---
library_name: transformers
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: cf-albert-finetuned1
  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. -->

# cf-albert-finetuned1

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4513
- F1: 0.2897
- Roc Auc: 0.5790
- Accuracy: 0.0826

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4712        | 1.0   | 908  | 0.4797          | 0.0104 | 0.5024  | 0.0055   |
| 0.4862        | 2.0   | 1816 | 0.4579          | 0.2726 | 0.5727  | 0.0936   |
| 0.4447        | 3.0   | 2724 | 0.4438          | 0.3161 | 0.5899  | 0.1101   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3