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---
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
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