bertweet-base-finetuned-SARC-DS

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 on the 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 model was finetuned on the 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

Framework versions

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