---
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