---
base_model: vinai/bertweet-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bertweet-base_3epoch3
  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. -->

# bertweet-base_3epoch3

This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0492
- Accuracy: 0.7450
- F1: 0.4080
- Precision: 0.61
- Recall: 0.3065
- Precision Sarcastic: 0.61
- Recall Sarcastic: 0.3065
- F1 Sarcastic: 0.4080

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Precision Sarcastic | Recall Sarcastic | F1 Sarcastic |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------------:|:----------------:|:------------:|
| No log        | 1.0   | 347  | 1.9148          | 0.7205   | 0.4294 | 0.5177    | 0.3668 | 0.5177              | 0.3668           | 0.4294       |
| 0.0342        | 2.0   | 694  | 2.0035          | 0.7450   | 0.4587 | 0.5859    | 0.3769 | 0.5859              | 0.3769           | 0.4587       |
| 0.0213        | 3.0   | 1041 | 2.0492          | 0.7450   | 0.4080 | 0.61      | 0.3065 | 0.61                | 0.3065           | 0.4080       |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1