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distilroberta-offensive
valurank/distilroberta-offensive
distilroberta-offensive is a text classification model from valurank. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as other.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- other
- Model type
- roberta
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Jun 8, 2022
- SHA
- 1ece43c9
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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. -->
distilroberta-offensive
This model is a fine-tuned version of distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4526
- Acc: 0.8975
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: 32
- eval_batch_size: 32
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Acc |
|---|---|---|---|---|
| 0.2321 | 1.0 | 1030 | 0.2404 | 0.9044 |
| 0.2539 | 2.0 | 2060 | 0.2139 | 0.9098 |
| 0.1997 | 3.0 | 3090 | 0.2561 | 0.9090 |
| 0.1663 | 4.0 | 4120 | 0.2409 | 0.9030 |
| 0.1515 | 5.0 | 5150 | 0.3000 | 0.9055 |
| 0.1035 | 6.0 | 6180 | 0.4170 | 0.9027 |
| 0.0466 | 7.0 | 7210 | 0.4526 | 0.8975 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- other
- Created
- Mar 2, 2022
- Updated
- Jun 8, 2022