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robbert-L-dbrd-imdb-insurance
sytnaxerror/robbert-L-dbrd-imdb-insurance
robbert-L-dbrd-imdb-insurance is a text classification model from sytnaxerror. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Apr 16, 2026
- Updated
- Apr 16, 2026
- SHA
- 89d81d6c
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Base models
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. -->
robbert-L-dbrd-imdb-insurance
This model is a fine-tuned version of sytnaxerror/robbert-large-with-dbrd-plus-imdb-mac on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9573
- Accuracy: 0.7838
- F1: 0.7785
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: 2e-05
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.06
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 20 | 0.9799 | 0.7711 | 0.7599 |
| No log | 2.0 | 40 | 0.9582 | 0.7838 | 0.7785 |
| 3.8634 | 3.0 | 60 | 0.9573 | 0.7838 | 0.7785 |
Framework versions
- Transformers 5.5.4
- Pytorch 2.11.0+cu130
- Datasets 4.8.4
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Apr 16, 2026
- Updated
- Apr 16, 2026