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hybrid_lexical_hosting_domain_cat_meta
phunganhsang/hybrid_lexical_hosting_domain_cat_meta
hybrid_lexical_hosting_domain_cat_meta is a machine learning model from phunganhsang. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as agpl-3.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- agpl-3.0
- Model type
- roberta
- Access
- Public
- Created
- Oct 23, 2025
- Updated
- Oct 31, 2025
- SHA
- 84294698
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. -->
hybrid_lexical_hosting_domain_cat_meta
This model is a fine-tuned version of gechim/metadata-cls_15_10 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1718
- Accuracy: 0.8781
- F1: 0.8779
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2445
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.2759 | 1.0 | 978 | 0.2220 | 0.8282 | 0.8276 |
| 0.2018 | 2.0 | 1956 | 0.1849 | 0.8708 | 0.8706 |
| 0.1773 | 3.0 | 2934 | 0.1718 | 0.8781 | 0.8779 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.1.1
- Tokenizers 0.22.1
- Library
- transformers
- Type
- roberta
- License
- agpl-3.0
- Created
- Oct 23, 2025
- Updated
- Oct 31, 2025