metadata

library_name: transformers
base_model: lilt-xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - token-classification
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: finetuned-v-1
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: funsd
          type: token-classification
        metrics:
          - name: Precision
            type: precision
            value: 0.5977671451355662
          - name: Recall
            type: recall
            value: 0.5977671451355662
          - name: F1
            type: f1
            value: 0.5977671451355662
          - name: Accuracy
            type: accuracy
            value: 0.5977671451355662

finetuned-v-1

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

Loss: 1.1752
Precision: 0.5978
Recall: 0.5978
F1: 0.5978
Accuracy: 0.5978

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: 8
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.3986	0.5319	100	1.1752	0.5978	0.5978	0.5978	0.5978

Framework versions

Transformers 4.45.2
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.1