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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model_creators: |
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- Jordan Painter, Diptesh Kanojia |
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widget: |
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- text: wow, i mean who would have thought |
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base_model: vinai/bertweet-base |
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model-index: |
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- name: bertweet-base-finetuned-SARC-combined-DS |
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results: [] |
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--- |
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# Utilising Weak Supervision to Create S3D: A Sarcasm Annotated Dataset |
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This is the repository for the S3D dataset published at EMNLP 2022. The dataset can help build sarcasm detection models. |
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# bertweet-base-finetuned-SARC-combined-DS |
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This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co./vinai/bertweet-base) on our combined sarcasm dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4624 |
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- Accuracy: 0.7611 |
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- Precision: 0.7611 |
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- Recall: 0.7611 |
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- F1: 0.7611 |
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## Model description |
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The given description for BERTweet by VinAI is as follows: <br> |
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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. |
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<br> |
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## Training and evaluation data |
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More information neededThis [vinai/bertweet-base](https://huggingface.co./vinai/bertweet-base) model was finetuned on our combined sarcasm dataset. This dataset was created to aid the building of sarcasm detection models |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 16 |
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- seed: 43 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.4319 | 4.0 | 44819 | 0.5049 | 0.7790 | 0.7796 | 0.7789 | 0.7789 | |
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| 0.2835 | 8.0 | 89638 | 0.6475 | 0.7663 | 0.7664 | 0.7663 | 0.7663 | |
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| 0.1797 | 12.0 | 134457 | 0.8746 | 0.7638 | 0.7639 | 0.7637 | 0.7637 | |
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| 0.1219 | 16.0 | 179276 | 1.0595 | 0.7585 | 0.7597 | 0.7587 | 0.7583 | |
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| 0.0905 | 20.0 | 224095 | 1.2115 | 0.7611 | 0.7612 | 0.7612 | 0.7611 | |
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| 0.0728 | 24.0 | 268914 | 1.3644 | 0.7628 | 0.7629 | 0.7627 | 0.7627 | |
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| 0.0612 | 28.0 | 313733 | 1.4624 | 0.7611 | 0.7611 | 0.7611 | 0.7611 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.10.1+cu111 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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