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roberta-large-unlabeled-gab-semeval2023-task10-9000sample
HPL/roberta-large-unlabeled-gab-semeval2023-task10-9000sample
roberta-large-unlabeled-gab-semeval2023-task10-9000sample is a fill-mask model from HPL. Use it when you need the model to fill a missing word. 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
- Fill-Mask
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Nov 12, 2022
- Updated
- Nov 12, 2022
- SHA
- 1800252d
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-large-unlabeled-gab-semeval2023-task10-9000sample
This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.0541
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.515 | 1.0 | 563 | 2.3288 |
| 2.2807 | 2.0 | 1126 | 2.1769 |
| 2.0351 | 3.0 | 1689 | 2.0541 |
Framework versions
- Transformers 4.13.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.10.3
- Task
- Fill-Mask
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Nov 12, 2022
- Updated
- Nov 12, 2022