---
base_model: cardiffnlp/twitter-roberta-base-irony
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Twroberta-baseB_10epoch
  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. -->

# Twroberta-baseB_10epoch

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-irony](https://huggingface.co/cardiffnlp/twitter-roberta-base-irony) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1763
- Accuracy: 0.7771
- Precision: 0.2366
- Recall: 0.3137
- F1: 0.2679

## 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 | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 217  | 0.1251          | 0.8571   | 0.0       | 0.0    | 0.0    |
| No log        | 2.0   | 434  | 0.1213          | 0.8571   | 0.0       | 0.0    | 0.0    |
| 0.1617        | 3.0   | 651  | 0.1226          | 0.8157   | 0.2655    | 0.3026 | 0.2828 |
| 0.1617        | 4.0   | 868  | 0.1423          | 0.7671   | 0.1991    | 0.2989 | 0.2389 |
| 0.0899        | 5.0   | 1085 | 0.1594          | 0.7364   | 0.2142    | 0.3727 | 0.2695 |
| 0.0899        | 6.0   | 1302 | 0.1560          | 0.8086   | 0.2567    | 0.2214 | 0.2320 |
| 0.0411        | 7.0   | 1519 | 0.1963          | 0.715    | 0.1945    | 0.3875 | 0.2584 |
| 0.0411        | 8.0   | 1736 | 0.1687          | 0.7914   | 0.2520    | 0.2804 | 0.2601 |
| 0.0411        | 9.0   | 1953 | 0.1726          | 0.7843   | 0.2419    | 0.2989 | 0.2646 |
| 0.0197        | 10.0  | 2170 | 0.1763          | 0.7771   | 0.2366    | 0.3137 | 0.2679 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1