---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model_creators:
- Jordan Painter, Diptesh Kanojia
widget:
- text: I'm ecstatic my flight was just delayed
base_model: vinai/bertweet-base
model-index:
- name: bertweet-base-finetuned-SARC-DS
  results: []
---
# Utilising Weak Supervision to Create S3D: A Sarcasm Annotated Dataset
This is the repository for the S3D dataset published at EMNLP 2022. The dataset can help build sarcasm detection models.

# bertweet-base-finetuned-SARC-DS

This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the [SARC](https://metatext.io/datasets/self-annotated-reddit-corpus-(sarc)) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7094
- Accuracy: 0.7636
- Precision: 0.7637
- Recall: 0.7636
- F1: 0.7636

## Model description

The given description for BERTweet by VinAI is as follows: <br>
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the RoBERTa pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Tweets (16B word tokens ~ 80GB), containing 845M Tweets streamed from 01/2012 to 08/2019 and 5M Tweets related to the COVID-19 pandemic.
<br>

## Training and evaluation data

This [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) model was finetuned on the [SARC](https://metatext.io/datasets/self-annotated-reddit-corpus-(sarc)) dataset. The dataset is intended to help build sarcasm detection models.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4978        | 1.0   | 44221  | 0.4899          | 0.7777   | 0.7787    | 0.7778 | 0.7775 |
| 0.4413        | 2.0   | 88442  | 0.4833          | 0.7798   | 0.7803    | 0.7798 | 0.7797 |
| 0.3943        | 3.0   | 132663 | 0.5387          | 0.7784   | 0.7784    | 0.7784 | 0.7784 |
| 0.3461        | 4.01  | 176884 | 0.6184          | 0.7690   | 0.7701    | 0.7690 | 0.7688 |
| 0.3024        | 5.01  | 221105 | 0.6899          | 0.7684   | 0.7691    | 0.7684 | 0.7682 |
| 0.2653        | 6.01  | 265326 | 0.7805          | 0.7654   | 0.7660    | 0.7654 | 0.7653 |
| 0.2368        | 7.01  | 309547 | 0.9066          | 0.7643   | 0.7648    | 0.7643 | 0.7642 |
| 0.2166        | 8.01  | 353768 | 1.0548          | 0.7612   | 0.7620    | 0.7611 | 0.7610 |
| 0.2005        | 9.01  | 397989 | 1.0649          | 0.7639   | 0.7639    | 0.7639 | 0.7639 |
| 0.1837        | 10.02 | 442210 | 1.1805          | 0.7621   | 0.7624    | 0.7621 | 0.7621 |
| 0.1667        | 11.02 | 486431 | 1.3017          | 0.7658   | 0.7659    | 0.7659 | 0.7658 |
| 0.1531        | 12.02 | 530652 | 1.2947          | 0.7627   | 0.7628    | 0.7627 | 0.7627 |
| 0.1377        | 13.02 | 574873 | 1.3877          | 0.7639   | 0.7639    | 0.7639 | 0.7639 |
| 0.1249        | 14.02 | 619094 | 1.4468          | 0.7613   | 0.7616    | 0.7613 | 0.7612 |
| 0.1129        | 15.02 | 663315 | 1.4951          | 0.7620   | 0.7621    | 0.7620 | 0.7620 |
| 0.103         | 16.02 | 707536 | 1.5599          | 0.7619   | 0.7624    | 0.7619 | 0.7618 |
| 0.0937        | 17.03 | 751757 | 1.6270          | 0.7615   | 0.7616    | 0.7615 | 0.7615 |
| 0.0864        | 18.03 | 795978 | 1.6918          | 0.7622   | 0.7624    | 0.7622 | 0.7621 |
| 0.0796        | 19.03 | 840199 | 1.7094          | 0.7636   | 0.7637    | 0.7636 | 0.7636 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1