---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-finetuned-twitter_sentiment_analysis
  results: []
---

<!-- 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. -->

# bert-finetuned-twitter_sentiment_analysis

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4175
- F1: 0.7741
- Roc Auc: 0.8301
- Accuracy: 0.7639

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 197  | 0.3546          | 0.7613 | 0.8172  | 0.7210   |
| No log        | 2.0   | 394  | 0.3312          | 0.7622 | 0.8151  | 0.6924   |
| 0.3121        | 3.0   | 591  | 0.3511          | 0.7699 | 0.8244  | 0.7368   |
| 0.3121        | 4.0   | 788  | 0.4018          | 0.7833 | 0.8355  | 0.7654   |
| 0.3121        | 5.0   | 985  | 0.4175          | 0.7741 | 0.8301  | 0.7639   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3