metadata
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: out
results: []
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