README.md

metadata

license: mit
base_model: xlm-roberta-large
tags:
  - generated_from_trainer
datasets:
  - uner_all
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: uner_all
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: uner_all
          type: uner_all
          config: default
          split: None
        metrics:
          - name: Precision
            type: precision
            value: 0.8566170026292725
          - name: Recall
            type: recall
            value: 0.8522846180676665
          - name: F1
            type: f1
            value: 0.8544453186467348
          - name: Accuracy
            type: accuracy
            value: 0.9842612991521463

uner_all

This model is a fine-tuned version of xlm-roberta-large on the uner_all dataset. The uner_all dataset combines all training datasets in UNER. It achieves the following results on the evaluation set:

• Loss: 0.1180
• Precision: 0.8566
• Recall: 0.8523
• F1: 0.8544
• Accuracy: 0.9843

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: 3e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5.0

Training results

Framework versions

• Transformers 4.31.0
• Pytorch 1.10.1+cu113
• Datasets 2.14.4
• Tokenizers 0.13.3