---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- id_nergrit_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-ner-silvanus
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: id_nergrit_corpus
      type: id_nergrit_corpus
      config: ner
      split: validation
      args: ner
    metrics:
    - name: Precision
      type: precision
      value: 0.918622848200313
    - name: Recall
      type: recall
      value: 0.9280632411067193
    - name: F1
      type: f1
      value: 0.9233189146677152
    - name: Accuracy
      type: accuracy
      value: 0.9850887866850908
---

<!-- 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. -->

# xlm-roberta-base-ner-silvanus

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the id_nergrit_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0635
- Precision: 0.9186
- Recall: 0.9281
- F1: 0.9233
- Accuracy: 0.9851

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1607        | 1.0   | 827  | 0.0519          | 0.9094    | 0.9249 | 0.9171 | 0.9855   |
| 0.0464        | 2.0   | 1654 | 0.0545          | 0.9137    | 0.9289 | 0.9212 | 0.9849   |
| 0.0322        | 3.0   | 2481 | 0.0635          | 0.9186    | 0.9281 | 0.9233 | 0.9851   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1