---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: self_iterative_v2_targeted_iteration_0
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/c487331i)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/c487331i)
# self_iterative_v2_targeted_iteration_0

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6226
- Accuracy Targeted: 0.7044
- F1 Macro Targeted: 0.5439
- F1 Weighted Targeted: 0.6401
- F1 Macro Total: 0.5439
- F1 Weighted Total: 0.6401

## 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: 6e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 1337
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy Targeted | F1 Macro Targeted | F1 Weighted Targeted | F1 Macro Total | F1 Weighted Total |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:-----------------:|:--------------------:|:--------------:|:-----------------:|
| 0.7828        | 1.0   | 1100  | 2.3297          | 0.6778            | 0.4040            | 0.5476               | 0.4040         | 0.5476            |
| 0.989         | 2.0   | 2200  | 2.1488          | 0.7               | 0.4894            | 0.6060               | 0.4894         | 0.6060            |
| 0.916         | 3.0   | 3300  | 2.4146          | 0.6911            | 0.4638            | 0.5879               | 0.4638         | 0.5879            |
| 0.7552        | 4.0   | 4400  | 2.6174          | 0.7044            | 0.4920            | 0.6088               | 0.4920         | 0.6088            |
| 0.612         | 5.0   | 5500  | 2.9978          | 0.6978            | 0.4831            | 0.6015               | 0.4831         | 0.6015            |
| 0.4097        | 6.0   | 6600  | 3.6226          | 0.7044            | 0.5439            | 0.6401               | 0.5439         | 0.6401            |
| 0.2504        | 7.0   | 7700  | 4.1092          | 0.6911            | 0.5194            | 0.6215               | 0.5194         | 0.6215            |
| 0.2001        | 8.0   | 8800  | 4.9055          | 0.6956            | 0.4868            | 0.6032               | 0.4868         | 0.6032            |
| 0.137         | 9.0   | 9900  | 4.8960          | 0.6956            | 0.4964            | 0.6090               | 0.4964         | 0.6090            |
| 0.0979        | 10.0  | 11000 | 5.5659          | 0.6844            | 0.4549            | 0.5807               | 0.4549         | 0.5807            |
| 0.0768        | 11.0  | 12100 | 6.1068          | 0.6889            | 0.4626            | 0.5866               | 0.4626         | 0.5866            |


### Framework versions

- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.0.1
- Tokenizers 0.21.1