metadata

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

my_xlm-roberta-large-finetuned-conlljob04

This model is a fine-tuned version of 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