---
license: mit
base_model: camembert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: VogagenRelation
  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. -->

# VogagenRelation

This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4869
- Accuracy: 0.9016
- Precision: 0.8671
- Recall: 0.9484
- F1: 0.9060

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.21  | 100  | 0.6272          | 0.6729   | 0.6415    | 0.7828 | 0.7051 |
| No log        | 0.42  | 200  | 0.4933          | 0.7799   | 0.7406    | 0.8609 | 0.7962 |
| No log        | 0.62  | 300  | 0.4114          | 0.8431   | 0.8087    | 0.8984 | 0.8512 |
| No log        | 0.83  | 400  | 0.4483          | 0.8384   | 0.8054    | 0.8922 | 0.8466 |
| 0.5445        | 1.04  | 500  | 0.4149          | 0.8525   | 0.7971    | 0.9453 | 0.8649 |
| 0.5445        | 1.25  | 600  | 0.4221          | 0.8532   | 0.8038    | 0.9344 | 0.8642 |
| 0.5445        | 1.46  | 700  | 0.4022          | 0.8712   | 0.8728    | 0.8688 | 0.8708 |
| 0.5445        | 1.66  | 800  | 0.4083          | 0.8509   | 0.8013    | 0.9328 | 0.8621 |
| 0.5445        | 1.87  | 900  | 0.4272          | 0.8704   | 0.8455    | 0.9062 | 0.8748 |
| 0.3857        | 2.08  | 1000 | 0.3800          | 0.8759   | 0.8501    | 0.9125 | 0.8802 |
| 0.3857        | 2.29  | 1100 | 0.4684          | 0.8673   | 0.8357    | 0.9141 | 0.8731 |
| 0.3857        | 2.49  | 1200 | 0.4754          | 0.8634   | 0.8207    | 0.9297 | 0.8718 |
| 0.3857        | 2.7   | 1300 | 0.4392          | 0.8681   | 0.8294    | 0.9266 | 0.8753 |
| 0.3857        | 2.91  | 1400 | 0.5272          | 0.8470   | 0.7803    | 0.9656 | 0.8631 |
| 0.2687        | 3.12  | 1500 | 0.3529          | 0.9016   | 0.8693    | 0.9453 | 0.9057 |
| 0.2687        | 3.33  | 1600 | 0.3857          | 0.8899   | 0.8499    | 0.9469 | 0.8958 |
| 0.2687        | 3.53  | 1700 | 0.3852          | 0.9016   | 0.8836    | 0.925  | 0.9038 |
| 0.2687        | 3.74  | 1800 | 0.4860          | 0.8829   | 0.8365    | 0.9516 | 0.8904 |
| 0.2687        | 3.95  | 1900 | 0.4014          | 0.9001   | 0.8657    | 0.9469 | 0.9045 |
| 0.1785        | 4.16  | 2000 | 0.4295          | 0.8993   | 0.8655    | 0.9453 | 0.9037 |
| 0.1785        | 4.37  | 2100 | 0.4592          | 0.8977   | 0.8550    | 0.9578 | 0.9035 |
| 0.1785        | 4.57  | 2200 | 0.4392          | 0.9055   | 0.8844    | 0.9328 | 0.9080 |
| 0.1785        | 4.78  | 2300 | 0.4659          | 0.9024   | 0.8759    | 0.9375 | 0.9057 |
| 0.1785        | 4.99  | 2400 | 0.4059          | 0.9110   | 0.9021    | 0.9219 | 0.9119 |
| 0.1098        | 5.2   | 2500 | 0.4869          | 0.9016   | 0.8671    | 0.9484 | 0.9060 |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1