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roberta-scarcasm-discriminator
XSY/roberta-scarcasm-discriminator
roberta-scarcasm-discriminator is a text classification model from XSY. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Nov 10, 2021
- SHA
- 99842c76
Try a prompt
Model card
From the Hugging Face model README
<!-- 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. -->
roberta-scarcasm-discriminator
roberta-base
label0: unsarcasitic
label1: sarcastic The fine tune method in my github https://github.com/yangyangxusheng/Fine-tune-use-transformers
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1844
- Accuracy: 0.9698
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
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.144 | 1.0 | 2179 | 0.2522 | 0.9215 |
| 0.116 | 2.0 | 4358 | 0.2105 | 0.9530 |
| 0.0689 | 3.0 | 6537 | 0.2015 | 0.9610 |
| 0.028 | 4.0 | 8716 | 0.1844 | 0.9698 |
Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Mar 2, 2022
- Updated
- Nov 10, 2021