--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - financial_phrasebank metrics: - f1 - accuracy model-index: - name: finetuning-llms-project-2 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: 0.8330949180475766 - name: Accuracy type: accuracy value: 0.8493810178817056 --- # finetuning-llms-project-2 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: 0.5427 - F1: 0.8331 - Accuracy: 0.8494 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| | 0.6137 | 0.94 | 100 | 0.5180 | 0.7614 | 0.8061 | | 0.297 | 1.89 | 200 | 0.4018 | 0.8201 | 0.8425 | | 0.1648 | 2.83 | 300 | 0.4641 | 0.8327 | 0.8521 | | 0.0736 | 3.77 | 400 | 0.5427 | 0.8331 | 0.8494 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0 - Datasets 2.14.5 - Tokenizers 0.14.1