---
base_model: vinai/bertweet-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bertweet-base_3epoch10.2
  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_3epoch10.2

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: 1.8545
- Accuracy: 0.7493
- F1: 0.4663
- Precision: 0.5984
- Recall: 0.3819
- Precision Sarcastic: 0.5984
- Recall Sarcastic: 0.3819
- F1 Sarcastic: 0.4663

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Precision Sarcastic | Recall Sarcastic | F1 Sarcastic |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------------:|:----------------:|:------------:|
| No log        | 1.0   | 174  | 1.8493          | 0.7464   | 0.4323 | 0.6036    | 0.3367 | 0.6036              | 0.3367           | 0.4323       |
| No log        | 2.0   | 348  | 1.5481          | 0.7522   | 0.5301 | 0.5808    | 0.4874 | 0.5808              | 0.4874           | 0.5301       |
| 0.0477        | 3.0   | 522  | 1.6249          | 0.7565   | 0.4531 | 0.6364    | 0.3518 | 0.6364              | 0.3518           | 0.4531       |
| 0.0477        | 4.0   | 696  | 1.6593          | 0.7464   | 0.4793 | 0.5827    | 0.4070 | 0.5827              | 0.4070           | 0.4793       |
| 0.0477        | 5.0   | 870  | 1.7213          | 0.7493   | 0.42   | 0.6238    | 0.3166 | 0.6238              | 0.3166           | 0.42         |
| 0.0277        | 6.0   | 1044 | 1.7249          | 0.7450   | 0.4381 | 0.5948    | 0.3467 | 0.5948              | 0.3467           | 0.4381       |
| 0.0277        | 7.0   | 1218 | 1.8038          | 0.7450   | 0.4486 | 0.5902    | 0.3618 | 0.5902              | 0.3618           | 0.4486       |
| 0.0277        | 8.0   | 1392 | 1.8409          | 0.7493   | 0.4387 | 0.6126    | 0.3417 | 0.6126              | 0.3417           | 0.4387       |
| 0.0105        | 9.0   | 1566 | 1.8427          | 0.7522   | 0.4487 | 0.6195    | 0.3518 | 0.6195              | 0.3518           | 0.4487       |
| 0.0105        | 10.0  | 1740 | 1.8545          | 0.7493   | 0.4663 | 0.5984    | 0.3819 | 0.5984              | 0.3819           | 0.4663       |


### Framework versions

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