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g-patentsberta-e2e
HamidBekam/g-patentsberta-e2e
g-patentsberta-e2e is a text classification model from HamidBekam. 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
- mpnet
- Access
- Public
- Created
- Feb 18, 2026
- Updated
- Feb 18, 2026
- SHA
- c74b90ed
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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. -->
g-patentsberta-e2e
This model is a fine-tuned version of microsoft/mpnet-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4002
- Accuracy: 0.8255
- Precision: 0.2789
- Recall: 0.8247
- F1: 0.4168
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: 16
- eval_batch_size: 64
- seed: 42
- 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: linear
- lr_scheduler_warmup_steps: 0.06
- num_epochs: 1.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.4534 | 0.1866 | 2000 | 0.4317 | 0.7988 | 0.7985 | 0.7899 | 0.7942 |
| 0.4212 | 0.3731 | 4000 | 0.4359 | 0.8043 | 0.8573 | 0.7218 | 0.7837 |
| 0.4095 | 0.5597 | 6000 | 0.4160 | 0.8157 | 0.8004 | 0.8325 | 0.8161 |
| 0.3992 | 0.7463 | 8000 | 0.4039 | 0.8210 | 0.8255 | 0.8061 | 0.8157 |
| 0.3828 | 0.9328 | 10000 | 0.3913 | 0.8241 | 0.8179 | 0.8260 | 0.8219 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.9.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- mpnet
- Created
- Feb 18, 2026
- Updated
- Feb 18, 2026