---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-tweeteval-distilbert
  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-tweeteval-distilbert

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3856
- Accuracy: 0.7701
- F1: 0.7191

## 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: 16
- eval_batch_size: 100
- seed: 15179996
- optimizer: Use OptimizerNames.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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6482        | 1.0   | 204  | 0.6140          | 0.7834   | 0.7183 |
| 0.4896        | 2.0   | 408  | 0.6577          | 0.7620   | 0.7002 |
| 0.3321        | 3.0   | 612  | 0.6471          | 0.7834   | 0.7244 |
| 0.1805        | 4.0   | 816  | 0.8309          | 0.7754   | 0.7145 |
| 0.0903        | 5.0   | 1020 | 0.9430          | 0.7647   | 0.7207 |
| 0.0523        | 6.0   | 1224 | 1.0135          | 0.7834   | 0.7260 |
| 0.0474        | 7.0   | 1428 | 1.1707          | 0.7567   | 0.7056 |
| 0.0514        | 8.0   | 1632 | 1.2040          | 0.7701   | 0.7232 |
| 0.0129        | 9.0   | 1836 | 1.3856          | 0.7701   | 0.7191 |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2