Downloads · 30 days
28
16% of all-time downloads
nli-professional-status
selsar/nli-professional-status
nli-professional-status is a text classification model from selsar. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
170
Public
Parameters
609M
30.4 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors2.4 GB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- eurobert
- Access
- Public
- Created
- May 28, 2025
- Updated
- Jun 3, 2025
- SHA
- 1c7721d0
Try a prompt
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. -->
nli-professional-status
This model is a fine-tuned version of EuroBERT/EuroBERT-610m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5105
- Accuracy: 0.8906
- Precision Binary: 0.5169
- Recall Binary: 0.3770
- F1 Binary: 0.4360
- Precision Micro: 0.8906
- Recall Micro: 0.8906
- F1 Micro: 0.8906
- F1 Macro: 0.6877
- Pr Auc: 0.8671
- Cohen Kappa: 0.3771
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: 8
- eval_batch_size: 8
- 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
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Binary | Recall Binary | F1 Binary | Precision Micro | Recall Micro | F1 Micro | F1 Macro | Pr Auc | Cohen Kappa |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4706 | 1.0 | 544 | 0.3099 | 0.8925 | 0.5472 | 0.2377 | 0.3314 | 0.8925 | 0.8925 | 0.8925 | 0.6365 | 0.8528 | 0.2827 |
| 0.3256 | 2.0 | 1088 | 0.4521 | 0.8998 | 0.6275 | 0.2623 | 0.3699 | 0.8998 | 0.8998 | 0.8998 | 0.6578 | 0.8660 | 0.3253 |
| 0.2073 | 3.0 | 1632 | 0.5105 | 0.8906 | 0.5169 | 0.3770 | 0.4360 | 0.8906 | 0.8906 | 0.8906 | 0.6877 | 0.8671 | 0.3771 |
Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- eurobert
- License
- apache-2.0
- Created
- May 28, 2025
- Updated
- Jun 3, 2025