---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-WikiNeural
  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. -->

# roberta-base-finetuned-WikiNeural

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0871
- Loc: {'precision': 0.9276567437219359, 'recall': 0.9366918555835433, 'f1': 0.9321524064171123, 'number': 5955}
- Misc: {'precision': 0.8334231805929919, 'recall': 0.916419679905157, 'f1': 0.872953133822699, 'number': 5061}
- Org: {'precision': 0.9296179258833669, 'recall': 0.9382429689765149, 'f1': 0.9339105339105339, 'number': 3449}
- Per: {'precision': 0.9688723570869224, 'recall': 0.9499040307101727, 'f1': 0.9592944369063772, 'number': 5210}
- Overall Precision: 0.9124
- Overall Recall: 0.9352
- Overall F1: 0.9237
- Overall Accuracy: 0.9910

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Loc                                                                                                       | Misc                                                                                                      | Org                                                                                                       | Per                                                                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.1086        | 1.0   | 5795  | 0.1001          | {'precision': 0.9148971193415638, 'recall': 0.9333333333333333, 'f1': 0.9240232751454697, 'number': 5955} | {'precision': 0.8157800785433774, 'recall': 0.9029836000790358, 'f1': 0.8571696520678983, 'number': 5061} | {'precision': 0.9133903133903134, 'recall': 0.9295447955929255, 'f1': 0.9213967524069551, 'number': 3449} | {'precision': 0.9642018779342723, 'recall': 0.9460652591170825, 'f1': 0.9550474714202672, 'number': 5210} | 0.8997            | 0.9282         | 0.9137     | 0.9896           |
| 0.0727        | 2.0   | 11590 | 0.0871          | {'precision': 0.9276567437219359, 'recall': 0.9366918555835433, 'f1': 0.9321524064171123, 'number': 5955} | {'precision': 0.8334231805929919, 'recall': 0.916419679905157, 'f1': 0.872953133822699, 'number': 5061}   | {'precision': 0.9296179258833669, 'recall': 0.9382429689765149, 'f1': 0.9339105339105339, 'number': 3449} | {'precision': 0.9688723570869224, 'recall': 0.9499040307101727, 'f1': 0.9592944369063772, 'number': 5210} | 0.9124            | 0.9352         | 0.9237     | 0.9910           |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.13.0
- Tokenizers 0.13.3