---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: tweet_sentiment_analysis_clean_tweet
  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. -->

# tweet_sentiment_analysis_clean_tweet

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: 0.4886
- Accuracy: 0.8081
- F1: 0.8073
- Recall: 0.8037
- Precision: 0.8110

## 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: 64
- eval_batch_size: 64
- seed: 42
- 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.4077        | 1.0   | 20000 | 0.4019          | 0.8153   | 0.8177 | 0.8281 | 0.8076    |
| 0.3382        | 2.0   | 40000 | 0.4175          | 0.8148   | 0.8133 | 0.8063 | 0.8204    |
| 0.2948        | 3.0   | 60000 | 0.4528          | 0.8107   | 0.8129 | 0.8220 | 0.8041    |
| 0.2541        | 4.0   | 80000 | 0.4886          | 0.8081   | 0.8073 | 0.8037 | 0.8110    |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0