metadata

license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - financial_phrasebank
metrics:
  - f1
  - accuracy
model-index:
  - name: phrasebank-sentiment-analysis
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: financial_phrasebank
          type: financial_phrasebank
          config: sentences_50agree
          split: train
          args: sentences_50agree
        metrics:
          - name: F1
            type: f1
            value:
              f1: 0.8172545518133599
          - name: Accuracy
            type: accuracy
            value:
              accuracy: 0.8328748280605227

phrasebank-sentiment-analysis

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

Loss: 0.7236
F1: {'f1': 0.8172545518133599}
Accuracy: {'accuracy': 0.8328748280605227}

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: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy
0.3941	0.94	100	0.4098	{'f1': 0.7962755487135231}	{'accuracy': 0.828060522696011}
0.1921	1.89	200	0.5360	{'f1': 0.8094154455783058}	{'accuracy': 0.8321870701513068}
0.0873	2.83	300	0.7086	{'f1': 0.8146311198809535}	{'accuracy': 0.8301237964236589}
0.0404	3.77	400	0.7236	{'f1': 0.8172545518133599}	{'accuracy': 0.8328748280605227}

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3