metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - udpos28
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: 1a5e2b8e
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: udpos28
          type: udpos28
          config: te
          split: validation
          args: te
        metrics:
          - name: Precision
            type: precision
            value: 0.894336015358501
          - name: Recall
            type: recall
            value: 0.8576779328683283
          - name: F1
            type: f1
            value: 0.8680916339670367
          - name: Accuracy
            type: accuracy
            value: 0.947129909365559

1a5e2b8e

This model is a fine-tuned version of xlm-roberta-base on the udpos28 dataset. It achieves the following results on the evaluation set:

Loss: 0.3219
Precision: 0.8943
Recall: 0.8577
F1: 0.8681
Accuracy: 0.9471

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: 5e-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
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0423	7.58	1000	0.3219	0.8943	0.8577	0.8681	0.9471

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0