---
library_name: transformers
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: euk_roberta_large_essentiality_Network
  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. -->

# euk_roberta_large_essentiality_Network

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4307
- Accuracy: 0.8210
- Precision: 0.7886
- Recall: 0.8771
- F1: 0.8305

## 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: 1e-05
- train_batch_size: 60
- eval_batch_size: 60
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 240
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 47   | 0.5793          | 0.7023   | 0.7021    | 0.7031 | 0.7026 |
| No log        | 2.0   | 94   | 0.4761          | 0.7812   | 0.7861    | 0.7727 | 0.7794 |
| No log        | 3.0   | 141  | 0.4792          | 0.7769   | 0.7506    | 0.8295 | 0.7881 |
| No log        | 4.0   | 188  | 0.4617          | 0.7822   | 0.7641    | 0.8168 | 0.7896 |
| No log        | 5.0   | 235  | 0.4748          | 0.7769   | 0.7393    | 0.8558 | 0.7933 |
| No log        | 6.0   | 282  | 0.4401          | 0.7961   | 0.7773    | 0.8303 | 0.8029 |
| No log        | 7.0   | 329  | 0.4273          | 0.7968   | 0.7828    | 0.8217 | 0.8018 |
| No log        | 8.0   | 376  | 0.4282          | 0.8099   | 0.7825    | 0.8587 | 0.8188 |
| No log        | 9.0   | 423  | 0.4242          | 0.8099   | 0.8       | 0.8267 | 0.8131 |
| No log        | 10.0  | 470  | 0.4248          | 0.8089   | 0.7908    | 0.8402 | 0.8147 |
| 1.8645        | 11.0  | 517  | 0.4183          | 0.8139   | 0.8095    | 0.8210 | 0.8152 |
| 1.8645        | 12.0  | 564  | 0.4206          | 0.8195   | 0.7988    | 0.8544 | 0.8257 |
| 1.8645        | 13.0  | 611  | 0.4225          | 0.8178   | 0.7985    | 0.8501 | 0.8235 |
| 1.8645        | 14.0  | 658  | 0.4307          | 0.8210   | 0.7886    | 0.8771 | 0.8305 |
| 1.8645        | 15.0  | 705  | 0.4259          | 0.8163   | 0.8016    | 0.8409 | 0.8208 |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.9.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2