README.md

metadata

license: mit
base_model: romainlhardy/roberta-large-finetuned-ner
tags:
  - generated_from_trainer
datasets:
  - plod-cw
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: roberta-large-finetuned-ner-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: plod-cw
          type: plod-cw
          config: PLOD-CW
          split: validation
          args: PLOD-CW
        metrics:
          - name: Precision
            type: precision
            value: 0.9597188892697978
          - name: Recall
            type: recall
            value: 0.9502715546503734
          - name: F1
            type: f1
            value: 0.9549718574108819
          - name: Accuracy
            type: accuracy
            value: 0.949480642115203

roberta-large-finetuned-ner-finetuned-ner

This model is a fine-tuned version of romainlhardy/roberta-large-finetuned-ner on the plod-cw dataset. It achieves the following results on the evaluation set:

• Loss: 0.2327
• Precision: 0.9597
• Recall: 0.9503
• F1: 0.9550
• Accuracy: 0.9495

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: 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: 6

Training results

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2