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metadata
				license: mit
tags:
  - generated_from_trainer
datasets:
  - financial_phrasebank
metrics:
  - accuracy
  - f1
model-index:
  - name: roberta-large-financial-phrasebank-allagree1
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: financial_phrasebank
          type: financial_phrasebank
          config: sentences_allagree
          split: train
          args: sentences_allagree
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9734513274336283
          - name: F1
            type: f1
            value: 0.9736033872259027

			

roberta-large-financial-phrasebank-allagree1

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

  • Loss: 0.1417
  • Accuracy: 0.9735
  • F1: 0.9736

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: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.503 1.0 227 0.2774 0.9513 0.9517
0.177 2.0 454 0.1518 0.9779 0.9778
0.0789 3.0 681 0.1364 0.9823 0.9822
0.0512 4.0 908 0.1131 0.9779 0.9778
0.03 5.0 1135 0.1417 0.9735 0.9736

Framework versions

  • Transformers 4.21.1
  • Pytorch 1.12.0+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1