---
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-conlljob03
  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: 0.9592654424040067
    - name: Recall
      type: recall
      value: 0.9670144732413329
    - name: F1
      type: f1
      value: 0.9631243714381496
    - name: Accuracy
      type: accuracy
      value: 0.9933024414937113
---

<!-- 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-conlljob03

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: 0.0364
- Precision: 0.9593
- Recall: 0.9670
- F1: 0.9631
- Accuracy: 0.9933

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1596        | 1.0   | 896  | 0.0385          | 0.9393    | 0.9556 | 0.9474 | 0.9915   |
| 0.0298        | 2.0   | 1792 | 0.0377          | 0.9532    | 0.9594 | 0.9563 | 0.9920   |
| 0.0158        | 3.0   | 2688 | 0.0339          | 0.9579    | 0.9658 | 0.9619 | 0.9931   |
| 0.0087        | 4.0   | 3584 | 0.0364          | 0.9593    | 0.9670 | 0.9631 | 0.9933   |


### Framework versions

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