--- 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: 0.8369540630005199 - name: Accuracy type: accuracy value: 0.8514442916093535 --- # phrasebank-sentiment-analysis This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.5738 - F1: 0.8370 - Accuracy: 0.8514 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| | 0.6737 | 0.94 | 100 | 0.4361 | 0.7936 | 0.8287 | | 0.3148 | 1.89 | 200 | 0.3941 | 0.8269 | 0.8494 | | 0.1572 | 2.83 | 300 | 0.5384 | 0.8289 | 0.8494 | | 0.0615 | 3.77 | 400 | 0.5738 | 0.8370 | 0.8514 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1