README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: test-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - name: Precision
            type: precision
            value: 0.9467731204258151
          - name: Recall
            type: recall
            value: 0.9579266240323123
          - name: F1
            type: f1
            value: 0.952317215994646
          - name: Accuracy
            type: accuracy
            value: 0.9920953233908337

test-ner

This model is a fine-tuned version of roberta-base on the conll2003 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0398
• Precision: 0.9468
• Recall: 0.9579
• F1: 0.9523
• Accuracy: 0.9921

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: 1
• eval_batch_size: 2
• seed: 42
• distributed_type: IPU
• total_train_batch_size: 16
• total_eval_batch_size: 10
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0
• training precision: Mixed Precision

Training results

Framework versions

• Transformers 4.20.0
• Pytorch 1.10.0+cpu
• Datasets 2.4.0
• Tokenizers 0.12.1