metadata

tags:
  - generated_from_trainer
datasets:
  - financial_phrasebank
metrics:
  - accuracy
model-index:
  - name: sentiment_bert
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: financial_phrasebank
          type: financial_phrasebank
          config: sentences_66agree
          split: train
          args: sentences_66agree
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9360189573459715

sentiment_bert

This model is a fine-tuned version of SALT-NLP/FLANG-BERT on the financial_phrasebank dataset. It achieves the following results on the evaluation set:

Loss: 0.3754
Accuracy: 0.9360

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: 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: 6

Training results

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2