---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-ner-silvanus
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: id
      split: validation
      args: id
    metrics:
    - name: Precision
      type: precision
      value: 0.9574581228396704
    - name: Recall
      type: recall
      value: 0.9664519592055824
    - name: F1
      type: f1
      value: 0.9619340189662082
    - name: Accuracy
      type: accuracy
      value: 0.9889216263995286
---

<!-- 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-large-ner-silvanus

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co./xlm-roberta-large) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0495
- Precision: 0.9575
- Recall: 0.9665
- F1: 0.9619
- Accuracy: 0.9889

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 427  | 0.0560          | 0.9339    | 0.9514 | 0.9426 | 0.9828   |
| 0.1405        | 2.0   | 855  | 0.0539          | 0.9430    | 0.9595 | 0.9512 | 0.9859   |
| 0.0449        | 3.0   | 1281 | 0.0495          | 0.9575    | 0.9665 | 0.9619 | 0.9889   |


### Framework versions

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