---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_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. -->

# distilbert_base_uncased_twitter

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4864
- Accuracy: 0.7665
- F1 Macro: 0.7130
- F1 Micro: 0.7665

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 0.4671        | 0.37  | 50   | 0.4990          | 0.7665   | 0.7214   | 0.7665   |
| 0.4724        | 0.74  | 100  | 0.4864          | 0.7665   | 0.7130   | 0.7665   |
| 0.4569        | 1.1   | 150  | 0.4924          | 0.7619   | 0.7171   | 0.7619   |
| 0.4577        | 1.47  | 200  | 0.4881          | 0.7675   | 0.7357   | 0.7675   |
| 0.4438        | 1.84  | 250  | 0.4902          | 0.7638   | 0.7222   | 0.7638   |
| 0.405         | 2.21  | 300  | 0.4901          | 0.7647   | 0.7211   | 0.7647   |
| 0.4308        | 2.57  | 350  | 0.4900          | 0.7693   | 0.7326   | 0.7693   |
| 0.3584        | 2.94  | 400  | 0.4931          | 0.7675   | 0.7288   | 0.7675   |


### Framework versions

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