---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- conll2003job
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_xlm-roberta-large-finetuned-conlljob04
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003job
      type: conll2003job
      config: conll2003job
      split: validation
      args: conll2003job
    metrics:
    - name: Precision
      type: precision
      value: 0.961673640167364
    - name: Recall
      type: recall
      value: 0.9670144732413329
    - name: F1
      type: f1
      value: 0.964336661911555
    - name: Accuracy
      type: accuracy
      value: 0.9935750165491998
---

<!-- 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. -->

# my_xlm-roberta-large-finetuned-conlljob04

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the conll2003job dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0420
- Precision: 0.9617
- Recall: 0.9670
- F1: 0.9643
- Accuracy: 0.9936

## 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: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1566        | 1.0   | 896  | 0.0403          | 0.9425    | 0.9542 | 0.9483 | 0.9911   |
| 0.0319        | 2.0   | 1792 | 0.0359          | 0.9523    | 0.9571 | 0.9547 | 0.9922   |
| 0.0156        | 3.0   | 2688 | 0.0356          | 0.9594    | 0.9625 | 0.9609 | 0.9929   |
| 0.01          | 4.0   | 3584 | 0.0377          | 0.9604    | 0.9672 | 0.9638 | 0.9934   |
| 0.0058        | 5.0   | 4480 | 0.0398          | 0.9618    | 0.9662 | 0.9640 | 0.9934   |
| 0.0034        | 6.0   | 5376 | 0.0420          | 0.9617    | 0.9670 | 0.9643 | 0.9936   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1