---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_base_uncased_twitter
  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_base_uncased_twitter

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4780
- Accuracy: 0.7767
- F1 Macro: 0.7415
- F1 Micro: 0.7767

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 0.4689        | 0.37  | 50   | 0.4876          | 0.7583   | 0.7185   | 0.7583   |
| 0.4675        | 0.74  | 100  | 0.4780          | 0.7767   | 0.7415   | 0.7767   |
| 0.4489        | 1.1   | 150  | 0.4803          | 0.7776   | 0.7440   | 0.7776   |
| 0.457         | 1.47  | 200  | 0.4820          | 0.7757   | 0.7482   | 0.7757   |
| 0.44          | 1.84  | 250  | 0.4857          | 0.7831   | 0.7429   | 0.7831   |
| 0.3905        | 2.21  | 300  | 0.4835          | 0.7739   | 0.7406   | 0.7739   |
| 0.4276        | 2.57  | 350  | 0.4898          | 0.7711   | 0.7452   | 0.7711   |
| 0.3413        | 2.94  | 400  | 0.4929          | 0.7757   | 0.7468   | 0.7757   |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2