | 616 | 0.9742 | 0.7196 | 0.7922 | |
| | 1.9169 | 9.0 | 693 | 0.9781 | 0.7034 | 0.7857 | |
| | 1.8150 | 10.0 | 770 | 0.9680 | 0.7035 | 0.7857 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.8.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.8.5 |
| - Tokenizers 0.22.2 |
| |
