---
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: 0.8650918975766398
    - name: Accuracy
      type: accuracy
      value: 0.874828060522696
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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: 0.5291
- F1: 0.8651
- Accuracy: 0.8748

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.6006        | 0.94  | 100  | 0.3973          | 0.8203 | 0.8418   |
| 0.2798        | 1.89  | 200  | 0.3888          | 0.8334 | 0.8508   |
| 0.1398        | 2.83  | 300  | 0.4821          | 0.8474 | 0.8480   |
| 0.0611        | 3.77  | 400  | 0.5291          | 0.8651 | 0.8748   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1