---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: EVALutionRelationTrain-5
  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. -->

# EVALutionRelationTrain-5

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6933
- Accuracy: 0.5
- Precision: 0.5
- Recall: 1.0
- F1: 0.6667

## 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        | 0.35  | 100  | 0.7119          | 0.5      | 0.0       | 0.0    | 0.0    |
| No log        | 0.71  | 200  | 0.7123          | 0.5      | 0.5       | 1.0    | 0.6667 |
| No log        | 1.06  | 300  | 0.6936          | 0.5      | 0.5       | 1.0    | 0.6667 |
| No log        | 1.42  | 400  | 0.6933          | 0.5      | 0.5       | 1.0    | 0.6667 |
| 0.6993        | 1.77  | 500  | 0.6945          | 0.5      | 0.0       | 0.0    | 0.0    |
| 0.6993        | 2.13  | 600  | 0.6948          | 0.5      | 0.0       | 0.0    | 0.0    |
| 0.6993        | 2.48  | 700  | 0.6999          | 0.5      | 0.5       | 1.0    | 0.6667 |
| 0.6993        | 2.84  | 800  | 0.6943          | 0.5      | 0.0       | 0.0    | 0.0    |
| 0.6993        | 3.19  | 900  | 0.6951          | 0.5      | 0.5       | 1.0    | 0.6667 |
| 0.698         | 3.55  | 1000 | 0.6945          | 0.5      | 0.0       | 0.0    | 0.0    |
| 0.698         | 3.9   | 1100 | 0.6956          | 0.5      | 0.0       | 0.0    | 0.0    |
| 0.698         | 4.26  | 1200 | 0.6933          | 0.5      | 0.5       | 1.0    | 0.6667 |
| 0.698         | 4.61  | 1300 | 0.6941          | 0.5      | 0.5       | 1.0    | 0.6667 |
| 0.698         | 4.96  | 1400 | 0.6934          | 0.5      | 0.0       | 0.0    | 0.0    |
| 0.6964        | 5.32  | 1500 | 0.6933          | 0.5      | 0.0       | 0.0    | 0.0    |
| 0.6964        | 5.67  | 1600 | 0.6943          | 0.5      | 0.0       | 0.0    | 0.0    |
| 0.6964        | 6.03  | 1700 | 0.6946          | 0.5      | 0.0       | 0.0    | 0.0    |
| 0.6964        | 6.38  | 1800 | 0.6932          | 0.5      | 0.0       | 0.0    | 0.0    |
| 0.6964        | 6.74  | 1900 | 0.6952          | 0.5      | 0.0       | 0.0    | 0.0    |
| 0.6952        | 7.09  | 2000 | 0.6934          | 0.5      | 0.5       | 1.0    | 0.6667 |
| 0.6952        | 7.45  | 2100 | 0.6935          | 0.5      | 0.5       | 1.0    | 0.6667 |
| 0.6952        | 7.8   | 2200 | 0.6933          | 0.5      | 0.5       | 1.0    | 0.6667 |


### Framework versions

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