---
base_model: ElKulako/cryptobert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
  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. -->

# results

This model is a fine-tuned version of [ElKulako/cryptobert](https://huggingface.co/ElKulako/cryptobert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8983
- Accuracy: 0.6433
- Precision: 0.6614
- Recall: 0.6433
- F1: 0.6461

## 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: 3.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 69420
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9586        | 0.19  | 100  | 0.8746          | 0.6033   | 0.5990    | 0.6033 | 0.5944 |
| 0.7362        | 0.38  | 200  | 0.8187          | 0.63     | 0.6322    | 0.63   | 0.6232 |
| 0.577         | 0.57  | 300  | 0.8065          | 0.6767   | 0.6821    | 0.6767 | 0.6761 |
| 0.4632        | 0.76  | 400  | 0.8437          | 0.63     | 0.6411    | 0.63   | 0.6321 |
| 0.3243        | 0.95  | 500  | 0.8983          | 0.6433   | 0.6614    | 0.6433 | 0.6461 |
| 0.2257        | 1.14  | 600  | 1.3704          | 0.6033   | 0.6863    | 0.6033 | 0.6046 |
| 0.1333        | 1.33  | 700  | 1.2951          | 0.6033   | 0.6201    | 0.6033 | 0.6052 |
| 0.0574        | 1.52  | 800  | 1.5119          | 0.6333   | 0.6331    | 0.6333 | 0.6309 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Tokenizers 0.15.0